Opportunities for using spatial property assessment data in air pollution exposure assessments
© Setton et al; licensee BioMed Central Ltd. 2005
Received: 14 September 2005
Accepted: 31 October 2005
Published: 31 October 2005
Many epidemiological studies examining the relationships between adverse health outcomes and exposure to air pollutants use ambient air pollution measurements as a proxy for personal exposure levels. When pollution levels vary at neighbourhood levels, using ambient pollution data from sparsely located fixed monitors may inadequately capture the spatial variation in ambient pollution. A major constraint to moving toward exposure assessments and epidemiological studies of air pollution at a neighbourhood level is the lack of readily available data at appropriate spatial resolutions. Spatial property assessment data are widely available in North America and may provide an opportunity for developing neighbourhood level air pollution exposure assessments.
This paper provides a detailed description of spatial property assessment data available in the Pacific Northwest of Canada and the United States, and provides examples of potential applications of spatial property assessment data for improving air pollution exposure assessment at the neighbourhood scale, including: (1) creating variables for use in land use regression modelling of neighbourhood levels of ambient air pollution; (2) enhancing wood smoke exposure estimates by mapping fireplace locations; and (3) using data available on individual building characteristics to produce a regional air pollution infiltration model.
Spatial property assessment data are an extremely detailed data source at a fine spatial resolution, and therefore a source of information that could improve the quality and spatial resolution of current air pollution exposure assessments.
Many epidemiological studies examining the relationships between adverse health outcomes and exposure to air pollutants use ambient air pollution measurements as a proxy for personal exposure levels . Because the number of fixed outdoor monitoring sites within a city usually is limited, ambient pollution measurements often are extrapolated to areas between monitors, thus disregarding any neighbourhood-scale spatial variation in pollution levels. Recent research suggests that some neighbourhoods within a city can be disproportionately exposed to air pollution and that these differences may influence health outcomes . A major constraint to moving toward exposure assessments and epidemiological studies of air pollution at a neighbourhood level is the lack of readily available data at appropriate spatial resolutions.
Spatial property assessment data (SPAD) were identified as a potential data source for exposure research through an ongoing study, funded by Health Canada via the British Columbia Centre for Disease Control, examining the effects of air pollution on birth outcomes and subsequent development of health outcomes associated with exposure to air pollution for a birth cohort of 90,000. The study area encompasses the Georgia Basin Puget Sound airshed, located in the Pacific Northwest of the United States and Canada and encompassing approximately 10 million hectares of land and marine environments. SPAD (considered here to be made up of both tabular assessment data on building characteristics and spatial data that show the location of each property) for every assessed parcel of land are generally available for the airshed as spatially referenced digital databases, suitable for use with common geographic information systems (GIS). These data may increase the resolution and accuracy of variables used in exposure assessment models and epidemiological analyses, but the authors have found few published studies using SPAD for developing exposure assessments or in epidemiological analyses of air pollution impacts on health.
The purpose of this paper is to describe SPAD and to illustrate their potential utility for neighbourhood level exposure estimates and epidemiological research. Three possible uses of SPAD are examined, including: (1) creating variables for use in land use regression modelling of neighbourhood levels of ambient air pollution; (2) enhancing wood smoke exposure estimates by mapping fireplace locations; and (3) using data available on individual building characteristics to produce a regional air pollution infiltration model.
Results and discussion
Typical SPAD characteristics
Common variables in tabular assessment data
School District #, Area #, Township Range, Jurisdiction #, Neighbourhood #, Street Address, Street Direction, Street Type, ZIP Code, City, Property Identifier
Appraisal Date, Property Size, Property Use Code, Land Use Code, Electricity, Water, Sewer, Street Surface Type, # of Dwelling Units, # of Outbuildings, # of Improvements, Building Permit.
Sale Date, Sale Price, Sales Excise Number, Deed Type, Qualification Code, Multiple Sales, Land Value, Improvements Value.
Improvement Type, Structure Use, Building Type, # of Stories, Year Built, Total Square Footage, # of Bedrooms, Predominant Heating Type, Fireplace, Structural Quality.
SPAD are created when tabular property assessment data are spatially referenced, either by linking property addresses to a digital street network, or by linking property identifiers to a digital cadastral map. Spatial referencing allows for complex queries of the tabular assessment, the results of which can be mapped. In effect, the spatial resolution of SPAD is the individual parcel size, generally much finer than other spatially referenced data commonly used in exposure assessment and epidemiological analyses, such as Census data.
Data format and availability
Access to SPAD (or its constituent tabular and cadastral data) is markedly different in British Columbia in comparison to Washington State. In British Columbia, researchers must negotiate data sharing or purchasing agreements with each jurisdiction in order to access SPAD, and may also have to purchase additional tabular assessment data directly from the BC Assessment Authority in order to develop SPAD specific to the research question. In Washington State, SPAD are available for download through each county's internet site, or may be ordered directly from each county at no cost or for a small fee (i.e., for CD writing and postage). In many cases, due to large file sizes, the tabular assessment data and the spatial cadastral data are provided separately, and must be linked by the researcher using GIS to create the final SPAD.
Linking tabular assessment data using property addresses or identifiers to produce SPAD is not always trouble free. In cases where tabular assessment data and the spatial cadastral data are provided by the same jurisdiction, linking the two datasets often is easily accomplished. In Washington State, for example, where each county develops and maintains its own SPAD, we were able to download the tabular assessment data and the spatial cadastral data, and link each record with a 98 percent success rate. For the British Columbia portion of the airshed, we initially purchased tabular assessment data from the BC Assessment Authority and spatially referenced them using the included property addresses and a commercially available digital street network with ESRI© ArcGIS 8.1. Approximately 1.1 million records were received from BC Assessment for the entire Georgia Basin airshed, which is comprised of 26 separate taxing jurisdictions. Linking between the tabular assessment data and the street network was successfully completed for approximately 83 percent of the records, with the number of links in urban areas better than in rural regions (89 percent versus 67 percent respectively). The lower success rate in rural regions is generally due to incomplete or non-standard street addresses (i.e., post office boxes or rural post offices rather than street addresses) in the tabular assessment data. Also, the road network (circa 2003) did not contain information on the most recent subdivisions and new construction, so those properties were excluded by default. We subsequently acquired cadastral data from each of the 26 taxing authorities, and achieved an average success rate of 96 percent when linking the tabular data provided by BC Assessment Authority. Obviously, linking tabular assessment data to cadastral data is preferred; however, in jurisdictions without digital cadastral data, using a digital street network may be the only option, and link success rates may vary widely.
Developing variables from SPAD for use in land use regression models of neighbourhood pollution levels
When adequate measured data are not available, neighbourhood level exposure assessments may use outdoor pollution levels derived by models that require land use data as inputs. For example, land use regression (LUR) models have been used to predict traffic-related air pollution levels for neighbourhood areas depending on nearby roads, traffic volume, population density, and land uses [3–5]; these predicted levels were then used as indicators of exposure for epidemiological analyses. In their 1997 study, Briggs, Collins et al. used land cover data interpreted from aerial photographs, as well as building density (six classes) derived from local planning maps in a LUR model to predict spatial surfaces of nitrogen dioxide (NO2) levels in three European cities . In 2003, Brauer et al. used 100 m raster grids of population density in a LUR model to predict fine particulate (PM2.5) levels at over 10,000 residential addresses in Sweden and the Netherlands . The 100 m raster grids of population density were developed by national agencies from population registries that record the current residential address for most of the population. In research currently underway in the Pacific Northwest, Brauer, Henderson, et al. have included the area of commercial land, provided by local government as a digital map, as a predictor in a LUR model of traffic-related air pollution in Vancouver, British Columbia .
SPAD also provide very detailed information about land use. In British Columbia, properties have a designated actual use code, organized in an hierarchical fashion. For example, a property's Level 1 (Property Code) designation may be 'major industry'; the Level 2 (Actual Use Code) designation may be 'primary metal industry', and its Level 3 (Manual Class Code) designation may be 'primary smelting and refining'. Similarly, a residential property may be designated as residential, single family residence, and 1 1/2 storey good condition. In British Columbia, there are 950 unique Level 3 designations. In Washington State, the property use codes used by counties generally correspond to the standard Land Use Coding Manual created by the Urban Renewal Administration, Housing and Home Finance Agency and Bureau of Public Roads (1965), which contains 4 levels of classification (see http://www.planning.org/LBCS/OtherStandards/SLUCM.html for more information).
Detailed information from SPAD for commercial land use
'Actual Use' Classification
'Actual Use' Classification
Storage and Warehousing – closed
Stores and Services – Commercial
Fast Food Restaurant
Office Building (primary use)
Automobile sales – lot
Industrial – Vacant
Self-Serve Service Station
Commercial – strata lot
Shopping Center – neighbourhood
Automobile Paint Shop/Garage
Stores and Offices
Metal Fabricating Industry
Shopping Center – regional
Bakery and Biscuit Manufacturing
Shopping Center – community
Convenience Store/Service Station
Motel and Auto Court
Lumber Yard or Building Supplies
Furniture and Fixtures Industry
Marine and Navigational Facilities
Sash and Door Industry
Soft Drink Bottling
Storage and Warehousing – cold
Stores and Living Quarters
Using SPAD to estimate exposure to wood smoke
Exposure to wood smoke has been associated with negative health impacts, particularly for children and the elderly [7–9] and there is increasing interest in developing models to predict spatial estimates of wood smoke levels in order to provide spatially refined estimates that do not rely on individual surveys or monitoring campaigns. Spatial estimates of residential wood burning have been included in regional emissions inventories prepared for air quality management purposes and so a very brief overview of the methods used for emissions inventory purposes is provided here. In general, the contribution of residential wood burning to regional air quality is estimated by applying an emission factor to the proportion of households thought to have a wood burning appliance. Both the emission factor and the proportion of households are often derived from telephone surveys conducted in the region of interest. An example of this approach, employed for eight regions in British Columbia, is described in a recent report produced by the British Columbia Ministry of Water, Land and Air Protection . Recent research by Tian et al. describes an approach in which a number of spatial variables are used to predict the proportion of wood-burning households, similar to the LUR models described above . In their study, Tian et al. found that elevation, age (retired or ages 34-54), presence of farm income, and owner occupied residences predicted the number of households using wood as a primary heating source (as per the 1990 US Census) for census block groups. While it is not clear how this improves on the data already available from the US Census (at least for 1990 and 2000), this method could be used where US Census data do not exist, i.e., Canada.
In the context of epidemiological studies, Larson et al. have used SPAD in conjunction with other spatial variables in order to predict fine particulate (PM2.5) levels associated with wood smoke for a large epidemiological study currently underway in the Georgia Basin Puget Sound Airshed . Preliminary results suggest that building age, population density, and number of fireplaces are relatively strongly correlated with measured PM2.5 in the study area. A range of socio-economic variables are more weakly correlated. Of particular interest, this approach negates the need for additional information on wood-burning practices and emissions factors by relating spatial variables derived from SPAD and other sources (i.e., Census data) directly to actual measures of PM2.5 on cold clear evenings.
Infiltration modelling using SPAD
Population level epidemiological studies of air pollution commonly use an indirect approach to exposure assessment by assigning exposure levels based on outdoor ambient air pollution levels at the residential location, even though an increasing number of personal monitoring studies have shown that exposure measurements based on ambient monitoring are usually lower than those derived from personal monitoring . Strong associations have been found between indoor and outdoor PM2.5 concentrations which indicate that a significant proportion of indoor fine particles are of outdoor origin , and other studies have identified specific building characteristics that influence infiltration rates, for example, type of basement, and year of construction .
Variables common in SPAD that may be used in a regional infiltration model
Property Size, Property Use, Topography, Building Permit Class.
Improvement Type, Structure Use, Building Type, # of Stories, Year Built, Total Square Footage, Predominant Construction Type, # of Bedrooms, Predominant Heating Type, Air Conditioning, Fireplace, Structural Quality.
Considering that many exposure assessments and epidemiological analyses of the impacts of air pollution on health have been undertaken at regional scales, and that only recently have researchers begun to investigate neighbourhood-level variation in pollutant levels, it is not surprising that the authors could not find any published exposure assessments or epidemiological studies of air pollution that made use of SPAD. This paper illustrates that SPAD are a readily available data source that may provide an opportunity for conducting air pollution exposure assessment at neighbourhood level scales. SPAD also provide highly detailed information on building characteristics that may prove useful for modelling indoor levels of ambient-origin air pollution based on building infiltration characteristics, and there may be some utility in using SPAD to develop or refine indicators of socio-economic status. Some limitations to using SPAD are also apparent: SPAD are very large datasets which require GIS software and expertise to clean and extract the required subset of data in order to avoid slow processing times; and issues of comparability between GIS formats and data content may arise when a study area encompasses more than one jurisdiction. Limitations notwithstanding, the authors expect to see increasing uses of SPAD for exposure assessment and epidemiological analyses in the future, as researchers continue to investigate spatial variations in pollutant levels and other factors affecting exposure at increasingly finer scales.
SPAD were developed for the Canadian (southwest British Columbia) portion of the airshed by spatially referencing tabular property assessment data provided by the province to cadastral (parcel) data provided by municipal governments. For the American portion of the airshed (a portion of Washington State) the data were acquired in a readily useable format from each county. These data are used to illustrate the typical characteristics of SPAD, and to identify issues for using SPAD in terms of format, attributes and availability. Conceptual applications of SPAD to exposure assessment are demonstrated using SPAD from British Columbia and Washington State.
This research has been funded by the BC Centre for Disease Control, via a grant provided by Health Canada as part of the ongoing Border Air Quality Strategy agreement between Canada and the United States.
- Williams FLR, Ogston SA: Identifying populations at risk from environmental contamination from point sources. Occup Environ Med. 2002, 59: 2-8. 10.1136/oem.59.1.2.PubMedPubMed CentralView ArticleGoogle Scholar
- O'Neill MS, Jerrett M, Kawachi L, Levy JL, Cohen AJ, Gouveia N, Wilkinson P, Fletcher T, Cifuentes L, Schwartz J: Health, wealth, and air pollution: Advancing theory and methods. Environmental Health Perspectives. 2003, 111: 1861-1870.PubMedPubMed CentralView ArticleGoogle Scholar
- Clench-Aas J, Bartonova A, Bohler T, Gronskei KE, Sivertsen B, Larssen S: Air pollution exposure monitoring and estimating Part I. Integrated air quality monitoring system. Journal of Environmental Monitoring. 1999, 1: 313-319. 10.1039/a902775k.PubMedView ArticleGoogle Scholar
- Briggs DJ, Collins S, Elliott P, Fischer P, Kingham S, Lebret E, Pryl K, Van Reeuwijk H, Smallbone K, Van der Veen A: Mapping urban air pollution using GIS: a regression-based approach. International Journal of Geographical Information Science. 1997, 11: 699-718. 10.1080/136588197242158.View ArticleGoogle Scholar
- Brauer M, Hoek G, van Vliet P, Meliefste K, Fischer P, Gehring U, Heinrich J, Cyrys J, Bellander T, Lewne M, Brunekreef B: Estimating long-term average particulate air pollution concentrations: Application of traffic indicators and geographic information systems. Epidemiology. 2003, 14: 228-239. 10.1097/00001648-200303000-00019.PubMedGoogle Scholar
- Brauer M, Henderson S, Jerrett M, Beckerman B: Land Use Regression Modeling of Nitrogen Oxides and Fine Particulate Matter in the Greater Vancouver Regional District: November 8 - 11; Blaine, Washington. 2005,Google Scholar
- Salam MT, Li YF, Langholz B, Gilliland FD: Early-life environmental risk factors for asthma: Findings from the children's health study. Environmental Health Perspectives. 2004, 112: 760-765.PubMedPubMed CentralView ArticleGoogle Scholar
- Boman BC, Forsberg AB, Jarvholm BG: Adverse health effects from ambient air pollution in relation to residential wood combustion in modern society. Scandinavian Journal of Work Environment & Health. 2003, 29: 251-260.View ArticleGoogle Scholar
- Larson TV, Koenig JQ: Wood Smoke - Emissions and Noncancer Respiratory Effects. Annual Review of Public Health. 1994, 15: 133-156. 10.1146/annurev.pu.15.050194.001025.PubMedView ArticleGoogle Scholar
- British Columbia Ministry of Water LAP: Residential Wood Burning Emissions in British Columbia. 2004, Victoria, BC,Google Scholar
- Tian YQ, Radke JD, Gong P, Yu Q: Model development for spatial variation of PM2.5 emissions from residential wood burning. Atmospheric Environment. 2004, 38: 833-843. 10.1016/j.atmosenv.2003.10.040.View ArticleGoogle Scholar
- Larson T, Su J, Baribeau A, Buzzelli M, Setton E, Brauer M: A Spatial Model of Urban Winter Woodsmoke Concentrations: ; Blaine, Washington. 2005,Google Scholar
- Toivola M, Alm S, Reponen T, Kolari S, Nevalainen A: Personal exposures and microenvironmental concentrations of particles and bioaerosols. Journal of Environmental Monitoring. 2002, 4: 166-174. 10.1039/b108682k.PubMedView ArticleGoogle Scholar
- Rojas-Bracho L, Suh HH, Oyola P, Koutrakis P: Measurements of children's exposures to particles and nitrogen dioxide in Santiago, Chile. Science of the Total Environment. 2002, 287: 249-264. 10.1016/S0048-9697(01)00987-1.PubMedView ArticleGoogle Scholar
- Chang TJ, Huang MY, Wu YT, Liao CM: Quantitative prediction of traffic pollutant transmission into buildings. Journal of Environmental Science and Health Part a-Toxic/Hazardous Substances & Environmental Engineering. 2003, 38: 1025-1040. 10.1081/ESE-120019861.View ArticleGoogle Scholar
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