A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities
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In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.
KeywordsSoil Pb Spatial modeling Classification and regression trees Random Forest Urban
We are especially grateful to the homeowners for access to their property and to the University of California, Davis for use of the XRF. The building footprint dataset was used with permission under a license agreement with Baltimore City. Our understanding of GIS and spatial statistics greatly benefited from conversations with John Bognar, Dr. Adele Cutler, Amanda Elliot Lindsey, Dr. Elizabeth Freeman, Scott Haag, David Lewis, Dr. Zewei Maio, Dr. Samuel Simkin, Jim Trimble, and Dr. Weiqi Zhou. This work is a contribution to the long-term ecological research program (a program of the National Science Foundation) and the Cary Institute of Ecosystem Studies and was supported by NSF grants DEB 042376 and 0808418.
- Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). Land use and land cover classification systems for use with remote sensor data. Washington, DC: US Geological Service. Professional Paper 964.Google Scholar
- Bellinger, D., & Dietrich, K. N. (1994). Low-level lead exposure and cognitive function in children. Pediatric Annals, 23, 600–605.Google Scholar
- Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. London: Chapman and Hall.Google Scholar
- Centers for Disease Control (CDC). (2012). CDC response to advisory committee on childhood lead poisoning prevention recommendations in “Low level lead exposure harms children: A renewed call of primary prevention.” Atlanta, GA. US Department of Health and Human Services. Available at http://www.cdc.gov/nceh/lead/acclpp/cdc_response_lead_exposure_recs. pdf. Accessed November 1, 2012.
- Chaney, R. L., Sterrett, S. B., & Mielke, H. W. (1984). The potential for heavy metal exposure from urban gardens and soil. In J. R. Preer (Ed.), Proceedings of the symposium on heavy metals in urban gardens. Univ. Dist. Columbia Extension Service, Washington, DC.Google Scholar
- Dietrich, K. N., Berger, O. G., Succop, P. A., Hammond, P. B., & Bornschein, R. L. (1993). The developmental consequences of low to moderate prenatal and postnatal lead exposure: Intellectual attainment in the Cincinnati Lead Study cohort following school entry. Neurotoxicology Teratology, 15, 37–44.CrossRefGoogle Scholar
- Duggan, M. J., & Inskip, M. J. (1985). Childhood exposure to Pb in surface dust and soil: A community health problem. Public Health Review, 13, 1–54.Google Scholar
- Freeman, E., & Frescino, T. (2009). ModelMap. Methods in R, 18.Google Scholar
- Griffith, D. A., Johnson, D. L., & Hunt, A. (2009). The geographic distribution of metals in urban soils: The case of Syracuse, NY. GeoJournal, 74(4), 275–291.Google Scholar
- JMP 7.0.2. (2007). SAS Institute Inc.Google Scholar
- Liaw, A., & Wiener, M. (2009). Classification and regression by random forest. Resampling Methods in R, The boot Package,18.Google Scholar
- Maryland Department of the Environment. (2007). Maryland plan to eliminate childhood Pb poisoning by 2010. Baltimore, 110.Google Scholar
- Mielke, H. W. (1999). Pb in the inner cities. American Scientist, 87(1), 62–73.Google Scholar
- Mielke, H. W., & Reagan, P. L. (1998). Soil is an important pathway of human lead exposure. Environmental Health Perspectives, 106(1), 217–229.Google Scholar
- Nigg, J. T., Knottnerus, G. M., Martel, M. M., Nikolas, M., Cavanagh, K., Karmaus, W., et al. (2008). Low blood lead levels associated with clinically diagnosed attention-deficit/hyperactivity disorder and mediated by weak cognitive control. Biological Psychiatry, 63(3), 325–331.CrossRefGoogle Scholar
- Pouyat, R. V., Belt, K., Pataki, D., Groffman, P. M., Hom, J., & Band, L. (2007a). Urban land-use change effects on biogeochemical cycles. In J. G. Canadell, D. E. Pataki & L. F. Pitelka (Eds.), Terrestrial ecosystems in a changing world (pp. 45–58). Global Change, The IGBP Series, Springer, Berlin-Heidelberg-New York.Google Scholar
- S-Plus 6.1 for Windows. (2002). Insightful Corporation.Google Scholar
- United States Census Bureau. (2012). Topologically Integrated Geographic Encoding and Referencing (TIGER) database. Census.gov. May 20, 2012. <http://www.census.gov/geo/www/tiger/>.
- Weathers, K. C., Simkin, S. M., Lovett, G. M., & Lindberg, S. E. (2006). Empirical modeling of atmospheric deposition in mountainous landscapes. Ecological Applications, 16(4), 1590–1607.Google Scholar