The role of spatial representation in the development of a LUR model for Ottawa, Canada

Abstract

A land use regression (LUR) model for the mapping of NO2 concentrations in Ottawa, Canada was created based on data from 29 passive air quality samplers from the City of Ottawa’s National Capital Air Quality Mapping Project and two permanent stations. Model sensitivity was assessed against three spatial representations of population: population at the dissemination area level, population at the dissemination block level and a dasymetrically derived population representation. A spatial database with land use, roads, population, zoning, greenspaces and elevation was created. Polycategorical zoning data were used in dasymetric mapping to spatially focus population data derived from the dissemination blocks to a sub-block level for comparison purposes. Dasymetric population mapping provided no significant LUR model improvement in explained variance when compared to block level population; however, both the former were significantly better than the dissemination area level population representations. However, where block level population is not available or too costly to acquire, our method using polycategorical zoning data provides a viable alternative in LUR modelling endeavours.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment

“…The predicted NO 2 surfaces were similar with respect to concentrations in peripheral hotspot areas as both models included population density and industrial areas as predictors. However, the addition of intersection density in this study appeared to improve upon the overall model fit and suggested that NO 2 concentrations in central areas of Ottawa where the street network is characterized by higher connectivity were underestimated by Parenteau and Sawada (2012). Wheeler et al (2008) found that their NO 2 LUR model in Windsor explained less spatial variation than a previous model by Luginaah et al (2006) and attributed this to a lack of traffic data.…”

“…The study area also offered an opportunity to improve upon a previous NO 2 LUR model for Ottawa by using measurements from more sampling locations and two seasonal monitoring campaigns rather than just fall measurements (Parenteau and Sawada, 2012). Parenteau and Sawada (2012) found that population densities calculated based on dissemination block (DB) data rather than the traditional dissemination area level improved the prediction of NO 2 . The current study computed population and predictor variables from DB level data, but additionally assessed the influence of industrial point sources and intersection density.…”

“…The sampling dates were chosen based on previous research suggesting that fall and spring levels of air pollution were more representative of annual concentrations in Windsor, Ontario (Wheeler et al, 2008). Furthermore, the only previous monitoring campaign for NO 2 in Ottawa conducted by Parenteau and Sawada (2012) in 2007 took place in the fall, therefore the current study provided an opportunity to substantiate their results.…”

“…LUR models have been developed for NO 2 and other pollutants in approximately thirteen urban areas across the country. Parenteau and Sawada (2012) developed an LUR model for NO 2 in Ottawa using spatial monitoring data from 30 sites sampled in fall 2007. The current study builds and expands on that work by developing LUR models for NO 2 and BTEX species that incorporate new predictors, including industrial point source emissions and alternative traffic proxies, and two seasonal sampling campaigns in Ottawa, Ontario, Canada.…”

The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health

“…Details of that model are published elsewhere [47]. The model, which included data on the road network, population, green spaces and industrial land-use, yielded an R 2 of 0.8055.…”