A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China.



Citation:

Jin L, Berman JD, Warren JL, Levy JI, Thurston G, Zhang Y, Xu X, Wang S, Zhang Y, Bell ML. A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China. Environmental Research 2019;177:108597.

Abstract:

BACKGROUND: Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. OBJECTIVE: A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. METHODS: In each of four seasons in 2016-2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. RESULTS: We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. CONCLUSIONS: Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.