Application of land use regression to assess exposure and identify potential sources in PM, BC, NO concentrations.

BACKGROUND

Understanding spatial variation of air pollution is critical for public health assessments. Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations. However, they have limited application in China due to the lack of spatially resolved data.

OBJECTIVE

Based on purpose-designed monitoring networks, this study developed LUR models to predict fine particulate matter (PM), black carbon (BC) and nitrogen dioxide (NO) exposure and to identify their potential outdoor-origin sources within an urban/rural region, using Taizhou, China as a case study.

METHOD

Two one-week integrated samples were collected at 30 PM (BC) sites and 45 NO sites in each two distinct seasons. Samples of 1/3 of the sites were collected simultaneously. Annual adjusted average was calculated and regressed against pre-selected GIS-derived predictor variables in a multivariate regression model.

RESULTS

LUR explained 65% of the spatial variability in PM, 78% in BC and 73% in NO. Mean (±Standard Deviation) of predicted PM, BC and NO exposure levels were 48.3 (±6.3) μg/m, 7.5 (±1.4) μg/m and 27.3 (±8.2) μg/m, respectively. Weak spatial corrections (Pearson r = 0.05-0.25) among three pollutants were observed, indicating the presence of different sources. Regression results showed that PM, BC and NO levels were positively associated with traffic variables. The former two also increased with farm land use; and higher NO levels were associated with larger industrial land use. The three pollutants were correlated with sources at a scale of ≤5 km and even smaller scales (100-700m) were found for BC and NO.

CONCLUSION

We concluded that based on a purpose-designed monitoring network, LUR model can be applied to predict PM, NO and BC concentrations in urban/rural settings of China. Our findings highlighted important contributors to within-city heterogeneity in outdoor-generated exposure, and indicated traffic, industry and agriculture may significantly contribute to PM, NO and BC concentrations.