Spatiotemporal Land Use Regression Models of Fine, Ultrafine, and Black Carbon Particulate Matter in New Delhi, India

TLDR

Air pollution in New Delhi is a major environmental and health concern. The study develops land‑use regression models to identify determinants of variability in PM₂.₅, black carbon, and ultrafine particle concentrations in the city. Using 136–147 h of monitoring at 26–39 sites, the authors built separate morning and afternoon LUR models that incorporated spatial variables and fixed‑site measurements for PM₂.₅ and BC, while UFPN data were limited to site‑based observations. Median concentrations were 133 µg m⁻³ for PM₂.₅, 11 µg m⁻³ for BC, and 4.0×10⁴ cm⁻³ for UFPN; temporal variability exceeded spatial variability, morning models had higher R², and population density and road metrics were significant predictors, yet geographic variables explained less variance than in other studies, highlighting uncharacterized sources.

Abstract

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800–1200) and afternoon (1200–1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96–232) μg m–3, 11 (6–21) μg m–3, and 40 (27–72) × 103 cm–3 respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R2 values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.