Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance
2021
Xu, Xiangyu | Qin, Ning | Yang, Zhenchun | Liu, Yunwei | Cao, Suzhen | Zou, Bin | Jin, Lan | Zhang, Yawei | Duan, Xiaoli
Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM₂.₅), inhalable particulate matter (PM₁₀), and nitrogen dioxide (NO₂) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R²) for the LUR models ranged from 0.53–0.87 (PM₂.₅), 0.53–0.85 (PM₁₀), and 0.33–0.67 (NO₂). The performance of the daytime/nighttime LUR models for PM₂.₅ and PM₁₀ was better than that of the full-day models according to the results of model adjusted R² and validation R². Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO₂ to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM₂.₅/PM₁₀ has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM₂.₅ and PM₁₀. Nighttime could be a critical exposure period, due to high pollutant concentrations.
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