Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance. (1st January 2021)

• Record Type: Journal Article
• Authors: Xu, Xiangyu; Qin, Ning; Yang, Zhenchun; Liu, Yunwei; Cao, Suzhen; Zou, Bin; Jin, Lan; Zhang, Yawei; Duan, Xiaoli
• Is Part Of: Environmental pollution. Volume 268(2021)Part B
• Publication Date: 2021-01-01
• Subjects: Land use regression -- Environmental modeling -- Diurnal model -- Particulate matter -- NO2; LUR land use regression -- PM2.5 fine particulate matter -- PM10 inhalable particulate matter -- NO2 nitrogen dioxide -- GIS Geographic Information System -- coefficients of determination R2 -- VIF variance inflation factor -- LOOCV leave one out cross-validation -- 10-fold-CV 10-fold cross-validation -- HV hold-out validation -- RMSE root-mean-square error -- MAE absolute estimation error -- COR-R2 Pearson's correlation R2 -- MSE-R2 mean squared error based R2 -- temp temperature -- RH relative humidity -- MR meteorological variables -- UFP ultrafine particles -- BC black carbon; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2020.115951
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: 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 R 2 ) 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 R 2 and validation R 2 . 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. Graphical abstract: Image 1 Highlights: The land use regression models were developed based on mobile monitoring campaigns. The strategy for daytime/nighttime independent modeling was evaluated in two periods. Daytime/nighttime models (PM2.5 and PM10 ) performed better than the full-day models. Exposure surfaces revealed variations in concentrations and spatial distribution. Abstract : The daytime/nighttime independent LUR modeling strategy can improve the model performance and contribute to achieving reliable exposure predictions for PM2.5 and PM10 .