Search

Abstract Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University , Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of − 0.0043 ppb, mean absolute error of 0.4836 ppb, root mean squared error of 0.5320 ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control. Graphical abstract Unlabelled Image [ABSTRACT FROM AUTHOR]

Peroxyacyl nitrates (PANs) are photochemical secondary pollutants that play a key role in the atmospheric chemistry of the troposphere. However, there have been few studies on the long-term variation and inter-regional transport of PANs. In this study, summertime ambient PAN concentrations were monitored at urban and rural sites in Beijing and Hebei, China, between 2006 and 2014. In Beijing, the peak concentrations of PAN and PPN were in the range of 6–17 ppbv and 0.6–2.2 ppbv, respectively, higher than concentrations in other provinces. The nitrogen oxide (NO x ) concentration decreased at a rate of 1.7 ppbv/yr (∼4% yr −1 ), and the PAN concentration decreased at a rate of 0.03 ppbv/yr (∼3% yr −1 ), while the ozone (O 3 ) concentration increased at a rate of 1.5 ppbv/yr (∼4% yr −1 ). Trajectory clustering analyses showed that high concentrations of PAN were mainly affected by low air masses transported medium/short distances from South Beijing, and the potential source contribution function maps showed that the likely pollution source area was concentrated in the southern region of Beijing. These findings provide a theoretical basis for pollution control in this region. [ABSTRACT FROM AUTHOR]