Accurately predicting the short- and long-term variations of total nitrogen (TN) is vital for operating the wastewater treatment plants (WWTPs), considering the critical role TN plays in reflecting the eutrophication of wastewater. However, only a few relevant water quality parameters with limited samples can be obtained in WWTPs, which tremendously increases the difficulty in precisely predicting TN concentration. In this study, a multiphase attention-based recurrent neural network (MPA-RNN) is proposed. Benefited from its unique decomposition-summary attention structure, MPA-RNN first learns the temporal correlations and effectively excavates the useful information hidden in the historical data. Then, by designing a two-channel structure to transmit attention information, summary attention can integrate the decomposed information and learn the spatial relationships without information loss. Experimental results demonstrate that MPA-RNN achieves the best performance on both the SML2010 and practical TN datasets with the smallest root-mean-squared error, mean absolute error, and mean absolute percentage error when compared with the other state-of-the-art methods.

This paper developed a noise-robust self-adaptive support vector machine (namely, NSSVM) for the multi-class classification problem in the residual oxygen concentration measurement of pharmaceutical vials. Two key elements are emphasized by the NSSVM framework: First, targeting the fast time-varying noises, two mutually compensated signal enhancement measures of SWT-based filtering and AIRPLS-based baseline corrector are contributed to obtain stable features being fed to classifier, where SWT and AIRPLS denote the synchrosqueezed wavelet transforms and adaptive iterative reweighted penalty least squares, respectively. Second, most typical slow time-varying interferences are avoided dynamically by the self-adaptive thresholding scheme learned from the intrinsic priori of production line. The experimental results indicate that our NSSVM-based measurement method achieves a considerable performance, with an average accuracy of 97.24% when applied on an automated visual inspection (AVI) machine of Truking Tech., which indicates that the proposed method is promising to the in-situ instrumentation for the residual oxygen concentration measurement of pharmaceutical glass vials.

Anthropogenic carbon dioxide (CO₂) emissions mainly come from cities and their surrounding areas. Thus, continuous measuring of CO₂ in urban areas is of great significance to studying human CO₂ emissions. We developed a compact, precise, and self-calibrated in-situ CO₂/H₂O sensor based on TDLAS (tunable diode laser absorption spectroscopy), WMS (wavelength modulation spectroscopy), and VCSEL (vertical-cavity surface-emitting laser). Multi-harmonic detection is utilized to improve the precision of both measurements to 0.02 ppm for CO₂ and 1.0 ppm for H₂O. Using the developed sensor, we measured CO₂ concentrations continuously in the city center of Munich, Germany, from February 2018 to January 2019. Urban CO₂ concentrations are strongly affected by several factors, including vegetation photosynthesis and respiration (VPR), planetary boundary layer (PBL) height, and anthropogenic activities. In order to further understand the anthropogenic contribution in terms of CO₂ sources, the HySPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model was applied to calculate six-hour backward trajectories. We analyzed the winter CO₂ with the trajectory clustering, PSCF (potential source contribution function), and CWT (concentration weighted trajectory) methods, and found that local emissions have a great impact on urban CO₂ concentration, with main emission sources in the north and southeast directions of the measurement site. In situations with an uneven trajectory distribution, PSCF proves somewhat superior in predicting the potential emission sources compared to CWT.

Greenhouse gases have a significant impact on general global climate change. A self-calibrated multiharmonic measurement system based on tunable diode laser absorption spectroscopy (TDLAS) with wavelength modulation spectroscopy, using the vertical-cavity surface-emitting laser, is developed and applied to measure the CO₂ and H₂O concentrations in the urban areas. The precision of the developed system is evaluated using the Allan variance method, and it is found that the multiharmonic can enhance the precision of the system and the first-to-third harmonics are sufficient to achieve the lowest Allan deviation. To validate its accuracy, comparison experiments with the commercial nondispersive infrared (NDIR) instrument (Li-Cor 840A) are performed and the results demonstrate the high accuracy of the TDLAS system and its consistency with the NDIR instrument. Measurements were carried out in autumn and winter of 2017 in the city center of Munich. The results indicate that the in situ measurement of the CO₂ concentration in the downtown area correlates with both natural and anthropogenic activities. Due to its high precision and accuracy, the self-calibrated multiharmonic measurement system has a great potential to further study the emission and distribution of CO₂ in the urban areas.