A multi-axis differential optical absorption spectroscopy (MAX-DOAS) instrument was deployed in May and June 2016 at a monitoring station (37.18^∘ N, 114.36^∘ E) in the suburban area of Xingtai, which is one of the most polluted cities in the North China Plain (NCP), during the Atmosphere-Aerosol-Boundary Layer-Cloud (A²BC) experiment and Air chemistry Research In Asia (ARIAs) joint experiments to derive tropospheric vertical profiles of NO₂, SO₂, HONO, HCHO, CHOCHO and aerosols. Aerosol optical depths derived from MAX-DOAS were found to be consistent with collocated sun-photometer measurements. Also the derived near-surface aerosol extinction and HCHO mixing ratio agree well with the coincident visibility meter and in situ HCHO measurements, with mean HCHO near-surface mixing ratios of ∼3.5 ppb. Underestimations of MAX-DOAS results compared to in situ measurements of NO₂ (∼60 %) and SO₂ (∼20 %) are found expectedly due to vertical and horizontal inhomogeneity of trace gases. Vertical profiles of aerosols and NO₂ and SO₂ are reasonably consistent with those measured by a collocated Raman lidar and aircraft spirals over the station. The deviations can be attributed to differences in sensitivity as a function of altitude and substantial horizontal gradients of pollutants. Aerosols, HCHO and CHOCHO profiles typically extended to higher altitudes (with 75 % integrated column located below ∼1.4 km) than NO₂, SO₂ and HONO did (with 75 % integrated column below ∼0.5 km) under polluted conditions. Lifted layers were systematically observed for all species (except HONO), indicating accumulation, secondary formation or long-range transport of the pollutants at higher altitudes. Maximum values routinely occurred in the morning for NO₂, SO₂ and HONO but occurred at around noon for aerosols, HCHO and CHOCHO, mainly dominated by photochemistry, characteristic upslope–downslope circulation and planetary boundary layer (PBL) dynamics. Significant day-to-day variations are found for all species due to the effect of regional transport and changes in synoptic pattern analysed with the backward propagation approach based on HYSPLIT trajectories. Low pollution was often observed for air masses from the north-west (behind cold fronts), and high pollution was observed from the southern areas such as industrialized Wu'an. The contribution of regional transport for the pollutants measured at the site during the observation period was estimated to be about 20 % to 30 % for trace gases and about 50 % for aerosols. In addition, agricultural burning events impacted the day-to-day variations in HCHO, CHOCHO and aerosols. It needs to be noted that although several MAX-DOAS measurements of trace gases and aerosols in the NCP area have been reported in previous studies, this study is the first work to derive a comprehensive set of vertical profiles of NO₂, SO₂, HONO, HCHO, CHOCHO and aerosols from measurements of one MAX-DOAS instrument. Also, so far, the validation of MAX-DOAS profile results by comparison with various surface in situ measurements as well as profile measurements from lidar and aircraft is scarce. Moreover, the backward propagation approach for characterizing the contributions of regional transport of pollutants from different regions was applied to the MAX-DOAS results of trace gases and aerosols for the first time.

Rapid and accurate detection of protein toxins is crucial for public health. The Raman spectra of several protein toxins, such as abrin, ricin, staphylococcal enterotoxin B (SEB), and bungarotoxin (BGT), have been studied. Multivariate scattering correction (MSC), Savitzky–Golay smoothing (SG), and wavelet transform methods (WT) were applied to preprocess Raman spectra. A principal component analysis (PCA) was used to extract spectral features, and the PCA score plots clustered four toxins with two other proteins. The k-means clustering results show that the spectra processed with MSC and MSC-SG methods have the best classification performance. Then, the two data types were classified using partial least squares discriminant analysis (PLS-DA) with an accuracy of 100%. The prediction results of the PCA and PLS-DA and the partial least squares regression model (PLSR) perform well for the fingerprint region spectra. The PLSR model demonstrates excellent classification and regression ability (accuracy = 100%, Rcv = 0.776). Four toxins were correctly classified with interference from two proteins. Classification models based on spectral feature extraction were established. This strategy shows excellent potential in toxin detection and public health protection. These models provide alternative paths for the development of rapid detection devices.

The rapid and sensitive detection of pathogenic and suspicious bioaerosols are essential for public health protection. The impact of pollen on the identification of bacterial species by Raman and Fourier-Transform Infrared (FTIR) spectra cannot be overlooked. The spectral features of the fourteen class samples were preprocessed and extracted by machine learning algorithms to serve as input data for training purposes. The two types of spectral data were classified using classification models. The partial least squares discriminant analysis (PLS-DA) model achieved classification accuracies of 78.57% and 92.85%, respectively. The Raman spectral data were accurately classified by the support vector machine (SVM) algorithm, with a 100% accuracy rate. The two spectra and their fusion data were correctly classified with 100% accuracy by the random forest (RF) algorithm. The spectral processed algorithms investigated provide an efficient method for eliminating the impact of pollen interference.