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Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δ p - G f method based on the particle depolarization ratio at 355 nm ( δ p 355 ) and the fluorescence capacity ( G f ), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δ p 355 - G f method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δ p 355 - G f classification method and the δ p 355 - G f distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing. [ABSTRACT FROM AUTHOR]

ABSTRACT Spiroplasma eriocheiris is a pathogen in the Chinese mitten crab Eriocheir sinensis and is associated with tremor disease. S. eriocheiris infects virtually all artificially bred crustaceans, resulting in significant economic losses to the aquaculture industry in China. Recombinase polymerase amplification (RPA) is considered a promising technology for the rapid and sensitive detection of disease. For the rapid detection of S. eriocheiris, this study established basic RPA, RPA‐EXO and RPA‐LFD, which were visualised via agarose gel electrophoresis, SYBR Green I, fluorescence signals and test lines on lateral flow dipsticks. Specific primers and probes were designed based on the 16S ribosomal RNA sequence of S. eriocheiris. The optimal primer pairs for the basic RPA, RPA‐EXO and RPA‐LFD were RPA‐F3/R3, RPA‐F3/R3 and RPA‐LFD‐F3/R3, respectively. The optimal temperatures and times of basic RPA, RPA‐EXO and RPA‐LFD were 37°C for 25 min, 39°C for 30 min and 39°C for 10 min, respectively. The specificity test indicated that in addition to the positive test results for S. eriocheiris, the other five DNA templates tested negative, demonstrating strong specificity. The results of the sensitivity test revealed that the detection limits of RPA, RPA‐EXO and RPA‐LFD were 5.77 × 10−2, 5.77 × 10−4 and 1.52 × 10−6 ng/μL, respectively, indicating high sensitivity. This study established three methods for the detection of S. eriocheiris that are characterised by a constant temperature, rapid response, high sensitivity and strong specificity. [ABSTRACT FROM AUTHOR]