Nutrient content fluctuation in dairy production slurry is highly influenced by the various treatment processes applied in the Chinese dairy sector. The dynamic measurement of these contents is critical for the practical and efficient field application of slurry subjected to various processes. In the study, a total of 715 slurry samples were collected from 24 intensive dairy farms in Tianjin subjected to three typical treatment processes. Descriptive statistical analysis, principal component analysis, and partial least square regression were used to investigate the variation in total nitrogen (TN) and total phosphorus (TP) contents, spectral characteristics, and the performance of the prediction model of the slurry under the processes, respectively. Results revealed significant differences in both TN and TP contents along with the spectra for the slurry subjected to different treatment processes. All the inter-process models showed poor performance, and the results were worse compared to the intra-process models. Among the intra-process models for TN, the optimally performing models were the Pac fusion model (R²_pred = 0.82; RPD = 2.38) and the single model Pa (R²_pred = 0.83; RPD = 2.31). Among the intra-process models for TP, the optimum results were seen for Pab (R²_pred = 0.77; RPD = 2.07) and Pa (R²_pred = 0.79; RPD = 2.30). Taking different treatment processes into consideration is essential to establish flexible models that can be adaptive for diversified scenarios. This would be helpful to improve the tracking monitor measures, efficiently guide the land application of slurry, and support the sustainable development of animal farming and environmental conversation.

Particulate matter (PM_2.5) in pig houses and the microorganisms in PM_2.5 restrict the sustainable development of the pig industry and have a negative influence on environmental sustainability. This study aimed to investigate the morphological characteristics and diel microbial composition of PM_2.5 in fattening pig sheds and explore how changes in the diel microbial composition of PM_2.5 correlate with environmental factors and sources. To this end, environmental data from a fattening pig house were monitored, and PM_2.5, feed, and faecal particles were examined using electron microscopy. Additionally, the bacterial and fungal assemblages contained in PM_2.5 were analysed using 16S and 18S rRNA gene sequencing, respectively. The results showed that NH₃, CO₂, temperature, and relative humidity were significantly higher at night than during the day. PM_2.5 particles from the fattening pig house exhibited different morphologies such as spherical, flocculent, and chain structures. The microbial diversity and bacterial assemblage showed significant variations, which were related to diel environmental factors in the fattening house. In addition, faeces may be the main source of airborne bacteria and feed may be the main source of airborne fungi in fattening houses. These findings provide a scientific basis for exploring the potential risks of the morphological characteristics of PM_2.5 and its microbial composition to human and animal health. Additionally, they contribute to the sustainable development of the pig industry and the protection of the environment.