(1) Fine particulate matter (PM_2.5) seriously affects the respiratory tract health of both animals and humans. Growing evidence indicates that the pulmonary microbiota is involved in the development of respiratory tract health; however, there is still much that is unknown about the specific changes of pulmonary microbiota caused by PM_2.5 in broilers. (2) In this experiment, a total of 48 broilers were randomly divided into a control group and PM-exposure group. The experiment lasted for 21 days. Microbiota, inflammation biomarkers, and histological markers in the lungs were determined. (3) On the last day of the experiment, PM significantly disrupted the structure of lung tissue and induced chronic pulmonary inflammation by increasing IL-6, TNFα, and IFNγ expression and decreasing IL-10 expression. PM exposure significantly altered the α and β diversity of pulmonary microbiota. At the phylum level, PM exposure significantly decreased the Firmicutes abundance and increased the abundance of Actinobacteria and Proteobacteria. At the genus level, PM exposure significantly increased the abundance of Rhodococcus, Achromobacter, Pseudomonas, and Ochrobactrum. We also observed positive associations of the above altered genera with lung TNFα and IFNγ expression. (4) The results suggest that PM perturbs the pulmonary microbiota and induces chronic inflammation, and the pulmonary microbiota possibly contributes to the development of lung inflammation.

Inlet design assumes that supply air from side-wall inlets mixes with house air over several metres, but whether this holds in a densely caged multi-tier house, where cages and birds obstruct the jet, is untested. The bird-zone environment of a commercial stacked-cage house was measured with 24 in-cage sensors at three distances and two tiers over ten winter days. Three of four groups showed no resolvable cooling gradient at 1.5 m; only one north-side group cooled with distance, strengthening with ventilation (0.30 K per percent, R² = 0.39), while another showed the opposite sign. Every fitted decay coefficient ($\alpha d_{\mathrm{h}}=0$ to $0.004$) lay below the free-jet design range ($0.01$–$0.03$), by five or more times over the window. Vertical stratification of $0.78$ K dominated (side × tier $p<0.001$): the upper tier was up to 2.4 K warmer on the north side, while the south-side tiers differed by under 0.5 K. Shading nets lowered north-side inlet temperature by $0.80$ K but warmed the south side. The practical consequence is that the thermal environment is organised by tier and side rather than by near-inlet distance, so monitoring, control and welfare-oriented assessment should target tiers and sides.

Accurate indoor-temperature forecasting underpins feed-forward control in broiler production, yet the horizons at which data-driven models add value are unclear. Two questions are addressed: at which horizons learned models measurably beat persistence, and what preview a feed-forward controller needs. Thirty-five days of data at 15 min resolution from a commercial stacked-cage broiler house (78,000 birds; outdoor temperature $-19$ to 16 °C) are used to benchmark 18 single-step and 10 multi-horizon models, probabilistic forecasts, and control simulation. A single time constant, $\tau =25.7$ h, organises the results: at 15 min, all learned models collapse to the persistence baseline (RMSE ≈ 0.105 K), because only 1% of the response relaxes per step; significant gains appear only at ≈$1\tau$ (LSTM $-25$% at 24 h, $-30$% at 48 h; FDR q ≤ 0.031). In control simulation, 4–8 h of MPC preview held $\sigma$ = 0.17 K with zero violations (0.74 K for 1 h control), while longer horizons added no benefit and were less robust. Quantile intervals calibrated in-sample (PICP ≈ 0.90) under-covered under distribution shift (0.61–0.72); $\tau$-anchored bands stayed calibrated (0.92–0.97). Design rules: evaluate forecasting models at horizons of at least ≈$1\tau$ and set control preview to ≈0.7–1.5${\tau }_f$, re-estimated per house and season.