Determining an Optimal Combination of Meteorological Factors to Reduce the Intensity of Atmospheric Pollution During Prescribed Straw Burning

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM 2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM 2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM 2.5 concentration. The RSM relevance prediction model (MET-PM 2.5 ) established a correlation equation between meteorological factors and PM 2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM 2.5 value remained below 75 μg/m 3 when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities.