发表机构
North South University; University of Science and Technology; AIUB; Islamic University of Technology; University of Dhaka（北南大学; 科学技术大学; 伊斯兰阿齐兹大学; 伊斯兰技术大学; 达卡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究分析孟加拉国25年全国空气质量数据，识别出4种污染格局，发现PM2.5和PM10是AQI主要贡献因素，燃烧排放为管控核心。
AI 中文摘要
空气污染是孟加拉国最紧迫的环境问题之一，但鲜有研究在全国范围内开展长时间尺度的考察。为描述空气质量的趋势、季节性、地理格局及污染格局，本研究分析了25年（2000-2025年）的逐时空气质量数据，涵盖超319万条记录、8种污染物，以及从仅达卡1个监测站扩展后2022年起的103个城市。综合AQI序列无显著长期趋势，这是监测网络扩展导致的假象，而非污染实际趋于平稳，因为在网络扩展前，达卡自身的AQI持续上升。季节性特征显著且高度重要，AQI在1月达到峰值（162.26），7月降至最低（61.47），与旱季和季风季的变化一致。地理上，包括Teknaf和Bandarban在内的东南部沿海城市始终记录到最低的AQI水平，达卡仍是污染最严重的城市，且空气质量随距首都的距离增加而逐步改善。对城市级AQI轨迹的K-means聚类识别出4种不同的污染格局，范围从较清洁的沿海地区到快速恶化的达卡-纳辛迪集群，该集群年均AQI增幅为1.84。相关性分析显示，PM2.5和PM10是AQI变化的主要贡献因素，因此燃烧相关排放是空气质量管理和政策干预的主要目标。
英文摘要
Air pollution is one of Bangladesh's most pressing environmental problems, yet few studies have examined it at a national scale over a long time span. To describe trends, seasonality, geographic patterns, and pollution regimes, this study examines 25 years of hourly air quality data (2000-2025), covering over 3.19 million records, eight pollutants, and 103 cities monitored from 2022 onward following expansion from a single Dhaka station. The composite AQI series showed no significant long-term trend, an artifact of this network expansion rather than a real flattening of pollution, since Dhaka's own AQI rose steadily before the network grew. Seasonality was strong and highly significant, as AQI peaked in January (162.26) and fell to its lowest in July (61.47), tracking the dry and monsoon seasons. Geographically, southeastern coastal cities, including Teknaf and Bandarban, consistently recorded the lowest AQI levels. Dhaka remained the most polluted city, and air quality improved progressively with distance from the capital. K-means clustering of city-level AQI trajectories identified four distinct pollution regimes, ranging from cleaner coastal regions to a rapidly deteriorating Dhaka-Narsingdi cluster with an average increase of 1.84 AQI per year. Correlation analysis showed that PM2.5 and PM10 were the dominant contributors to AQI variation, and so combustion-related emissions are the primary target for air quality management and policy intervention.
