Air pollution in urban environments, particularly from fine particulate matter (PM_2.5), poses significant health risks. Addressing this issue, the current study developed a Random Forest (RF) model to estimate hourly PM_2.5 concentrations in Ankara, Türkiye. Utilizing ERA5 reanalysis data, the model incorporated various meteorological and environmental variables. Over the period 2020–2021, the model’s performance was validated against data from eleven air quality monitoring stations, demonstrating a robust coefficient of determination (R²) of 0.73, signifying its strong predictive capability. Low root mean squared error (RMSE) and mean absolute error (MAE) values further affirmed the model’s precision. Seasonal and temporal analysis revealed the model’s adaptability, with autumn showing the highest accuracy (R² = 0.82) and summer the least (R² = 0.51), suggesting seasonal variability in predictive performance. Hourly evaluations indicated the model’s highest accuracy at 23:00 (R² = 0.93), reflecting a solid alignment with observed data during nocturnal hours. On a monthly scale, November’s predictions were the most precise (R² = 0.82), while May presented challenges in accuracy (R² = 0.49). These seasonal and monthly fluctuations underscore the complex interplay of atmospheric dynamics affecting PM_2.5 dispersion. By integrating key determinants such as ambient air temperature, surface pressure, total column water vapor, boundary layer height, forecast albedo, and leaf area index, this study enhances the understanding of air pollution patterns in urban settings. The RF model’s comprehensive evaluation across time scales offers valuable insights for policymakers and environmental health practitioners, supporting evidence-based strategies for air quality management.

中文翻译：

---

大都市 PM2.5 估算的数据驱动方法：基于 ERA5 再分析数据的随机森林建模

城市环境中的空气污染，特别是细颗粒物 (PM _2.5 ) 造成的严重健康风险。为了解决这个问题，当前的研究开发了随机森林 (RF) 模型来估算土耳其安卡拉每小时的 PM _2.5浓度。该模型利用 ERA5 再分析数据，纳入了各种气象和环境变量。 2020-2021年期间，该模型的性能根据11个空气质量监测站的数据进行了验证，显示出稳健的决定系数（R ²）为0.73，表明其强大的预测能力。较低的均方根误差 (RMSE) 和平均绝对误差 (MAE) 值进一步证实了模型的精度。季节和时间分析揭示了该模型的适应性，其中秋季的准确度最高（R ² = 0.82），夏季的准确度最低（R ² = 0.51），这表明预测性能存在季节性变化。每小时评估表明该模型在 23:00 时的准确度最高（R ² = 0.93），反映出与夜间观测数据的紧密结合。从月度角度来看，11 月份的预测最为准确（R ² = 0.82），而 5 月份的预测在准确性方面面临挑战（R ² = 0.49）。这些季节性和每月的波动凸显了影响 PM _2.5扩散的大气动态的复杂相互作用。通过整合环境空气温度、表面压力、总水汽柱、边界层高度、预测反照率和叶面积指数等关键决定因素，这项研究增强了对城市环境中空气污染模式的理解。 RF 模型跨时间尺度的综合评估为政策制定者和环境健康从业者提供了宝贵的见解，支持基于证据的空气质量管理策略。