PM₁₀(空气动力学直径小于10微米的颗粒物)因其对人体健康的影响一直受到科学界的关注。预测PM₁₀浓度对早期预防措施至关重要，特别是在哥打京那巴鲁等城市。时间数据聚类可以通过对时间范围内的数据进行分组来提高预测模型的准确性。然而，在哥打京那巴鲁，时间数据聚类的必要性尚未得到研究。目标。本研究比较了气象因子和污染物因子对聚类和非聚类数据PM₁₀变化的显著性。方法。这项研究集中在沙巴州的哥打京那巴鲁。本文利用环境部2003 - 2012年气象因子(Ws、Wd、Hum、Temp)和污染物因子(CO₂、NO₂、O₃、SO₂、PM₁₀)的数据进行研究。在用季风聚类对缺失数据进行聚类之前，先用最近邻法对缺失数据进行估算。 Unclustered and clustered datasets are analysed using principal component analysis (PCA) to check significance of factors contributing to PM₁₀ concentration. FINDINGS. PCA results show that temporal clustering does not have noticeable effect on the variation of PM₁₀ concentration. For all datasets, humidity and x-component wind speed have highest factor loading on PC₁ and PC₂ respectively. Further statistical analysis by 2-D regression shows that humidity (ρ = -0.60 ± 0.20) and temperature (ρ = 0.63 ± 0.11) have moderate to strong correlation towards PM₁₀ concentration. This may be due to high humidity level and strong negative correlation between temperature and humidity (ρ = -0.91 ± 0.03). In contrast, both x- and y-component wind speed generally show weak correlation towards PM₁₀, with ρ value of 0.09 ± 0.14 and 0.24 ± 0.18 respectively probably because of varying direction of particle dispersion. Fourier analysis further confirms this result by showing that human activity contributes major effect to variation of PM₁₀ concentration.