Land-use regression (LUR) models have been used to estimate particle mass concentration (PMC), but few studies apply it to predict particle number concentration (PNC) at different sizes. This study aimed to determine both PMC and PNC throughout one year to establish predictive models in Taichung, Taiwan. The annual averages of PM₁₀, PM_2.5, and PM₁ were 71 ± 46 μg/m³, 44 ± 35 μg/m³, and 32 ± 28 μg/m³, respectively. The PNC at size ranges of <0.5 μm, 0.5–1 μm, 1–2.5 μm, 2.5–10 μm, and ≥10 μm were 715098 ± 664879 counts/L, 29053 ± 30615 counts/L, 1009 ± 659 counts/L, 647 ± 347 counts/L, and 3 ± 3 counts/L, respectively. The model-explained variance (R²) values of PM₁₀, PM_2.5, and PM₁ were 0.42, 0.53, and 0.51, respectively. The magnitude of the R² values ranged from 0.31 to 0.50 for the PNC with the highest R² between 0.5 and 1 μm. The differences between the model R² and the leave-one-out cross-validation R² ranged from 4% to 8% for PMC and from 3% to 10% for PNC. This study developed LUR models with moderate performance to estimate PMC and PNC at different sizes in an Asian metropolis. The built LUR models may be improved by combining with other open data to increase the predictive capacity.