While previous studies have emphasised the role of individual factors in understanding multimorbidity disparities, few have investigated contextual factors such as air pollution (AP). We first use cross-sectional latent class analysis (LCA) to assess the associations between PM2.5 exposure and multimorbidity disease clusters, and then estimate the associations between PM2.5 exposure and the development of multimorbidity longitudinally using growth curve modelling (GCM) among adults aged 45-85 in China. The results of LCA modelling suggest four latent classes representing three multimorbidity patterns (respiratory, musculoskeletal, cardio-metabolic) and one healthy pattern. The analysis shows that a 1 μg/m3 increase in cumulative exposure to PM2.5 is associated with a higher likelihood of belonging to respiratory, musculoskeletal or cardio-metabolic clusters: 2.4% (95% CI: 1.02, 1.03), 1.5% (95% CI: 1.01, 1.02) and 3.3% (95% CI: 1.03, 1.04), respectively. The GCM models show that there is a u-shaped association between PM2.5 exposure and multimorbidity, indicating that both lower and higher PM2.5 exposure is associated with increased multimorbidity levels. Higher multimorbidity in areas of low AP is explained by clustering of musculoskeletal diseases, whereas higher AP is associated with cardio-metabolic disease clusters. The study shows how multimorbidity clusters vary contextually and that PM2.5 exposure is more detrimental to health among older adults.