Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in PM2.5 chemical components remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal for chemical components. This paper introduces a novel hybrid nonlinear chemical DA system (NAQPMS-PDAF v2.0) to accurately interpret key chemical components (SO42-, NO3-, NH4+, OC, and EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handling and balancing stability and nonlinearity in chemical DA, which is achieved by incorporating the non-Gaussian distribution ensemble perturbation and hybrid localized Kalman-nonlinear ensemble transform filter with an adaptive forgetting factor for the first time. The dependence tests demonstrate that NAQPMS-PDAF v2.0 provides excellent DA results with a minimal ensemble size of 10, surpassing previous reports and v1.0. A 1-month DA experiment shows that the analysis field generated by NAQPMS-PDAF v2.0 is in good agreement with observations, especially in reducing the underestimation of NH4+ and NO3- and the overestimation of SO42-, OC, and EC. In particular, the Pearson correlation coefficient (CORR) values for NO3-, OC, and EC are above 0.96, and the R2 values are above 0.93. NAQPMS-PDAF v2.0 also demonstrates superior spatiotemporal interpretation, with most DA sites showing improvements of over 50 %-200 % in CORR and over 50 %-90 % in RMSE for the five chemical components. Compared to the poor performance in the global reanalysis dataset (CORR: 0.42-0.55, RMSE: 4.51-12.27 μg m-3) and NAQPMS-PDAF v1.0 (CORR: 0.35-0.98, RMSE: 2.46-15.50 μg m-3), NAQPMS-PDAF v2.0 has the highest CORR of 0.86-0.99 and the lowest RMSE of 0.14-3.18 μg m-3. The uncertainties in ensemble DA are also examined, further highlighting the potential of NAQPMS-PDAF v2.0 for advancing aerosol chemical component studies.