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High delicate particulate matter (PM 2.5) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM 2.5 plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM 2.5 forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM 2.5 concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM 2.5 alleviation strategies. [Display omitted] • A novel seasonal grey model is used to predict PM2.5 concentration in the YRD region. • The Cultural Algorithm is used for effectively identifying the coefficients. • Comparative studies exhibit the robustness and reliability of the new model. • The future PM2.5 concentrations are forecasted by the new model from 2020 to 2022. • Some corresponding policy strategies to mitigate PM2.5 pollution are put forward. [ABSTRACT FROM AUTHOR]