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Sun photometer measurements taken in urban Jiaozuo in central China from July 2016 to February 2018 were used to investigate the aerosol optical and microphysical properties, including the climatological variation in properties, the types of aerosols and the properties during haze and dust events. The mean annual aerosol optical depth at 440nm (AOD(440nm)) and the Angstrom exponent (AE (440-870nm)) were 0.84 +/- 0.55 and 1.12 +/- 0.17, respectively. The highest AOD occurred in summer, which may be associated with the hygroscopic growth of aerosols during enhanced relative humidity. Accordingly, a high volume of fine-mode particles was observed during this season. In addition, carbonaceous aerosols from biomass burning increased the volume of fine particles in June and September, but coarse-mode particles (mainly dust) dominated during spring, especially in May, with a maximum volume of 0.16 mu m(3) mu m(-2) . At 440 nm, the mean seasonal single scattering albedo (SSA(440 nm)) was lower in spring (0.87 +/- 0.05) and higher in summer (0.95 +/- 0.04), whereas the absorption aerosol optical depth (AAOD(440 nm)) displayed the opposite pattern, being higher in spring (0.079 +/- 0.019) and lower in summer (0.045 +/- 0.021). The increased absorptivity in spring and the strong scattering ability in summer of aerosols in this area may be related to particles originating from different sources. Using the method of classification based on the fine-mode fraction (FMF), SSA and AE, absorbing aerosols, both fine and mixed, were identified as the predominant type of particulate matter. The site was influenced not only by anthropogenic aerosols from local emissions and the surrounding regions but also by dust from northwestern China The mean AOD and AE were 1.66 and 1.38 on hazy days, while a slightly low AOD of 0.95 and an extremely low AE of 0.18 were exhibited on dusty days. This study provides a comprehensive understanding of aerosol properties in this area, and the results will help to optimize the satellite aerosol inversion algorithm and promote regional climate change research.