The climate crisis is driving the urgent need to develop negative emission technologies, such as adsorption-based direct air capture (DAC), to combat global warming. Although DAC holds promise, it remains expensive and requires further technology innovation, design optimisation, and development of supply chains to scale up effectively and have a meaningful impact on climate. The performance of DAC is influenced by both local ambient conditions and the selection of sorbents. However, previous research typically evaluated DAC performance under constant ambient conditions and considered only a single sorbent per case study. This approach may result in an incomplete picture of DAC performance and suboptimal decision-making. Additionally, current DAC optimisation can be computationally expensive, making comprehensive global analysis impractical. Therefore, this study presents a computationally efficient, simplified, time-dependent, zero-dimensional (0-D) DAC model that accounts for multiple sorbents and hourly changing ambient conditions. The model is used to identify a sorbent that maximises net carbon removal by optimising for different geographical case studies to assess the impact of local, varying ambient conditions. The results demonstrate that DAC modelling can be simplified from a one-dimensional model to a 0-D model, thereby reducing computational demands. Additionally, beyond their absolute values, diurnal and seasonal variations in ambient temperature and humidity have a strong impact on sorbent performance. Key performance indicators, such as the net carbon removal rate, vary by up to 400% depending on the sorbent used or daily and seasonal variations in ambient conditions. Consequently, to improve DAC performance, sorbents should be selected based on ambient conditions. Finally, this study aims to advance the understanding of DAC and its role in mitigating climate change by providing general guidelines for DAC sorbent selection.