More commonly air pollution observations are obtained with unstructured monitoring, where either a research grade monitor or low-cost sensor is irregularly relocated throughout the study area. This unstructured data is commonly observed in community science programs. Often the objective is to apply these data to estimate a long-term concentration, which is achieved using a temporal adjustment to correct for the irregular sampling. Temporal adjustments leverage information from a stationary continuous reference monitor, in combination with short-term monitoring data, to estimate long-term pollutant concentrations. We assess the performance of temporal adjustment approaches to predict long-term pollutant concentrations using data representing unstructured sampling. A series of monitoring campaigns are simulated from air pollution data obtained from regulatory monitoring networks in four different cities (Paris, France; Taipei, Taiwan; Toronto, Canada; and Vancouver, Canada) for eight different pollutants (CO, NO, NOx, NO2, O3, PM10, PM2.5, and SO2). These simulated campaigns have randomized monitoring locations and sampling times to simulate the irregular nature of crowd sourced or mobile monitoring data. The number of consecutive samples reported, and selection of the reference monitor used to adjust observations, are varied in this study. The accuracy of estimates is assessed by comparing the estimated long-term concentration to the observed long-term concentration from the complete regulatory monitoring dataset. This study found that a common temporal adjustment applied in research performed significantly worse than other adjustments including a Naïve Temporal Approach where no data adjustment occurred. Increasing the sample size improved the accuracy of estimates, which showed decreasing benefit with increased sample lengths. Lastly, controlling for land use conditions of the reference monitor did not consistently improve the long-term estimates, which suggests that land use pairing of mobile and reference monitors does not significantly influence the predictive power of temporal adjustment approaches. Temporal adjustments can reduce the error in long-term concentration estimates of air pollution using incomplete data, but this benefit cannot be assumed across all approaches, pollutants or sampling programs.