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The last several decades have seen steady and expansive growth both in the development and the application of low-cost sensors (LCS) in the field of air pollution research. They are now increasingly prevalent in air quality monitoring thanks to their affordability and adaptability across diverse environments. However, in the wider academic and monitoring communities, their deployment has largely focused on the expansion of spatial coverage and generating larger datasets, either to generate data where there previously was none, or to fill spatiotemporal gaps. In this work, we argue that LCS have high potential for use in the targeted assessment of policy interventions, and that this potential remains largely underexplored. Drawing on a number of recent studies, we demonstrate the value of LCS as an effective tool for evaluating the impacts of policy measures on urban air pollution.Utilizing these studies as an empirical basis, this work introduces a five-step rubric for guiding targeted policy assessments using LCS. These steps broadly are: 1. Identification, in which partnerships and policies are identified and established; 2. Planning, in which measurement and intervention timelines are aligned and campaigns designed; 3. Calibration, in which LCS are suitably calibrated using in-situ co-locations for the environments they are to be used in; 4. Analysis, in which LCS data is collected and analysed, with potential impacts of the policy intervention quantified; and 5. Dissemination, in which the results are published and presented in a timely manner to relevant stakeholders. These cyclical steps should be considered fluid and dynamic, as they are intended to align with policymaking timelines, which often diverge from research timelines.In addition, we discuss the strengths and limitations of LCS for use in targeted policy assessment, to clarify key criteria for deployment in this application. These strengths include their capacity for high spatiotemporal resolution, flexible deployment options (especially outdoor), and cost-effectiveness in shorter term campaigns. These attributes enable the detection of hyperlocal pollution patterns and emission events that are often missed by sparsely populated reference monitoring networks. Key limitations to this approach include sensor drift, inter-sensor variability, cross-sensitivities to other pollutants, and the need for rigorous calibration. These factors can constrain the data quality and limit the detection of the impact signal of the policy interventions in question. However, by properly quantifying uncertainties and accounting for e.g., meteorological variability, these limitations can be taken into account and relevant results can still be delivered to stakeholders.As such, this work argues for a shift in the research landscape surrounding LCS, and advocates for a shift away from indiscriminate large-scale sensor deployment and toward targeted assessment of individual policies at the local scale. We encourage its further uptake and remain optimistic that this approach can transform the evidence base for local policy decision-making, to create a step change in the tools and data used for mitigating air pollution and providing clean, healthy air for all.