High-resolution urban air pollution mapping is essential for understanding the complex spatial, temporal, and chemical processes that influence population exposure, health, and environmental justice. This review highlights the strengths and limitations of two key in situ measurement methods: mobile monitoring and dense sensor networks. Mobile monitoring provides fine spatial resolution and can measure multiple pollutants, while dense sensor networks offer high temporal resolution and spatial coverage. Together, these methods can reveal spatial and temporal patterns of air pollution, informing effective interventions. Urban air pollution varies significantly in space, with fine-scale gradients influenced by primary emissions and physicochemical transformations. These variations have important implications for science and society, affecting domains such as atmospheric chemistry, exposure assessment, and environmental epidemiology. Urban planning, public health, and environmental policy aim to address the impacts of spatially varying and often inequitably distributed air pollution exposures. Mobile monitoring and dense sensor networks are emerging observational paradigms that provide high-resolution data. Mobile monitoring is effective for capturing spatial patterns at high resolution but lacks comprehensive temporal data. Dense sensor networks offer excellent temporal resolution and spatial coverage but are limited in the number of pollutants they can measure. Combining these methods can provide a more comprehensive understanding of air pollution dynamics. High-resolution air pollution mapping has been applied in various cities, including Toronto, Montreal, West Oakland, Pittsburgh, and London, to assess exposure, disparities, and health impacts. These studies have shown the value of integrating mobile monitoring with fixed-site data and statistical models to develop accurate exposure surfaces. For example, in West Oakland, mobile monitoring and fixed-site sensors were used to measure BC, NO, NO₂, and UFP exposures for over 450,000 people. In London, mobile monitoring and inverse models were used to evaluate the impact of the Ultra Low Emission Zone (ULEZ) policy. Key challenges include the need for robust calibration of low-cost sensors, the importance of spatial and temporal sampling design, and the integration of data from different sources. High-resolution mapping is particularly valuable in low- and middle-income countries (LMICs), where traditional regulatory monitoring is costly and sparse. New methods offer a "leapfrog" opportunity to use advanced measurement and management practices. The review emphasizes the importance of combining different observational and modeling approaches to better understand the sources, processes, and impacts of urban air pollution. Future research should focus on improving spatiotemporal resolution, integrating observations with mechanistic models, and addressing disparities in exposure. High-resolution air pollution mapping plays a crucial role in informing policies and improving public health outcomes.High-resolution urban air pollution mapping is essential for understanding the complex spatial, temporal, and chemical processes that influence population exposure, health, and environmental justice. This review highlights the strengths and limitations of two key in situ measurement methods: mobile monitoring and dense sensor networks. Mobile monitoring provides fine spatial resolution and can measure multiple pollutants, while dense sensor networks offer high temporal resolution and spatial coverage. Together, these methods can reveal spatial and temporal patterns of air pollution, informing effective interventions. Urban air pollution varies significantly in space, with fine-scale gradients influenced by primary emissions and physicochemical transformations. These variations have important implications for science and society, affecting domains such as atmospheric chemistry, exposure assessment, and environmental epidemiology. Urban planning, public health, and environmental policy aim to address the impacts of spatially varying and often inequitably distributed air pollution exposures. Mobile monitoring and dense sensor networks are emerging observational paradigms that provide high-resolution data. Mobile monitoring is effective for capturing spatial patterns at high resolution but lacks comprehensive temporal data. Dense sensor networks offer excellent temporal resolution and spatial coverage but are limited in the number of pollutants they can measure. Combining these methods can provide a more comprehensive understanding of air pollution dynamics. High-resolution air pollution mapping has been applied in various cities, including Toronto, Montreal, West Oakland, Pittsburgh, and London, to assess exposure, disparities, and health impacts. These studies have shown the value of integrating mobile monitoring with fixed-site data and statistical models to develop accurate exposure surfaces. For example, in West Oakland, mobile monitoring and fixed-site sensors were used to measure BC, NO, NO₂, and UFP exposures for over 450,000 people. In London, mobile monitoring and inverse models were used to evaluate the impact of the Ultra Low Emission Zone (ULEZ) policy. Key challenges include the need for robust calibration of low-cost sensors, the importance of spatial and temporal sampling design, and the integration of data from different sources. High-resolution mapping is particularly valuable in low- and middle-income countries (LMICs), where traditional regulatory monitoring is costly and sparse. New methods offer a "leapfrog" opportunity to use advanced measurement and management practices. The review emphasizes the importance of combining different observational and modeling approaches to better understand the sources, processes, and impacts of urban air pollution. Future research should focus on improving spatiotemporal resolution, integrating observations with mechanistic models, and addressing disparities in exposure. High-resolution air pollution mapping plays a crucial role in informing policies and improving public health outcomes.