This Review highlights insights from two popular in situ measurement methods—mobile monitoring and dense sensor networks—that have distinct but complementary strengths in characterizing the dynamics and impacts of urban air quality. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. The Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts. Urban air pollution varies sharply in space, with fine-scale gradients arising from primary emissions and physicochemical transformations. These variations have important implications for science and society. Characterization of fine-scale air pollution dynamics is important for domains such as atmospheric chemistry, exposure assessment, and environmental epidemiology. In turn, urban planning, public health, and environmental policy seek to address the impacts of spatially varying and often inequitably distributed urban air pollution exposures. The Review explores the emerging field of high-resolution urban air pollution mapping. Over the past 10 to 15 years, new techniques have been developed to characterize fine-scale pollution gradients. The Review focuses on in situ observational methods and attempts to summarize the state of the art, synthesize new insights, and identify directions for future work. Most studies use some combination of mobile monitoring and dense stationary sensor networks, often coupled with sophisticated data processing and statistical analysis. These complementary in situ measurements can validate, challenge, and augment other techniques. Mobile monitoring involves using diverse vehicle platforms to measure pollutants at high spatial resolution. It can efficiently represent seasonal or annual patterns across monitored road segments at very high spatial resolution. However, it generally cannot resolve temporal air quality patterns comprehensively. Stationary sensor networks, by contrast, typically have excellent temporal resolution and good spatial coverage. Lower-cost sensors are often used to defray the costs of running many sites at once. Dense multipollutant networks are less common due to limited availability of robust sensors. Dense stationary sensor networks provide continuous temporal information and moderate to high spatial resolution. They are increasingly used to supplement sparser regulatory monitoring systems and support community-based monitoring initiatives. LCSs are used to detect pollutants at high time resolution, but they face challenges with data quality, sensor degradation, and cross-sensitivity. Hybrid approaches combining mobile and stationary monitoring can provide a more comprehensive representation of spatiotemporal variability. These approaches can help in understanding emissions, transport, and transformations of urban air pollution. High-resolution air pollution mapping has multiple aims, including understanding key processes that govern urban air quality and quantifying patterns relevant for population exposures, disparities, and health effects. High-resolution air pollution mapping methods offer the potential to help fill critical data gaps in LMICs. They can also help in assessing air pollution disparities and health impacts. The Review concludes that increasing dialogue and collaboration among different communities is essential for advancing the fieldThis Review highlights insights from two popular in situ measurement methods—mobile monitoring and dense sensor networks—that have distinct but complementary strengths in characterizing the dynamics and impacts of urban air quality. Mobile monitoring can measure many pollutants at fine spatial scales, thereby informing about processes and control strategies. Sensor networks excel at providing temporal resolution at many locations. Increasingly sophisticated studies leveraging both methods can vividly identify spatial and temporal patterns that affect exposures and disparities and offer mechanistic insight toward effective interventions. The Review summarizes the strengths and limitations of these methods and discusses their implications for understanding fine-scale processes and impacts. Urban air pollution varies sharply in space, with fine-scale gradients arising from primary emissions and physicochemical transformations. These variations have important implications for science and society. Characterization of fine-scale air pollution dynamics is important for domains such as atmospheric chemistry, exposure assessment, and environmental epidemiology. In turn, urban planning, public health, and environmental policy seek to address the impacts of spatially varying and often inequitably distributed urban air pollution exposures. The Review explores the emerging field of high-resolution urban air pollution mapping. Over the past 10 to 15 years, new techniques have been developed to characterize fine-scale pollution gradients. The Review focuses on in situ observational methods and attempts to summarize the state of the art, synthesize new insights, and identify directions for future work. Most studies use some combination of mobile monitoring and dense stationary sensor networks, often coupled with sophisticated data processing and statistical analysis. These complementary in situ measurements can validate, challenge, and augment other techniques. Mobile monitoring involves using diverse vehicle platforms to measure pollutants at high spatial resolution. It can efficiently represent seasonal or annual patterns across monitored road segments at very high spatial resolution. However, it generally cannot resolve temporal air quality patterns comprehensively. Stationary sensor networks, by contrast, typically have excellent temporal resolution and good spatial coverage. Lower-cost sensors are often used to defray the costs of running many sites at once. Dense multipollutant networks are less common due to limited availability of robust sensors. Dense stationary sensor networks provide continuous temporal information and moderate to high spatial resolution. They are increasingly used to supplement sparser regulatory monitoring systems and support community-based monitoring initiatives. LCSs are used to detect pollutants at high time resolution, but they face challenges with data quality, sensor degradation, and cross-sensitivity. Hybrid approaches combining mobile and stationary monitoring can provide a more comprehensive representation of spatiotemporal variability. These approaches can help in understanding emissions, transport, and transformations of urban air pollution. High-resolution air pollution mapping has multiple aims, including understanding key processes that govern urban air quality and quantifying patterns relevant for population exposures, disparities, and health effects. High-resolution air pollution mapping methods offer the potential to help fill critical data gaps in LMICs. They can also help in assessing air pollution disparities and health impacts. The Review concludes that increasing dialogue and collaboration among different communities is essential for advancing the field