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Background: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations., Objectives: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California., Methods: We estimated mortality and morbidity attributable to nitrogen dioxide ( NO 2 ), black carbon (BC), and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter ( PM 2.5 )] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a ) mobile monitoring of NO 2 and BC; and b ) models predicting annual NO 2 , BC and PM 2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates., Results: Estimated pollution-attributable deaths per 100,000 people at the 100 -m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO 2 [ mean = 30 (95% CI: 9, 50)], BC [ mean = 2 (95% CI: 1, 2)], and PM 2.5 , [ mean = 49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell-level NO 2 -attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean = 64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean = 101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO 2 and PM 2.5 , with more spatial heterogeneity at the grid-cell-level [ NO 2 CBG mean = 41 deaths per 100,000 people (95% CI: 12, 68); NO 2 county mean = 38 (95% CI: 11, 64); PM 2.5 CBG mean = 59 (95% CI: 40, 77); and PM 2.5 county mean = 55 (95% CI: 37, 71)]., Discussion: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R 2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R 2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Background: Some studies have linked long-term exposure to traffic related air pollutants (TRAP) with adverse cardiovascular health outcomes; however, previous studies have not linked highly variable concentrations of TRAP measured at street-level within neighborhoods to cardiovascular health outcomes., Methods: Long-term pollutant concentrations for nitrogen dioxide [NO 2 ], nitric oxide [NO], and black carbon [BC] were obtained by street-level mobile monitoring on 30 m road segments and linked to residential addresses of 41,869 adults living in Oakland during 2010 to 2015. We fit Cox proportional hazard models to estimate the relationship between air pollution exposures and time to first cardiovascular event. Secondary analyses examined effect modification by diabetes and age., Results: Long-term pollutant concentrations [mean, (standard deviation; SD)] for NO 2 , NO and BC were 9.9 ppb (SD 3.8), 4.9 ppb (SD 3.8), and 0.36 μg/m3 (0.17) respectively. A one SD increase in NO 2 , NO and BC, was associated with a change in risk of a cardiovascular event of 3% (95% confidence interval [CI] -6% to 12%), 3% (95% CI -5% to 12%), and - 1% (95% CI -8% to 7%), respectively. Among the elderly (≥65 yrs), we found an increased risk of a cardiovascular event of 12% for NO 2 (95% CI: 2%, 24%), 12% for NO (95% CI: 3%, 22%), and 7% for BC (95% CI: -3%, 17%) per one SD increase. We found no effect modification by diabetes., Conclusions: Street-level differences in long-term exposure to TRAP were associated with higher risk of cardiovascular events among the elderly, indicating that within-neighborhood differences in TRAP are important to cardiovascular health. Associations among the general population were consistent with results found in previous studies, though not statistically significant.

This article is an intervention in the epistemologies and methodologies of urban studies. It seeks to understand and transform the ways in which the cities of the global South are studied and represented in urban research, and to some extent in popular discourse. As such, the article is primarily concerned with a formation of ideas - "subaltern urbanism" - which undertakes the theorization of the megacity and its subaltern spaces and subaltern classes. Of these, the ubiquitous ‘slum’ is the most prominent. Writing against apocalyptic and dystopian narratives of the slum, subaltern urbanism provides accounts of the slum as a terrain of habitation, livelihood, self-organization and politics. This is a vital and even radical challenge to dominant narratives of the megacity. However, this article is concerned with the limits of and alternatives to subaltern urbanism. It thus highlights emergent analytical strategies, utilizing theoretical categories that transcend the familiar metonyms of underdevelopment such as the megacity, the slum, mass politics and the habitus of the dispossessed. Instead, four categories are discussed — peripheries, urban informality, zones of exception and gray spaces. Informed by the urbanism of the global South, these categories break with ontological and topological understandings of subaltern subjects and subaltern spaces.