Health effects attributed to outdoor fine particulate matter (PM_2.5) rank it among the risk factors with the highest health burdens in the world, annually accounting for over 3.2 million premature deaths and over 76 million lost disability-adjusted life years. Existing PM_2.5 monitoring infrastructure cannot, however, be used to resolve variations in ambient PM_2.5 concentrations with adequate spatial and temporal density, or with adequate coverage of human time-activity patterns, such that the needs of modern exposure science and control can be met. Small, inexpensive, and portable devices, relying on newly available off-the-shelf sensors, may facilitate the creation of PM_2.5 datasets with improved resolution and coverage, especially if many such devices can be deployed concurrently with low system cost.

Datasets generated with such technology could be used to overcome many important problems associated with exposure misclassification in air pollution epidemiology. Chapter 2 presents an epidemiological study of PM_2.5 that used data from ambient monitoring stations in the Los Angeles basin to observe a decrease of 6.1 g (95% CI: 3.5, 8.7) in population mean birthweight following in utero exposure to the Southern California wildfires of 2003, but was otherwise limited by the sparsity of the empirical basis for exposure assessment. Chapter 3 demonstrates technical potential for remedying PM_2.5 monitoring deficiencies, beginning with the generation of low-cost yet useful estimates of hourly and daily PM_2.5 concentrations at a regulatory monitoring site. The context (an urban neighborhood proximate to a major goods-movement corridor) and the method (an off-the-shelf sensor costing approximately USD $10, combined with other low-cost, open-source, readily available hardware) were selected to have special significance among researchers and practitioners affiliated with contemporary communities of practice in public health and citizen science. As operationalized by correlation with 1h data from a Federal Equivalent Method (FEM) β-attenuation data, prototype instruments performed as well as commercially available equipment costing considerably more, and as well as another reference instrument under similar conditions at the same timescale (R² = 0.6). Correlations were stronger when 24 h integrating times were used instead (R² = 0.72).

Chapter 4 replicates and extends the results of Chapter 3, showing that similar calibrations may be reasonably exchangeable between near-roadway and background monitoring sites. Chapter 4 also employs triplicate sensors to obtain data consistent with near-field (< 50 m) observations of plumes from a major highway (I-880). At 1 minute timescales, maximum PM_2.5 concentrations on the order of 100 μg m^-3 to 200 μg m^-3 were observed, commensurate with the magnitude of plumes from wildfires on longer timescales, as well as the magnitude of plumes that might be expected near other major highways on the same timescale. Finally, Chapter 4 quantifies variance among calibration parameters for a large sample of the sensors, as well as the error associated with the remote transfer of calibrations between two sufficiently large sets (± 10 % for n = 12). These findings suggest that datasets generated with similar sensors could also improve upstream scientific understandings of fluxes resulting from indoor and outdoor emissions, atmospheric transformations, and transport, and may also facilitate timely and empirical verification of interventions to reduce emissions and exposures, in many important contexts (e.g., the provision of improved cookstoves; congestion pricing; mitigation policies attached to infill development; etc.). They also demonstrate that calibrations against continuous reference monitoring equipment could be remotely transferred, within practical tolerances, to reasonably sized and adequately resourced participatory monitoring campaigns, with minimal risk of disruption to existing monitoring infrastructure (i.e., established monitoring sites). Given a collaborator with a short window of access to a reference monitoring site, this would overcome a nominally important barrier associated with non-gravimetric, in-situ calibration of continuous PM_2.5 monitors. Progressive and disruptive prospects linked to a proliferation of comparable sensing technologies based on commodity hardware are discussed in Chapter 5.

Background: In late October 2003, a series of wildfires exposed urban populations in Southern California to elevated levels of air pollution over several weeks. Previous research suggests that short-term hospital admissions for respiratory outcomes increased specifically as a result of these fires.

Objective: We assessed the impact of a wildfire event during pregnancy on birth weight among term infants.

Methods: Using records for singleton term births delivered to mothers residing in California’s South Coast Air Basin (SoCAB) during 2001–2005 (n = 886, 034), we compared birth weights from pregnancies that took place entirely before or after the wildfire event (n = 747, 590) with those where wildfires occurred during the first (n = 60, 270), second (n = 39, 435), or third (n = 38, 739) trimester. The trimester-specific effects of wildfire exposure were estimated using a fixed-effects regression model with several maternal characteristics included as covariates.

Results: Compared with pregnancies before and after the wildfires, mean birth weight was estimated to be 7.0 g lower [95% confidence interval (CI): –11.8, –2.2] when the wildfire occurred during the third trimester, 9.7 g lower when it occurred during the second trimester (95% CI: –14.5, –4.8), and 3.3 g lower when it occurred during the first trimester (95% CI: –7.2, 0.6).