Meredith Franklin

Predictors of intra-community variation in air quality

Air quality has emerged as a key determinant of important health outcomes in children and adults. This study aims to identify factors that influence local, within-community air quality, and to build a model for traffic-related air pollution (TRP).We utilized concentrations of NO2, NO, and total oxides of nitrogen (NOx), which were measured at 942 locations in 12 southern California communities. For each location, population density, elevation, land-use, and several indicators of traffic were calculated. A spatial random effects model was used to study the relationship of these predictors to each TRP.Variation in TRP was strongly correlated with traffic on nearby freeways and other major roads, and also with population density and elevation. After accounting for traffic, categories of land-use were not associated with the pollutants. Traffic had a larger relative impact in small urban (low regional pollution) communities than in large urban (high regional pollution) communities. For example, our best fitting model explained 70% of the variation in NOx in large urban areas and 76% in small urban areas. Compared with living at least 1,500 m from a freeway, living within 250 m of a freeway was associated with up to a 41% increase in TRP in a large urban area, and up to a 75% increase in small urban areas.Thus, traffic strongly affects local air quality in large and small urban areas, which has implications for exposure assessment and estimation of health risks.

The role of traffic noise on the association between air pollution and children's lung function

Abstract Although it has been shown that traffic‐related air pollution adversely affects childrens lung function, few studies have examined the influence of traffic noise on this association, despite both sharing a common source. Estimates of noise exposure (Ldn, dB), and freeway and non‐freeway emission concentrations of oxides of nitrogen (NOx, ppb) were spatially assigned to children in Southern California who were tested for forced vital capacity (FVC, n=1345), forced expiratory volume in 1 s, (FEV1, n=1332), and asthma. The associations between traffic‐related NOx and these outcomes, with and without adjustment for noise, were examined using mixed effects models. Adjustment for noise strengthened the association between NOx and reduced lung function. A 14.5 mL (95% CI −40.0, 11.0 mL) decrease in FVC per interquartile range (13.6 ppb) in freeway NOx was strengthened to a 34.6 mL decrease after including a non‐linear function of noise (95% CI −66.3, −2.78 mL). Similarly, a 6.54 mL decrease in FEV1 (95% CI −28.3, 15.3 mL) was strengthened to a 21.1 mL decrease (95% CI −47.6, 5.51) per interquartile range in freeway NOx. Our results indicate that where possible, noise should be included in epidemiological studies of the association between traffic‐related air pollution on lung function. Without taking noise into account, the detrimental effects of traffic‐related pollution may be underestimated. HighlightsThe joint effects of traffic noise and air pollution exposure on health are examined.Noise enhances the detrimental impact of air pollution on childrens lung function.Noise is an important exposure to include in studies of traffic‐related health outcomes.

Characterization of Subgrid-Scale Variability in Particulate Matter with Respect to Satellite Aerosol Observations

Recent use of satellite observations of aerosol optical depth (AOD) to characterize surface concentrations of particulate matter (PM) air pollution has proven extremely valuable in estimating exposures for health effects studies. While the spatial resolutions of satellite data provide far better coverage than existing fixed site surface monitoring stations, they are not able to capture atmospheric processes such as dilution of primary pollutants that vary at small spatial scales. As a result, small-scale variability due to highly localized sources such as traffic may be poorly represented, which in turn may lead to exposure measurement error in epidemiological analyses. Using a fixed spatial grid representing 4.4 km Multiangle Imaging SpectroRadiometer (MISR) aerosol observations, we examined the spatial variability in fine and coarse mode PM (PM2.5 and PM2.5–10 respectively) measured at ground monitors from a unique spatially-dense sampling campaign in Southern California. We found that while the variance in measured PM2.5 differed seasonally (warm 6.82 μg2/m6 and cool 24.5 μg2/m6) across the study region, the average subgrid (<4.4 km) variance did not (warm 2.03 μg2/m6 and cool 2.43 μg2/m6) and was significantly smaller. On the other hand, ground monitor PM2.5–10 concentrations showed large variance in warm (18.6 μg2/m6) and cool (20.6 μg2/m6) seasons, as well as seasonal differences in subgrid variance (warm 8.90 μg2/m6 and cool 3.28 μg2/m6). Geostatistical analysis of the semivariance as a function of distance indicated that variability in measured PM2.5 and PM2.5–10 concentrations was relatively constant for spatial scales of one to five kilometers, but there was evidence of small-scale (~500 m) variability in PM2.5–10 concentrations in the cool season. The lack of small-scale spatial variability in the warm season was likely due to large photochemical contributions to regional PM2.5, and greater regional contributions to PM2.5–10 from windblown dust. In contrast, in the cool season there tends to be greater localized concentrations from primary traffic sources due to stronger nocturnal inversions and delayed morning winds reducing dilution that contribute to greater spatial heterogeneity. Overall, these results suggest that regional contributions tend to dominate PM2.5, and spatial resolutions of satellite observations including the 4.4 km MISR and 3 km MODIS aerosol products aptly capture relevant spatial variability. Coarse PM2.5–10 can have seasonally dependent localized contributions, leading to small-scale variability below current satellite aerosol product resolutions.

OP III – 3 Using satellite observations to estimate exposure to flaring: implications for future studies of the health impacts of unconventional oil and gas operations

Background/aim Unconventional oil and gas (UOG) operations may increase exposure to hazardous air pollutants and several studies suggest they can harm the health of nearby residents. However, research is hampered by a lack of data on pollutant emissions from drilling sites and few studies have examined the potential health impacts of flaring, the common practice of combusting petroleum waste products on site. Methods We utilise a novel remote sensing data source to estimate exposure to flaring among residents of the Eagle Ford Shale region of Texas, U.S. This rural region has experienced a roughly tenfold increase in oil and gas production since 2010 and is the highest oil-producing and fourth highest gas-producing region in the U.S. We investigate the potential of the VIIRS Nightfire product – which includes satellite observations of infrared radiation at night from combustion sources – to characterise exposure to flaring and compare estimates of exposure to UOG operations derived from VIIRS and those derived from more traditional data sources (permit and self-reported production data) that have been used in previous epidemiological studies. Results Nearly 8 00 000 people live less than 5 km from one or more of the 22 000 active, permitted UOG wells in the study region. Nighttime infrared observations from VIIRS confirm reports of extensive flaring in close proximity to homes. We construct VIIRS-derived indices to characterise exposure to flaring based on residential proximity to flaring locations, flaring frequency and duration, temperature of combustion, and areal extent. We discuss the strengths and limitations of these measures for estimating air pollutant emissions, and the implications of this exposure assessment method for future epidemiological research on the health impacts of UOG operations. Conclusion While previous studies have relied on self-reported information on the location, timing, and productivity of oil and gas extraction activities, careful processing of VIIRS observations can provide novel, objective estimates of exposure to flaring that are likely better capture exposure to air pollutants resulting from UOG operations.

Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification

BackgroundLog-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.MethodsIn this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response).ResultsPoint estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased.ConclusionUnder model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.

Long-Term Ambient Temperature and Externalizing Behaviors in Adolescents

The climate-violence relationship has been debated for decades, and yet most of the supportive evidence has come from ecological or cross-sectional analyses with very limited long-term exposure data. We conducted an individual-level, longitudinal study to investigate the association between ambient temperature and externalizing behaviors of urban-dwelling adolescents. Participants (n = 1,287) in the Risk Factors for Antisocial Behavior Study, in California, were examined during 2000-2012 (aged 9-18 years) with repeated assessments of their externalizing behaviors (e.g., aggression, delinquency). Ambient temperature data were obtained from the local meteorological information system. In adjusted multilevel models, aggressive behaviors significantly increased with rising average temperatures (per 1°C increment) in the preceding 1, 2, or 3 years (respectively, β = 0.23, 95% confidence interval (CI): 0.00, 0.46; β = 0.35, 95% CI: 0.06, 0.63; or β = 0.41, 95% CI: 0.08, 0.74), equivalent to 1.5-3.0 years of delay in age-related behavioral maturation. These associations were slightly stronger among girls and families of lower socioeconomic status but greatly diminished in neighborhoods with more green space. No significant associations were found with delinquency. Our study provides the first individual-level epidemiologic evidence supporting the adverse association of long-term ambient temperature and aggression. Similar approaches to studying meteorology and violent crime might further inform scientific debates on climate change and collective violence.

Socioeconomic disparities and sexual dimorphism in neurotoxic effects of ambient fine particles on youth IQ: A longitudinal analysis

Mounting evidence indicates that early-life exposure to particulate air pollutants pose threats to children’s cognitive development, but studies about the neurotoxic effects associated with exposures during adolescence remain unclear. We examined whether exposure to ambient fine particles (PM2.5) at residential locations affects intelligence quotient (IQ) during pre-/early- adolescence (ages 9–11) and emerging adulthood (ages 18–20) in a demographically-diverse population (N = 1,360) residing in Southern California. Increased ambient PM2.5 levels were associated with decreased IQ scores. This association was more evident for Performance IQ (PIQ), but less for Verbal IQ, assessed by the Wechsler Abbreviated Scale of Intelligence. For each inter-quartile (7.73 μg/m3) increase in one-year PM2.5 preceding each assessment, the average PIQ score decreased by 3.08 points (95% confidence interval = [-6.04, -0.12]) accounting for within-family/within-individual correlations, demographic characteristics, family socioeconomic status (SES), parents’ cognitive abilities, neighborhood characteristics, and other spatial confounders. The adverse effect was 150% greater in low SES families and 89% stronger in males, compared to their counterparts. Better understanding of the social disparities and sexual dimorphism in the adverse PM2.5–IQ effects may help elucidate the underlying mechanisms and shed light on prevention strategies.