Determinants of the Spatial Distributions of Elemental Carbon and Particulate Matter in Eight Southern Californian Communities

Exposure models for particulate matter elemental concentrations in Southern California

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM_0.2), accumulation mode fine (PM_0.2-2.5), and coarse (PM_2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

Contribution of tailpipe and non-tailpipe traffic sources to quasi-ultrafine, fine and coarse particulate matter in southern California

Exposure to traffic-related air pollution (TRAP) in the near-roadway environment is associated with multiple adverse health effects. To characterize the relative contribution of tailpipe and non-tailpipe TRAP sources to particulate matter (PM) in the quasi-ultrafine (PM_0.2), fine (PM_2.5) and coarse (PM_2.5-10) size fractions and identify their spatial determinants in southern California (CA). Month-long integrated PM_0.2, PM_2.5 and PM_2.5-10 samples (n = 461, 265 and 298, respectively) were collected across cool and warm seasons in 8 southern CA communities (2008-9). Concentrations of PM mass, elements, carbons and major ions were obtained. Enrichment ratios (ER) in PM_0.2 and PM₁₀ relative to PM_2.5 were calculated for each element. The Positive Matrix Factorization model was used to resolve and estimate the relative contribution of TRAP sources to PM in three size fractions. Generalized additive models (GAMs) with bivariate loess smooths were used to understand the geographic variation of TRAP sources and identify their spatial determinants. EC, OC, and B had the highest median ER in PM_0.2 relative to PM_2.5. Six, seven and five sources (with characteristic species) were resolved in PM_0.2, PM_2.5 and PM_2.5-10, respectively. Combined tailpipe and non-tailpipe traffic sources contributed 66%, 32% and 18% of PM_0.2, PM_2.5 and PM_2.5-10 mass, respectively. Tailpipe traffic emissions (EC, OC, B) were the largest contributor to PM_0.2 mass (58%). Distinct gasoline and diesel tailpipe traffic sources were resolved in PM_2.5. Others included fuel oil, biomass burning, secondary inorganic aerosol, sea salt, and crustal/soil. CALINE4 dispersion model nitrogen oxides, trucks and intersections were most correlated with TRAP sources. The influence of smaller roadways and intersections became more apparent once Long Beach was excluded. Non-tailpipe emissions constituted ~8%, 11% and 18% of PM_0.2, PM_2.5 and PM_2.5-10, respectively, with important exposure and health implications. Future efforts should consider non-linear relationships amongst predictors when modeling exposures. Implications: Vehicle emissions result in a complex mix of air pollutants with both tailpipe and non-tailpipe components. As mobile source regulations lead to decreased tailpipe emissions, the relative contribution of non-tailpipe traffic emissions to near-roadway exposures is increasing. This study documents the presence of non-tailpipe abrasive vehicular emissions (AVE) from brake and tire wear, catalyst degradation and resuspended road dust in the quasi-ultrafine (PM_0.2), fine and coarse particulate matter size fractions, with contributions reaching up to 30% in PM_0.2 in some southern California communities. These findings have important exposure and policy implications given the high metal content of AVE and the efficiency of PM_0.2 at reaching the alveolar region of the lungs and other organ systems once inhaled. This work also highlights important considerations for building models that can accurately predict tailpipe and non-tailpipe exposures for population health studies.

Air pollution, noise exposure, and metabolic syndrome - A cohort study in elderly Mexican-Americans in Sacramento area

BACKGROUND

Previous studies suggested that air pollutants may increase the incidence of metabolic syndrome, but the potential impact from traffic sources is not well-understood. This study aimed to investigate associations between traffic-related nitrogen oxides (NOx) or noise pollution and risk of incident metabolic syndrome and its components in an elderly Mexican-American population.

METHODS

A total of 1,554 Mexican-American participants of the Sacramento Area Latino Study on Aging (SALSA) cohort were followed from 1998 to 2007. We used anthropometric measures and biomarkers to define metabolic syndrome according to the recommendations of the Third Adult Treatment Panel of the National Cholesterol Education Program (NCEP ATP III). Based on participants' residential addresses at baseline, estimates of local traffic-related NOx were generated using the California Line Source Dispersion Model version 4 (CALINE4), and of noise employing the SoundPLAN software package. We used Cox regression models with calendar time as the underlying time scale to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations of air pollution or noise with metabolic syndrome or its components.

RESULTS

Each per unit increase of traffic-related NOx (2.29 parts per billion (ppb)) was associated with a 15% (HR = 1.15, 95% CI: 1.04-1.28) lower level of high-density lipoprotein cholesterol (HDL-cholesterol), and each 11.6 decibels (dB) increase in noise increased the risk of developing metabolic syndrome by 17% (HR = 1.17, 95% CI: 1.01-1.35).

CONCLUSION

Policies aiming to reduce traffic-related air pollution and noise might mitigate the risk of metabolic syndrome and its components in vulnerable populations.

Study Design, Protocol and Profile of the Maternal And Developmental Risks from Environmental and Social Stressors (MADRES) Pregnancy Cohort: a Prospective Cohort Study in Predominantly Low-Income Hispanic Women in Urban Los Angeles

BACKGROUND

The burden of childhood and adult obesity disproportionally affects Hispanic and African-American populations in the US, and these groups as well as populations with lower income and education levels are disproportionately affected by environmental pollution. Pregnancy is a critical developmental period where maternal exposures may have significant impacts on infant and childhood growth as well as the future health of the mother. We initiated the "Maternal And Developmental Risks from Environmental and Social Stressors (MADRES)" cohort study to address critical gaps in understanding the increased risk for childhood obesity and maternal obesity outcomes among minority and low-income women in urban Los Angeles.

METHODS

The MADRES cohort is specifically examining whether pre- and postpartum environmental exposures, in addition to exposures to psychosocial and built environment stressors, lead to excessive gestational weight gain and postpartum weight retention in women and to perturbed infant growth trajectories and increased childhood obesity risk through altered psychological, behavioral and/or metabolic responses. The ongoing MADRES study is a prospective pregnancy cohort of 1000 predominantly lower-income, Hispanic women in Los Angeles, CA. Enrollment in the MADRES cohort is initiated prior to 30 weeks gestation from partner community health clinics in Los Angeles. Cohort participants are followed through their pregnancies, at birth, and during the infant's first year of life through a series of in-person visits with interviewer-administered questionnaires, anthropometric measurements and biospecimen collection as well as telephone interviews conducted with the mother.

DISCUSSION

In this paper, we outline the study rationale and data collection protocol for the MADRES cohort, and we present a profile of demographic, health and exposure characteristics for 291 participants who have delivered their infants, out of 523 participants enrolled in the study from November 2015 to October 2018 from four community health clinics in Los Angeles. Results from the MADRES cohort could provide a powerful rationale for regulation of targeted chemical environmental components, better transportation and urban design policies, and clinical recommendations for stress-coping strategies and behavior to reduce lifelong obesity risk.

Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health

Mobile and wearable technologies and novel methods of data collection are innovating health-related research. These technologies and methods allow for multi-system level capture of data across environmental, physiological, behavioral, and psychological domains. In the Adolescent Brain Cognitive Development (ABCD) Study, there is great potential for harnessing the acceptability, accessibility, and functionality of mobile and social technologies for in-vivo data capture to precisely measure factors, and interactions between factors, that contribute to childhood and adolescent neurodevelopment and psychosocial and health outcomes. Here we discuss advances in mobile and wearable technologies and methods of analysis of geospatial, ecologic, social network and behavioral data. Incorporating these technologies into the ABCD study will allow for interdisciplinary research on the effects of place, social interactions, environment, and substance use on health and developmental outcomes in children and adolescents.

RISK EFFECTS OF NEAR-ROADWAY POLLUTANTS AND ASTHMA STATUS ON BRONCHITIC SYMPTOMS IN CHILDREN

Supplemental Digital Content is available in the text.

Background:

Bronchitic symptoms in children pose a significant clinical and public health burden. Exposures to criteria air pollutants affect bronchitic symptoms, especially in children with asthma. Less is known about near-roadway exposures.

Methods:

Bronchitic symptoms (bronchitis, chronic cough, or phlegm) in the past 12 months were assessed annually with 8 to 9 years of follow-up on 6757 children from the southern California Children’s Health Study. Residential exposure to freeway and non-freeway near-roadway air pollution was estimated using a line-source dispersion model. Mixed-effects logistic regression models were used to relate near-roadway air pollutant exposures to bronchitic symptoms among children with and without asthma.

Results:

Among children with asthma, a 2 SD increase in non-freeway exposures (odds ratio [OR]: 1.44; 95% confidence interval [CI]: 1.17, 1.78) and freeway exposures (OR: 1.31; 95% CI: 1.06, 1.60) were significantly associated with increased risk of bronchitic symptoms. Among children without asthma, only non-freeway exposures had a significant association (OR: 1.14; 95% CI: 1.00, 1.29). Associations were strongest among children living in communities with lower regional particulate matter.

Conclusions:

Near-roadway air pollution was associated with bronchitic symptoms, especially among children with asthma and those living in communities with lower regional particulate matter. Better characterization of traffic pollutants from non-freeway roads is needed since many children live in close proximity to this source.

Particle count and black carbon measurements at schools in Las Vegas, NV and in the greater Salt Lake City, UT area

As part of two separate studies aimed to characterize ambient pollutant concentrations at schools in urban areas, we compare black carbon and particle count measurements at Adcock Elementary in Las Vegas, NV (April-June 2013), and Hunter High School in the West Valley City area of greater Salt Lake City, UT (February 2012). Both schools are in urban environments, but Adcock Elementary is next to the U.S. 95 freeway. Black carbon (BC) concentrations were 13% higher at Adcock compared to Hunter, while particle count concentrations were 60% higher. When wind speeds were low-less than 2 m/sec-both BC and particle count concentrations were significantly higher at Adcock, while concentrations at Hunter did not have as strong a variation with wind speed. When wind speeds were less than 2 m/sec, emissions from the adjacent freeway greatly affected concentrations at Adcock, regardless of wind direction. At both sites, BC and particle count concentrations peaked in the morning during commute hours. At Adcock, particle count also peaked during midday or early afternoon, when BC was low and conditions were conducive to new particle formation. While this midday peak occurred at Adcock on roughly 45% of the measured days, it occurred on only about 25% of the days at Hunter, since conditions for particle formation (higher solar radiation, lower wind speeds, lower relative humidity) were more conducive at Adcock. Thus, children attending these schools are likely to be exposed to pollution peaks during school drop-off in the morning, when BC and particle count concentrations peak, and often again during lunchtime recess when particle count peaks again.

IMPLICATIONS

Particle count concentrations at two schools were shown to typically be independent of BC or other pollutants. At a school in close proximity to a major freeway, particle count concentrations were high during the midday and when wind speeds were low, regardless of wind direction, showing a large area of effect from roadway emissions even when the school was not downwind of the roadway. At the second school, which sits in an urban neighborhood away from freeways, high particle counts occurred even though solar radiation was low during wintertime conditions, meaning that exposure to high particle counts can occur throughout the year.

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States

Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R² of the model was good in both cold (10-fold CV R² = 0.87) and warm seasons (CV R² = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.

PM10 mass concentration, chemical composition, and sources in the typical coal-dominated industrial city of Pingdingshan, China

The atmospheric pollution created by coal-dominated industrial cities in China cannot be neglected. This study focuses on the atmospheric PM10 in the typical industrial city of Pingdingshan City in North China. A total of 44 PM10 samples were collected from three different sites (power plant, mining area, and roadside) in Pingdingshan City during the winter of 2013, and were analyzed gravimetrically and chemically. The Pingdingshan PM10 samples were composed of mineral matter (average of 118.0±58.6μg/m(3), 20.6% of the total PM10 concentration), secondary crystalline particles (338.7±122.0μg/m(3), 59.2%), organic matter (77.3±48.5μg/m(3), 13.5%), and elemental carbon (38.0±28.3μg/m(3), 6.6%). Different sources had different proportions of these components in PM10. The power plant pollutant source was characterized by secondary crystalline particles (377.1μg/m(3)), elemental carbon (51.5μg/m(3)), and organic matter (90.6μg/m(3)) due to coal combustion. The mining area pollutant source was characterized by mineral matter (124.0μg/m(3)) due to weathering of waste dumps. The roadside pollutant source was characterized by mineral matter (130.0μg/m(3)) and organic matter (81.0μg/m(3)) due to road dust and vehicle exhaust, respectively. A positive matrix factorization (PMF) analysis was performed for PM10 source apportionment to identify major anthropogenic sources of PM10 in Pingdingshan. Six factors-crustal matter, coal combustion, vehicle exhaust and abrasion, local burning, weathering of waste dumps, and industrial metal smelting-were identified and their contributions to Pingdingshan PM10 were 19.0%, 31.6%, 7.4%, 6.3%, 9.8%, and 25.9%, respectively. Compared to other major cities in China, the source of PM10 in Pingdingshan was characterized by coal combustion, weathering of waste dumps, and industrial metal smelting.