High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city.

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am - 10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-hour measurements over two-week periods. Flexible Generalized Additive Models (GAMs), which combined data on pollution concentrations, spatial and spatial and temporal GIS predictors, and spatial and temporal terms, were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54 - 0.86). Varying spatial patterns was observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of predictive exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

Within-city spatial variations in PM2.5 magnetite nanoparticles and brain cancer incidence in Toronto and Montreal, Canada.

Magnetite nanoparticles are small, strongly magnetic iron oxide particles which are produced during high-temperature combustion and friction processes and form part of the outdoor air pollution mixture. These particles can translocate to the brain and have been found in human brain tissue. In this study, we estimated associations between within-city spatial variations in concentrations of magnetite nanoparticles in outdoor fine particulate matter (PM2.5) and brain cancer incidence. We performed a cohort study of 1.29 million participants in four cycles of the Canadian Census Health and Environment Cohort in Montreal and Toronto, Canada who were followed for malignant brain tumour (glioma) incidence. As a proxy for magnetite nanoparticle content, we measured the susceptibility of anhysteretic remanent magnetization (χARM) in PM2.5 samples (N = 124 in Montreal, N = 110 in Toronto), and values were assigned to residential locations. Stratified Cox proportional hazards models were used to estimate hazard ratios (per IQR change in volume-normalized χARM). ARM was not associated with brain tumour incidence (HR = 0.998, 95% CI 0.988, 1.009) after adjusting for relevant potential confounders. Although we found no evidence of an important relationship between within-city spatial variations in airborne magnetite nanoparticles and brain tumour incidence, further research is needed to evaluate this understudied exposure, and other measures of exposure to magnetite nanoparticles should be considered.

Associations between brake and tire wear-related PM2.5 metal components, particulate oxidative stress potential, and autism spectrum disorder in Southern California.

BACKGROUND: Air pollution is a global health concern, with fine particulate matter (PM2.5) constituents posing potential risks to human health, including children's neurodevelopment. Here we investigated associations between exposure during pregnancy and infancy to specific traffic-related PM2.5 components with Autism Spectrum Disorder (ASD) diagnosis. METHODS: For exposure assessment, we estimated PM2.5 components related to traffic exposure (Barium [Ba] as a marker of brake dust and Zinc [Zn] as a tire wear marker, Black Carbon [BC]) and oxidative stress potential (OSP) markers (Hydroxyl Radical [OPOH] formation, Dithiothreitol activity [OPDTT], reactive oxygen species [ROS]) modeled with land use regression with co-kriging based on an intensive air monitoring campaign. We assigned exposures to a cohort of 444,651 children born in Southern California between 2016 and 2019, among whom 11,466 ASD cases were diagnosed between 2018 and 2022, Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained with logistic regression for single pollutant and PM2.5 mass co-adjusted models, also adjusting for sociodemographic characteristics. RESULTS: Among PM2.5 components, we found the strongest positive association with ASD for our brake wear marker Ba (ORper IQR = 1.29, 95 % CI: 1.24, 1.34). This was followed by an increased risk for all PM2.5 oxidative stress potential markers; the strongest association was with ROS formation (ORper IQR = 1.22, 95 % CI: 1.18, 1.25). PM2.5 mass was linked to ASD in Hispanic and Black children, but not White children, while traffic-related PM2.5 and OSP markers increased ASD risk across all groups. In neighborhoods with the lowest socioeconomic status (SES), associations with ASD were stronger for all examined pollutants compared to higher SES areas. CONCLUSIONS: Our findings suggest that brake wear-related PM2.5 and PM2.5 OSP are associated with ASD diagnosis in Southern California. These results suggest that strategies aimed at reducing the public health impacts of PM2.5 need to consider specific sources.

Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution.

Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, and ensuring transportation equity. We conducted a large-scale mobile measurement campaign and employed a gradient boost model to generate exposure surfaces using land use, built environment, and meteorological conditions. The Ontario Marginalization Index was used to quantify various indicators of social disadvantage for Toronto's neighborhoods. Our findings reveal that people in socioeconomically disadvantaged areas experience elevated UFP exposures. We highlight significant disparities in accessing sustainable transportation, particularly in areas with higher ethnic concentrations. When factoring in daily mobility, UFP exposure disparities in disadvantaged populations are further exacerbated. Furthermore, individuals who do not generate emissions themselves are consistently exposed to higher UFPs, with active transportation users experiencing the highest UFP exposures both at home and at activity locations. Finally, we proposed a novel index, the Community Prioritization Index (CPI), incorporating three indicators, including air quality, social disadvantage, and sustainable transportation. This index identifies neighborhoods experiencing a triple burden, often situated near major infrastructure hubs with high diesel truck activity and lacking greenspace, marking them as high-priority areas for policy action and targeted interventions.

Air pollution prediction and backcasting through a combination of mobile monitoring and historical on-road traffic emission inventories.

An important challenge for studies of air pollution and health effects is the derivation of historical exposures. These generally entail some form of backcasting, which refers to a range of approaches that aim to project a current surface into the past. Accurate backcasting is conditional upon the availability of historical data for predictor variables and the ability to capture spatial and temporal trends in these variables. This study proposes a method to backcast traffic-related air pollution surfaces developed using land-use regression models by including temporal variability of traffic and emissions and trends in concentrations measured at reference stations. Nitrogen dioxide (NO2) concentrations collected in the City of Toronto using the Urban Scanner mobile platform were adjusted for historical trends captured at reference stations. The Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST), a powerful tool for time series decomposition, was employed to isolate seasonal variations, annual trends, and abrupt changes in NO2 at reference stations, hence decomposing the signal. Exposure surfaces were generated for a period extending from 2006 to 2020, exhibiting decreases ranging from 10 to 50 % depending on the neighborhood, with an average of 20.46 % across the city. Yearly surfaces were intersected with mobility patterns of Torontonians extracted from travel survey data for 2006 and 2016, illustrating strong spatial gradients in the evolution of NO2 over time, with larger decreases along major roads and highways and in the central core. These findings demonstrate that air pollution improvements throughout the 14 years are inhomogeneous across space.

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia.

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Fine Particulate Matter Metal Composition, Oxidative Potential, and Adverse Birth Outcomes in Los Angeles.

BACKGROUND: Although many studies have linked prenatal exposure to PM2.5 to adverse birth outcomes, little is known about the effects of exposure to specific constituents of PM2.5 or mechanisms that contribute to these outcomes. OBJECTIVES: Our objective was to investigate effects of oxidative potential and PM2.5 metal components from non-exhaust traffic emissions, such as brake and tire wear, on the risk of preterm birth (PTB) and term low birth weight (TLBW). METHODS: For a birth cohort of 285,614 singletons born in Los Angeles County, California, in the period 2017-2019, we estimated speciated PM2.5 exposures modeled from land use regression with cokriging, including brake and tire wear related metals (barium and zinc), black carbon, and three markers of oxidative potential (OP), including modeled reactive oxygen species based on measured iron and copper (ROS), OH formation (OPOH), and dithiothreitol (DTT) loss (OPDTT). Using logistic regression, we estimated odds ratios (OR) and 95% confidence intervals (CI) for PTB and TLBW with speciated PM2.5 exposures and PM2.5 mass as continuous variables scaled by their interquartile range (IQR). RESULTS: For both metals and oxidative potential metrics, we estimated increased risks for PTB (ORs ranging from 1.01 to 1.03) and TLBW (ORs ranging from 1.02 to 1.05) per IQR exposure increment that were robust to adjustment for PM2.5 mass. Associations for PM2.5 mass, black carbon, metal components, and oxidative potential (especially ROS and OPOH) with adverse birth outcomes were stronger in Hispanic, Black, and mixed-race or Native American women. DISCUSSION: Our results indicate that exposure to PM2.5 metals from brake and tire wear and particle components that contribute to oxidative potential were associated with an increased risk of PTB and TLBW in Los Angeles County, particularly among Hispanic, Black, and mixed-race or Native American women. Thus, reduction of PM2.5 mass only may not be sufficient to protect the most vulnerable pregnant women and children from adverse effects due to traffic source exposures. https://doi.org/10.1289/EHP12196.

Daily Summer Temperatures and Hospitalization for Acute Cardiovascular Events: Impact of Outdoor PM 2.5 Oxidative Potential on Observed Associations Across Canada.

BACKGROUND: Oxidative stress plays an important role in the health impacts of both outdoor fine particulate air pollution (PM 2.5 ) and thermal stress. However, it is not clear how the oxidative potential of PM 2.5 may influence the acute cardiovascular effects of temperature. METHODS: We conducted a case-crossover study of hospitalization for cardiovascular events in 35 cities across Canada during the summer months (July-September) between 2016 and 2018. We collected three different metrics of PM 2.5 oxidative potential each month in each location. We estimated associations between lag-0 daily temperature (per 5ºC) and hospitalization for all cardiovascular (n = 44,876) and ischemic heart disease (n = 14,034) events across strata of monthly PM 2.5 oxidative potential using conditional logistical models adjusting for potential time-varying confounders. RESULTS: Overall, associations between lag-0 temperature and acute cardiovascular events tended to be stronger when outdoor PM 2.5 oxidative potential was higher. For example, when glutathione-related oxidative potential (OP GSH ) was in the highest tertile, the odds ratio (OR) for all cardiovascular events was 1.040 (95% confidence intervals [CI] = 1.004, 1.074) compared with 0.980 (95% CI = 0.943, 1.018) when OP GSH was in the lowest tertile. We observed a greater difference for ischemic heart disease events, particularly for older subjects (age >70 years). CONCLUSIONS: The acute cardiovascular health impacts of summer temperature variations may be greater when outdoor PM 2.5 oxidative potential is elevated. This may be particularly important for ischemic heart disease events.

Predicting spatial variations in annual average outdoor ultrafine particle concentrations in Montreal and Toronto, Canada: Integrating land use regression and deep learning models.

BACKGROUND: Concentrations of outdoor ultrafine particles (UFP; <0.1 µm) and black carbon (BC) can vary greatly within cities and long-term exposures to these pollutants have been associated with a variety of adverse health outcomes. OBJECTIVE: This study integrated multiple approaches to develop new models to estimate within-city spatial variations in annual median (i.e. average) outdoor UFP and BC concentrations as well as mean UFP size in Canada's two largest cities, Montreal and Toronto. METHODS: We conducted year-long mobile monitoring campaigns in each city that included evenings and weekends. We developed generalized additive models trained on land use parameters and deep Convolutional Neural Network (CNN) models trained on satellite-view images. Using predictions from these models, we developed final combined models. RESULTS: In Toronto, the median observed UFP concentration, UFP size, and BC concentration values were 16,172pt/cm3, 33.7 nm, and 1225 ng/m3, respectively. In Montreal, the median observed UFP concentration, UFP size, and BC concentration values were 14,702pt/cm3, 29.7 nm, and 1060 ng/m3, respectively. For all pollutants in both cities, the proportion of spatial variation explained (i.e., R2) was slightly greater (1-2 percentage points) for the combined models than the generalized additive models and a greater (approximately 10 percentage points) than the deep CNN models. The Toronto combined model R2 values in the test set were 0.73, 0.55, and 0.61 for UFP concentrations, UFP size, and BC concentration, respectively. The Montreal combined model R2 values were 0.60, 0.49, and 0.60 for UFP concentration, UFP size, and BC concentration models respectively. For each pollutant, predictions from the combined, deep CNN, and generalized additive models were highly correlated with each other and differences between models were explored in sensitivity analyses. CONCLUSION: Predictions from these models are available to support future epidemiological research examining long-term health impacts of outdoor UFPs and BC.

Within-city spatial variations in long-term average outdoor oxidant gas concentrations and cardiovascular mortality: Effect modification by oxidative potential in the Canadian Census Health and Environment Cohort.

Health effects of oxidant gases may be enhanced by components of particulate air pollution that contribute to oxidative stress. Our aim was to examine if within-city spatial variations in the oxidative potential of outdoor fine particulate air pollution (PM2.5) modify relationships between oxidant gases and cardiovascular mortality. Methods: We conducted a retrospective cohort study of participants in the Canadian Census Health and Environment Cohort who lived in Toronto or Montreal, Canada, from 2002 to 2015. Cox proportional hazards models were used to estimate associations between outdoor concentrations of oxidant gases (Ox, a redox-weighted average of nitrogen dioxide and ozone) and cardiovascular deaths. Analyses were performed across strata of two measures of PM2.5 oxidative potential and reactive oxygen species concentrations (ROS) adjusting for relevant confounding factors. Results: PM2.5 mass concentration showed little within-city variability, but PM2.5 oxidative potential and ROS were more variable. Spatial variations in outdoor Ox were associated with an increased risk of cardiovascular mortality [HR per 5 ppb = 1.028, 95% confidence interval (CI): 1.001, 1.055]. The effect of Ox on cardiovascular mortality was stronger above the median of each measure of PM2.5 oxidative potential and ROS (e.g., above the median of glutathione-based oxidative potential: HR = 1.045, 95% CI: 1.009, 1.081; below median: HR = 1.000, 95% CI: 0.960, 1.043). Conclusion: Within-city spatial variations in PM2.5 oxidative potential may modify long-term cardiovascular health impacts of Ox. Regions with elevated Ox and PM2.5 oxidative potential may be priority areas for interventions to decrease the population health impacts of outdoor air pollution.

Personal Mobility Choices and Disparities in Carbon Emissions.

The promotion of sustainable mobility choices is a crucial element of transport decarbonization. It requires a fundamental understanding of the choices available to urban dwellers and of the equity and justice implications of green mobility solutions. In this study, we quantified personal mobility-related greenhouse gas (GHG) emissions in the Greater Toronto and Hamilton Area (GTHA) and their associations with various land use, built environment, and socioeconomic factors. Our study captured personal, household, and neighborhood-level characteristics that are related to high emissions and disparities in emissions across the study region. We observed that the top 30% of emitters generated 70% of all transportation GHG emissions. Household income, family size, and vehicle ownership were associated with increased mobility emissions, while increased population density was associated with lower emissions. The percentage of visible minorities in a neighborhood was associated with lower emissions, but this effect was small. We further contrasted the spatial distribution of traffic-related air pollution with mobility GHG emissions. The results suggest that individuals who emit less GHG live in areas with higher air pollution. A computer vision-based model was used to predict GHG emissions from aerial images of neighborhoods, demonstrating that areas with high land use mixture were linked to a lower generation of mobility-based GHG emissions.

Exposure to ultrafine particles and the incidence of asthma in children: A population-based cohort study in Montreal, Canada.

Asthma is the most prevalent chronic respiratory disease in children. The role of ultrafine particles (UFPs) in the development of the disease remains unclear. We used a population-based birth cohort to evaluate the association between prenatal and childhood exposure to low levels of ambient UFPs and childhood-onset asthma. Methods: The cohort included all children born and residing in Montreal, Canada, between 2000 and 2015. Children were followed for asthma onset from birth until <13 years of age. Spatially resolved annual mean concentrations of ambient UFPs were estimated from a land use regression model. We assigned prenatal exposure according to the residential postal code at birth. We also considered current exposure during childhood accounting for time-varying residence location. We estimated hazard ratios (HRs) using Cox proportional hazards models adjusted for age, sex, neighborhood material and social deprivation, calendar year, and coexposure to ambient nitrogen dioxide (NO2) and fine particles (PM2.5). Results: The cohort included 352,966 children, with 30,825 children developing asthma during follow-up. Mean prenatal and childhood UFP exposure were 24,706 particles/cm3 (interquartile range [IQR] = 3,785 particles/cm3) and 24,525 particles/cm3 (IQR = 3,427 particles/cm3), respectively. Both prenatal and childhood UFP exposure were not associated with childhood asthma onset in single pollutant models (HR per IQR increase of 0.99 [95% CI = 0.98, 1.00]). Estimates of association remained similar when adjusting for coexposure to ambient NO2 and PM2.5. Conclusion: In this population-based birth cohort, childhood asthma onset was not associated with prenatal or childhood exposure to low concentrations of UFPs.

Randomized Cross-Over Study of In-Vehicle Cabin Air Filtration, Air Pollution Exposure, and Acute Changes to Heart Rate Variability, Saliva Cortisol, and Cognitive Function.

To determine how traffic-related air pollution (TRAP) exposures affect commuter health, and whether cabin air filtration (CAF) can mitigate exposures, we conducted a cross-over study of 48 adults exposed to TRAP during two commutes with and without CAF. Measurements included particulate air pollutants (PM2.5, black carbon [BC], ultrafine particles [UFPs]), volatile organic compounds, and nitrogen dioxide. We measured participants' heart rate variability (HRV), saliva cortisol, and cognitive function. On average, CAF reduced concentrations of UFPs by 26,232 (95%CI: 11,734, 40,730) n/cm3, PM2.5 by 6 (95%CI: 5, 8) µg/m3, and BC by 1348 (95%CI: 1042, 1654) ng/m3, or 28, 30, and 32%, respectively. Each IQR increase in PM2.5 was associated with a 28% (95%CI: 2, 60) increase in high-frequency power HRV at the end of the commute and a 22% (95%CI: 7, 39) increase 45 min afterward. IQR increases in UFPs were associated with increased saliva cortisol in women during the commute (18% [95%CI: 0, 40]). IQR increases in UFPs were associated with strong switching costs (19% [95%CI: 2, 39]), indicating a reduced capacity for multitasking, and PM2.5 was associated with increased reaction latency, indicating slower responses (5% [95%CI: 1, 10]). CAF can reduce particulate exposures by almost a third.

Short-term exposure to ambient air pollution and individual emergency department visits for COVID-19: a case-crossover study in Canada.

BACKGROUND: Ambient air pollution is thought to contribute to increased risk of COVID-19, but the evidence is controversial. OBJECTIVE: To evaluate the associations between short-term variations in outdoor concentrations of ambient air pollution and COVID-19 emergency department (ED) visits. METHODS: We conducted a case-crossover study of 78 255 COVID-19 ED visits in Alberta and Ontario, Canada between 1 March 2020 and 31 March 2021. Daily air pollution data (ie, fine particulate matter with diameter less than 2.5 µm (PM2.5), nitrogen dioxide (NO2) and ozone were assigned to individual case of COVID-19 in 10 km × 10 km grid resolution. Conditional logistic regression was used to estimate associations between air pollution and ED visits for COVID-19. RESULTS: Cumulative ambient exposure over 0-3 days to PM2.5 (OR 1.010; 95% CI 1.004 to 1.015, per 6.2 µg/m3) and NO2 (OR 1.021; 95% CI 1.015 to 1.028, per 7.7 ppb) concentrations were associated with ED visits for COVID-19. We found that the association between PM2.5 and COVID-19 ED visits was stronger among those hospitalised following an ED visit, as a measure of disease severity, (OR 1.023; 95% CI 1.015 to 1.031) compared with those not hospitalised (OR 0.992; 95% CI 0.980 to 1.004) (p value for effect modification=0.04). CONCLUSIONS: We found associations between short-term exposure to ambient air pollutants and COVID-19 ED visits. Exposure to air pollution may also lead to more severe COVID-19 disease.

Fine Particulate Air Pollution and the "No-Multiple-Versions-of-Treatment" Assumption: Does Particle Composition Matter for Causal Inference?

Here we discuss possible violations of the "no-multiple-versions-of-treatment" assumption in studies of outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5)) owing to differences in particle composition, which in turn influence health. This assumption is part of the potential outcomes framework for causal inference, and it is needed for well-defined potential outcomes, as multiple versions of the same treatment could lead to different health risks for the same level of treatment. Since 2 locations can have the same outdoor PM2.5 mass concentration (i.e., treatment) but different chemical compositions (i.e., versions of treatment), violations of the "no-multiple-versions-of-treatment" assumption seem likely. Importantly, violations of this assumption will not bias health risk estimates for PM2.5 mass concentrations if there are no unmeasured confounders of the "version of treatment"-outcome relationship. However, confounding can occur if these factors are not identified and controlled for in the analysis. We describe situations in which this may occur and provide simulations to estimate the magnitude and direction of this possible bias. In general, violations of the "no-multiple-versions-of-treatment" assumption could be an underappreciated source of bias in studies of outdoor PM2.5. Analysis of the health impacts of outdoor PM2.5 mass concentrations across spatial domains with similar composition could help to address this issue.

Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform.

This paper describes a mobile air pollution sampling system, the Urban Scanner, which aims at gathering dense spatiotemporal air quality data to support urban air quality and exposure science. Urban Scanner comprises custom vehicle-mounted sensors for air pollution, meteorology, and built environment data collection (low-cost sensors, wind anemometer, 360 deg camera, LIDAR, GPS) as well as a server to store, process, and map all gathered geo-referenced sensory information. Two levels of sensor calibration were implemented, both in a chamber and in the field, against reference instrumentation. Chamber tests and a set of mathematical tools were developed to correct for sensor noise (wavelet denoising), misalignment (linear and nonlinear), and hysteresis removal. Models based on chamber testing were further refined based on field co-location. While field co-location captures natural changes in air pollution and meteorology, chamber tests allow for simulating fast transitions in these variables, like the transitions experienced by a mobile sensor in an urban environment. The best suite of models achieved an R2 higher than 0.9 between sensor output and reference station observations and an RMSE of 2.88 ppb for nitrogen dioxide and 4.03 ppb for ozone. A mobile sampling campaign was conducted in the city of Toronto, Canada, to further test Urban Scanner. We observe that the platform adequately captures spatial and temporal variability in urban air pollution, leading to the development of land-use regression models with high explanatory power.

Aerosol Oxidative Potential in the Greater Los Angeles Area: Source Apportionment and Associations with Socioeconomic Position.

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 µm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.

Unequal airborne exposure to toxic metals associated with race, ethnicity, and segregation in the USA.

Persons of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Does early life exposure to exogenous sources of reactive oxygen species (ROS) increase the risk of respiratory and allergic diseases in children? A longitudinal cohort study.

BACKGROUND: Excess reactive oxygen species (ROS) can cause oxidative stress damaging cells and tissues, leading to adverse health effects in the respiratory tract. Yet, few human epidemiological studies have quantified the adverse effect of early life exposure to ROS on child health. Thus, this study aimed to examine the association of levels of ROS exposure at birth and the subsequent risk of developing common respiratory and allergic diseases in children. METHODS: 1,284 Toronto Child Health Evaluation Questionnaire (T-CHEQ) participants were followed from birth (born between 1996 and 2000) until outcome, March 31, 2016 or loss-to-follow-up. Using ROS data from air monitoring campaigns and land use data in Toronto, ROS concentrations generated in the human respiratory tract in response to inhaled pollutants were estimated using a kinetic multi-layer model. These ROS values were assigned to participants' postal codes at birth. Cox proportional hazards regression models, adjusted for confounders, were then used to estimate hazard ratios (HR) with 95% confidence intervals (CI) per unit increase in interquartile range (IQR). RESULTS: After adjusting for confounders, iron (Fe) and copper (Cu) were not significantly associated with the risk of asthma, allergic rhinitis, nor eczema. However, ROS, a measure of the combined impacts of Fe and Cu in PM2.5, was associated with an increased risk of asthma (HR = 1.11, 95% CI: 1.02-1.21, p < 0.02) per IQR. There were no statistically significant associations of ROS with allergic rhinitis (HR = 0.96, 95% CI: 0.88-1.04, p = 0.35) and eczema (HR = 1.03, 95% CI: 0.98-1.09, p = 0.24). CONCLUSION: These findings showed that ROS exposure in early life significantly increased the childhood risk of asthma, but not allergic rhinitis and eczema.