Characterizing traffic-related air pollutants (TRAPs), which significantly impact health, and greenhouse gases (GHGs) can be challenging in urban environments. Mobile monitoring has the potential to capture the spatial distribution of these pollutants. We present results from a campaign using the Denver Mobile Monitoring Laboratory (DMML) in the summer of 2023 when we measured ultrafine particles (UFPs), black carbon (BC), ozone (O3), methane (CH4), and carbon dioxide (CO2) concentrations in Denver, CO. Despite our campaign being brief, we obtained several interesting results. We observed elevated UFP and BC concentrations on major roads. In contrast, O3 concentrations were higher on neighborhood streets and roads and in the industrial neighborhood of Commerce City. We consistently observed elevated CH4 concentrations (>2.5 ppm) on highway I-70, suggesting the presence of a previously unknown major source of CH4. The CH4 concentrations measured in our campaign did not align with those from an overlapping aerial campaign, suggesting that mobile monitoring is crucial to capture important, potentially intermittent CH4 hotspots in cities. We evaluated if trees mitigated pollution concentrations, as planting trees is a key policy initiative of the city of Denver. We observed significant negative associations between tree canopy coverage and UFPs, BC, and CH4, and a positive association with O3 when using linear mixed-effects regression models. Our work highlights the importance of investigating the role of tree canopy coverage to mitigate TRAPs.

Ultrafine particles (UFP, Dp < 100 nm) are abundantly emitted by aircraft, but quantifying their contributions to ambient particle number concentrations (PNC) is challenging due to confounding from local traffic and complex interactions between aircraft plumes and meteorology. We applied a machine learning (ML) model to a multi-year PNC data set collected near Boston Logan International Airport, incorporating meteorology, road traffic, and runway-specific aircraft activity. We used SHapley Additive exPlanations (SHAP), a game-theoretic method that attributes feature contributions to model predictions, to interpret the black box ensemble ML model. SHAP enabled hourly source attribution, revealing feature interactions and nonlinear effects often missed by traditional tools (e.g., linear regression). The model performed well (R2 = 0.66), exceeding typical hourly PNC models. SHAP results revealed that aircraft arrivals, particularly those on runways oriented perpendicular to the monitor-airport axis, were more influential than departures or on-ground airport activity. This suggests that aircraft not flying directly overhead can substantially impact ground-level air quality due to crosswinds. SHAP analysis further indicated that aircraft impacts depended on planetary boundary layer height, with intermediate heights associated with elevated PNC. This approach provides a novel and transferable framework for retrospective source-specific exposure assessment and improved characterization of aviation-related UFP in near-airport communities.

Bus lanes with intermittent prioritization (BLIPs) have been proposed as a way to reduce traffic burden and improve air quality along busy urban streets; however, to date, the impacts of BLIPs on local-scale air quality have not been thoroughly evaluated, due in part to challenges in study design. We measured traffic-emission proxies—black carbon aerosol and ultrafine particles—before and after the installation of a BLIP in the Boston area (Massachusetts, USA) in 2021, and compared our data with traffic measurements to determine whether changes in air quality were attributable to changes in traffic patterns. We used both stationary and mobile monitoring to characterize temporal and spatial variations in air quality both before and after the BLIP went into operation. Although the BLIP led to a reduction in traffic volume (~20%), we did not find evidence that this reduction caused a significant change in local air quality. Nonetheless, substantial spatial and temporal differences in pollutant concentrations were observed; the highest concentrations occurred closest to a nearby highway along a section of the bus lane that was in an urban canyon, likely causing pollutant trapping. Wind direction was a dominant influence: pollutant concentrations were generally higher during winds that oriented the bus lane downwind of or parallel to the highway. Based on our findings, we recommend in future studies to evaluate the effectiveness of BLIPs that: (i) traffic and air quality measurements be collected simultaneously for several non-weekend days immediately before and immediately after bus lanes are first put into operation; (ii) the evaluation should be performed when other significant changes in motorists’ driving behavior and bus ridership are not anticipated; and (iii) coordinated efforts be made to increase bus ridership and incentivize motorists to avoid using the bus lane during the hours of intermittent prioritization.

Determining aviation-related contributions to ambient ultrafine particle (UFP) concentrations in complex, multisource environments is challenging; source-specific differences in particle size distribution may provide a mechanism for source attribution. We examined UFP concentrations and size distribution across 32 particle diameters at a monitoring site in close proximity to Boston Logan International Airport across a two-year period, incorporating covariates for flight activity and meteorology. Total particle number concentration (PNC) was ∼2-fold higher when the site was downwind of the airport. During these wind conditions, particles between 8 and 12 nm in diameter comprised the largest proportion of overall PNC observed, consistent with aircraft contributions. Particle size distribution differed substantially between hours of predominant aircraft arrivals (peak modal diameter 9-11 nm) versus departures (peak modal diameter 39-52 nm). Peak concentrations of particles between 9 and 11 nm were found in the winter and during afternoon hours. We conducted a principal component analysis (PCA) to confirm particle size distributions from aviation activity. PCA results showed that nucleation-mode particles (<30 nm in diameter), specifically those between 9 and 11 nm, were associated with landing aircraft on a nearby runway, especially when the monitor was downwind of the airport. Our findings confirm that aviation-specific UFP emissions are dominated by nucleation mode particles, with long-term size distribution information able to distinguish between aircraft operations in near-airport communities.

Mobile monitoring has proven to be a very efficient tool to measure and feed into models of air pollution as it complements fixed air quality monitoring networks by adding spatiotemporal resolution. This paper explores best practices, opportunities and challenges related to mobile monitoring of air pollutants, focusing on three key application areas, namely source-, exposure-, and health-related use cases. Use cases are linked to users, ensuring mobile monitoring is effectively tailored to diverse research and policy needs. Tailoring mobile monitoring involves experimental design choices (platform, instrumentation, route planning and spatiotemporal coverage) and data processing choices (data-only vs modelling) optimized towards the envisaged use case. This position paper aims to guide researchers and air pollution stakeholders in generating high-quality mobile monitoring datasets. We identify best practices, discuss monitoring strategies, and highlight future research directions. Additionally, mobile monitoring supports public engagement and actionability, allowing communities to advocate for cleaner air and drive behavior change.

Low-cost sensors (LCSs) for measuring air pollution are increasingly being deployed in mobile applications, but questions concerning the quality of the measurements remain unanswered. For example, what is the best way to correct LCS data in a mobile setting? Which factors most significantly contribute to differences between mobile LCS data and those of higher-quality instruments? Can data from LCSs be used to identify hotspots and generate generalizable pollutant concentration maps? To help address these questions, we deployed low-cost PM2.5 sensors (Alphasense OPC-N3) and a research-grade instrument (TSI DustTrak) in a mobile laboratory in Boston, MA, USA. We first collocated these instruments with stationary PM2.5 reference monitors (Teledyne T640) at nearby regulatory sites. Next, using the reference measurements, we developed different models to correct the OPC-N3 and DustTrak measurements and then transferred the corrections to the mobile setting. We observed that more complex correction models appeared to perform better than simpler models in the stationary setting; however, when transferred to the mobile setting, corrected OPC-N3 measurements agreed less well with the corrected DustTrak data. In general, corrections developed by using minute-level collocation measurements transferred better to the mobile setting than corrections developed using hourly-averaged data. Mobile laboratory speed, OPC-N3 orientation relative to the direction of travel, date, hour-of-the-day, and road class together explain a small but significant amount of variation between corrected OPC-N3 and DustTrak measurements during the mobile deployment. Persistent hotspots identified by the OPC-N3s agreed with those identified by the DustTrak. Similarly, maps of PM2.5 distribution produced from the mobile corrected OPC-N3 and DustTrak measurements agreed well. These results suggest that identifying hotspots and developing generalizable maps of PM2.5 are appropriate use-cases for mobile LCS data.

BACKGROUND:Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. OBJECTIVES:We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. METHODS:We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. RESULTS:We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.) VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.) LFN was minimally affected by high wind speeds at either Site. IMPACT STATEMENT:Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.

Low-cost air sensing is changing the paradigm of ambient air quality management research and practices. However, consensus on a structured low-cost sensor calibration and performance evaluation framework is lacking. Our study aims to devise a standardized low-cost sensor calibration protocol and evaluate the performance of various calibration algorithms. Extensive collocation data were collected in stationary and mobile settings in two American cities, New York and Boston. We trained the calibration models using stationary data aggregated at various intervals to examine the performance of several commonly used calibration algorithms described in the literature. Linear models provide consistently satisfactory calibration results, indicating linear responses from the low-cost sensors in our stationary test environment. Its simplicity is recommended for citizen science and education usages. Models that can account for non-linear relationships, especially random forest, perform well and transfer between sensors better than generalized linear regression models for PM2.5 calibration, which should be adopted for regulatory and scientific purposes. Data collected in a mobile validation campaign in Boston were passed through the best-performing calibration models to assess their transferability. The results indicate that models trained with data from a different urban environment and season in the stationary setting did not transfer well to a mobile setting. It is recommended that low-cost sensors should be calibrated more often than suggested in Environmental Protection Agency's air sensor performance evaluation guidelines and used in an environment that is as similar as possible to the calibration environment.

Background: Evidence suggests that exposure to traffic-related air pollution (TRAP) and social stressors can increase inflammation. Given that there are many different markers of TRAP exposure, socio-economic status (SES) and inflammation, analytical approaches can leverage multiple markers to better elucidate associations. In this study, we applied structural equation modeling (SEM) to assess the association between a TRAP construct and a SES construct with an inflammation construct.Methods: This analysis was conducted as part of the Community Assessment of Freeway Exposure and Health (CAFEH) study. Air pollution was characterized using a spatiotemporal model of particle number concentration (PNC) combined with individual participant time-activity patterns (TAA-PNC). TAA-PNC and proximity to highways were considered for a construct of TRAP exposure. Participant demographics on education and income for a socio-economic status construct were assessed via questionnaires. Blood samples were analyzed for high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α receptor II (TNFα), which were considered for the construct for inflammation. We conducted SEM and compared our findings with those obtained using generalized linear models (GLM).Results: Using GLM, TAA-PNC was associated with multiple inflammation biomarkers. An IQR (10,000 particles/cm3) increase of TAA-PNC was associated with a 14% increase in hsCRP in the GLM. During SEM the TRAP construct with the inflammation construct in SEM was twice as large as the associations with any individual inflammation biomarker. SES had an inverse association with inflammation in all models. Using SEM to estimate the indirect effects of SES on inflammation through TRAP construct strengthened confidence in the association of TRAP with inflammation.Conclusion: Our TRAP construct resulted in stronger associations with a combined construct for inflammation than with individual biomarkers, reinforcing the value of statistical approaches that combine multiple, related exposures or outcomes. Our findings are consistent with inflammatory risk from TRAP exposure.

While human mobility plays a crucial role in determining air pollution exposures and health risks, research to-date has assessed risks based solely on residential location. Here we leveraged a database of 130 million workers in the US and published PM2.5 data between 2011-2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures (W) relative to home exposures (H), as well as increasing exposures for non-white and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job-types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities to exposure among these subpopulations. We also quantified the error in assigning workers H, instead of a weighted home-and-work (HW) exposure. We observed that biases in associations between PM2.5 and health impacts by using H instead of HW were highest among urban, younger populations.

Mobility reductions following the COVID-19 pandemic in the United States were higher, and sustained longer, for aviation than ground transportation activity. We evaluate changes in ultrafine particle (UFP, Dp < 100 nm, a marker of fuel-combustion emissions) concentrations at a site near Logan Airport (Boston, Massachusetts) in relation to mobility reductions. Several years of particle number concentration (PNC) data prepandemic [1/2017-9/2018] and during the state-of-emergency (SOE) phase of the pandemic [4/2020-6/2021] were analyzed to assess the emissions reduction impact on PNC, controlling for season and wind direction. Mean PNC was 48% lower during the first three months of the SOE than prepandemic, consistent with 74% lower flight activity and 39% (local)-51% (highway) lower traffic volume. Traffic volume and mean PNC for all wind directions returned to prepandemic levels by 6/2021; however, when the site was downwind from Logan Airport, PNC remained lower than prepandemic levels (by 23%), consistent with lower-than-normal flight activity (44% below prepandemic levels). Our study shows the effect of pandemic-related mobility changes on PNC in a near-airport community, and it distinguishes aviation-related and ground transportation source contributions.

Background and aim: We have shown previously that cardiovascular biomarkers are associated with traffic-related air pollution (TRAP) near highways and that reducing particulate exposure improves blood pressure (BP) in controlled settings. Our aim here is to assess the efficacy of portable in-home high-efficiency particle arrestance (HEPA) air purifiers to reduce indoor concentrations of TRAP and therefore reduce BP and systemic inflammation in a real world, lived-in setting. Methods: We are conducting a randomized crossover trial of air purifiers in homes <200m from a major highway. Participants receive real or sham filtration for one month each separated by a one-month wash out period. We collect peripheral and central BP, as well as blood samples for inflammatory biomarker analysis before and after each one-month period. We also conduct air monitoring and satisfaction interviews with a subset of homes and participants. Results: A sample of 57 participants has been enrolled with recruitment ongoing. Thirty-three percent live within 100m of the highway. The average age of participants is 44 years (range: 30-78 years), 68% are female, and 37% are racial/ethnic minorities. The mean (SD) baseline peripheral and central systolic BP are 123 (14) mmHg and 113 (13) mmHg, respectively. Indoor and outdoor PNC measurements indicate that median concentrations were 40-60% lower during HEPA than SHAM and during HEPA use indoor concentrations were 60-70% lower than outdoors. Data on electricity use and quantitative and qualitative feedback suggest air purifiers were on the medium setting most of the time. Conclusions: Approximately one-third of the way to our recruitment goal we have good acceptance and compliance with the intervention, quality data and indications that the air purifiers are reducing TRAP as intended. An interim analysis of health outcomes should be possible in a year.

Rationale: Ambient ultrafine particles (UFPs; with an aerodynamic diameter < 0.1 μm) may exert greater toxicity than other pollution components because of their enhanced oxidative capacity and ability to translocate systemically. Studies examining associations between prenatal UFP exposure and childhood asthma remain sparse. Objectives: We used daily UFP exposure estimates to identify windows of susceptibility of prenatal UFP exposure related to asthma in children, accounting for sex-specific effects. Methods: Analyses included 376 mother-child dyads followed since pregnancy. Daily UFP exposure during pregnancy was estimated by using a spatiotemporally resolved particle number concentration prediction model. Bayesian distributed lag interaction models were used to identify windows of susceptibility for UFP exposure and examine whether effect estimates varied by sex. Incident asthma was determined at the first report of asthma (3.6 ± 3.2 yr). Covariates included maternal age, education, race, and obesity; child sex; nitrogen dioxide (NO2) and temperature averaged over gestation; and postnatal UFP exposure. Measurements and Main Results: Women were 37.8% Black and 43.9% Hispanic, with 52.9% reporting having an education at the high school level or lower; 18.4% of children developed asthma. The cumulative odds ratio (95% confidence interval) for incident asthma per doubling of the UFP exposure concentration across pregnancy was 4.28 (1.41-15.7), impacting males and females similarly. Bayesian distributed lag interaction models indicated sex differences in the windows of susceptibility, with the highest risk of asthma seen in females exposed to higher UFP concentrations during late pregnancy. Conclusions: Prenatal UFP exposure was associated with asthma development in children, independent of correlated ambient NO2 and temperature. Findings will benefit future research and policy-makers who are considering appropriate regulations to reduce the adverse effects of UFPs on child respiratory health.

Exposure to traffic-related air pollution (TRAP) may contribute to increased prevalence of hypertension and elevated blood pressure (BP) for residents of near-highway neighborhoods. Relatively few studies have investigated the effects of reducing TRAP exposure on short-term changes in BP. We assessed whether reducing indoor TRAP concentrations by using stand-alone high-efficiency particulate arrestance (HEPA) filters and limiting infiltration through doors and windows effectively prevented acute (ie, over a span of hours) increases in BP. Using a 3-period crossover design, 77 participants were randomized to attend three 2-hour-long exposure sessions separated by 1-week washout periods. Each participant was exposed to high, medium, and low TRAP concentrations in a room near an interstate highway. Particle number concentrations, black carbon concentrations, and temperature were monitored continuously. Systolic BP (SBP), diastolic BP, and heart rate were measured every 10 minutes. Outcomes were analyzed with a linear mixed model. The primary outcome was the change in SBP from 20 minutes from the start of exposure. SBP increased with exposure duration, and the amount of increase was related to the magnitude of exposure. The mean change in SBP was 0.6 mm Hg for low exposure (mean particle number and black carbon concentrations, 2500 particles/cm(3) and 149 ng/m(3)), 1.3 mm Hg for medium exposure (mean particle number and black carbon concentrations, 11 000 particles/cm(3) and 409 ng/m(3)), and 2.8 mm Hg for high exposure (mean particle number and black carbon concentrations, 30 000 particles/cm(3) and 826 ng/m(3); linear trend P=0.019). There were no statistically significant differences in the secondary outcomes, diastolic BP, or heart rate. In conclusion, reducing indoor concentrations of TRAP was effective in preventing acute increases in SBP.

Background: Near highway residents are exposed to elevated levels of traffic-related air pollution (TRAP), including ultrafine particles, which are associated with adverse health effects. The efficacy of using in-home air filtration units that reduce exposure and potentially yield health benefits has not been tested in a randomized controlled trial. Methods: We will conduct a randomized double-blind crossover trial of portable air filtration units for 200 adults 30 years and older who live in near-highway homes in Somerville, MA, USA. We will recruit participants from 172 households. The intervention periods will be one month of true or sham filtration, followed by a one-month wash out period and then a month of the alternate intervention. The primary health outcome will be systolic blood pressure (BP); secondary outcome measures will include diastolic and central BP, C-Reactive Protein (CRP) and D-dimer. Reasons for success or failure of the intervention will be evaluated in a subset of homes using indoor/outdoor monitoring for particulate pollution, personal monitoring, size and composition of particulate pollution, tracking of time spent in the room with the filter, and interviews for qualitative feedback. Results: This trial has begun recruitment and is expected to take 2-3 years to be completed. Recruitment has been particularly challenging because of additional precautions required by the COVID-19 pandemic. Discussion: This study has the potential to shed light on the value of using portable air filtration in homes close to highways to reduce exposure to TRAP and whether doing so has benefits for cardiovascular health.

Exposure to traffic-related air pollution (TRAP) is associated with adverse health outcomes, including hypertension and elevated blood pressure; however, the nature of the exposure-response functions for these associations is not well established. In a previous three-exposure, three-period crossover trial we showed that using portable HEPA filters to reduce indoor infiltration of TRAP was significantly effective in preventing short-term average increases in systolic blood pressure (SBP). We measured particle number concentrations (PNC), black carbon (BC) concentrations, and room temperatures continuously during the trial, and we measured participant blood pressures every 10 minutes. We then examined the relationship between SBP and PNC. A total of 1321 SBPs arising from 76 participants were analyzed. The average age of the participants was 60 years, 79% were female, 77% were Asian, and 17% were White. None had serious health conditions. The average SBP was 120.0 mmHg and the average particle number concentration (PNC) was 13,000 particles/cm3, ranging from 860 to 99,000 particles/cm3. A linear mixed model was used to fit a curvilinear relationship between SBP and PNC. The model included BC concentrations and room temperatures as covariates, as well as a random intercept to account for the interdependence of multiple SBPs recorded from each participant. The resulting adjusted model, yielding statistically significant regression coefficients (p < 0.001), can be written as: SBP = 117.54 + 1.22 (PNC/5000) - 0.059 (PNC/5000)2, which implies that SBP increases by 1.22 mmHg for every 5000 particles/cm3 increase in PNC but tapers off at higher levels of PNC. For example, at 5000 particles/cm3 the average SBP is expected to be 118.7 mmHg, but it increases to 121.0 mmHg at 15,000 particles/cm3 and to 124.5 mmHg at 30,000 particles/cm3. The results of the present study suggest an exposure-response function for SBP and PNC.