Christian L'ORange

Exposure to hazardous air pollutants (HAPs) is associated with an elevated cancer risk, immune system damage, respiratory and reproductive effects, and other adverse health outcomes. Regulatory HAP monitoring is costly, spatially limited, and vulnerable to power outages and extreme weather, which often coincide with periods when monitoring is most needed. This work describes the development of a lower-cost, battery-powered, and community-based sampling platform for HAPs. The sampler supports reliable measurements by recording key quality assurance/control parameters including time, location, battery power, sample flow rate, temperature, humidity, and pressure. We partnered with and trained community volunteers to deploy the platform for ambient formaldehyde sampling following the EPA Method TO-11A in Houston, Texas. Over 6 weeks, 28 fully validated samples were collected, with formaldehyde concentrations ranging from 1.27 to 7.30 ppb, consistent with concentrations reported by nearby regulatory monitors. Noncancer effects are unlikely at the concentrations and sampling durations observed, although contributions to cancer risk are non-negligible. With minor technological improvements and refined training and communication protocols, this platform shows promise for generating reliable, community-gathered HAP data with greater spatial coverage and lower cost than established monitoring approaches, particularly in communities underserved by existing regulatory networks.

Household air pollution (HAP) contributes to the global cardiovascular disease burden. Early life exposures may impact future disease risk; however, evidence of HAP’s impact on blood pressure (BP) among children in low-resource settings is limited. We assessed baseline cross-sectional associations between personal exposure to fine particulate matter (PM _2.5 ) and black carbon (BC) and children’s (8–15 years) systolic and diastolic BP (SBP, DBP) from the Sustainable Household Energy Adoption in Rwanda study. We enrolled 626 households in rural Eastern Rwanda that used traditional biomass fuels for cooking. Children wore Ultrasonic Personal Aerosol Samplers for 48 h monitoring of personal exposure, followed by BP measurement using standard protocols. Multiple linear regression was used to characterize associations between PM _2.5 and BC exposure and BP in separate models; effect modification by age and sex was evaluated. Among 622 children (mean age = 11.8 years, standard deviation [SD] = 2.1; males = 303, females = 319), the average SBP and DBP were 105.8 mmHg (SD = 10.0) and 68.3 mmHg (SD = 7.6), respectively. BP percentiles were higher than those in a US reference population (e.g. 59.0% and 85.5% had SBP and DBP percentiles >50th, respectively; 24% were classified as having elevated BP and/or were hypertensive, defined as ⩾90th percentile). The median 48 h PM _2.5 and BC concentrations were 192.9 µ g m ⁻³ (25th percentile [ Q 1] = 119.7, 75th percentile [ Q 3] = 336.0) and 9.85 µ g m ⁻³ ( Q 1 = 6.56, Q 3 = 13.23), respectively. We did not observe evidence of associations between HAP and BP levels (e.g. PM _2.5 –SBP = 0.51 mmHg per interquartile range [IQR, 211.7 μ g m ⁻³ ] increase, 95% confidence interval: −0.46, 1.49). We did not observe clear evidence of effect modification. Exposures well above international health-based guidelines may have limited our ability to observe associations if the true exposure–response is flat in that part of the global exposure continuum. Importantly, our BP data suggest an elevated cardiovascular disease burden among children in this low-resource setting compared to in the US, demonstrating a need for more research and the development of more appropriate reference data in these settings. ClinicalTrials.gov Identifier: NCT05668624.

The volatility of individual species is a fundamental property governing the gas-particle partitioning of organic aerosols. However, in complex organic mixtures, a compound’s apparent volatility may differ from its intrinsic volatility, depending on the matrix’s chemical composition. Herein, we systematically investigate component-resolved, mixture-specific volatility for more than 1,500 individual species across 33 proxies and chemically complex mixtures representative of selected organic aerosol types. The results show that species in simplified proxies and reference mixtures with limited components follow a higher-volatility trend that approaches their intrinsic values, whereas species present in ambient and biomass-burning organic aerosols exhibit the opposite behavior, with systematically reduced apparent volatility attributable to matrix effects. Using levoglucosan (LG), a representative biomass-burning tracer, as an illustrative example, we find that its volatility in complex organic mixtures is reduced by 1 to 4 orders of magnitude relative to its intrinsic volatility in pure LG. This pronounced reduction underscores strong matrix effects that substantially suppress the apparent volatility of individual species in mixed systems. Machine-learning analysis further indicates that mixture-dependent molecular metrics are more predictive of apparent volatility than compound-specific molecular properties that define intrinsic volatility. Collectively, these findings highlight the critical role of intermolecular interactions in governing gas-particle partitioning in multicomponent systems. This study provides strong evidence that matrix effects significantly influence the apparent volatility of individual species in aerosols and other environmental organic mixtures and should be explicitly considered in volatility prediction frameworks and aerosol transport models.

The wildland-urban interface (WUI) has grown in recent decades at the same time as wildfires have expanded in range and scope. Fires at the WUI are therefore more common, and structural materials make up more of the wildfire fuel mass. While emissions from biomass fires are fairly well understood, volatile organic compound (VOC) emissions from structural fires are less constrained. In this study, we perform measurements of VOC emissions from small-scale laboratory burns of 18 different structural materials across 78 experiments using a Vocus proton-transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) to better understand these unique emissions. We calculate emission factors for 73 VOCs across all materials, including aromatics and polycyclic aromatic hydrocarbons (PAHs). We compare the emissions from both flaming and pyrolysis, which separate the processes of direct release of VOCs from combustion formation. Mass spectra comparisons were used to qualitatively highlight high-emission compounds across materials and identify notable emissions (e.g., nylon monomers) and potential tracers (e.g., halogen species) for WUI fires. Using these data, the emissions from a whole-house fire were compared with those from an equivalent mass of wood, and we found that some aromatic and nitrile species may be suitable WUI fire tracers.

Few studies examine health effects of metals in ambient fine particulate matter (PM_2.5), as measurements of elemental composition are sparse. To facilitate intraurban studies in Denver, Colorado, we developed land use regression models for seven speciescopper (Cu), iron (Fe), titanium (Ti), zinc (Zn), potassium (K), calcium (Ca), and magnesium (Mg). As part of the Healthy Start Cohort study, we collected filter-based PM_2.5 using personal air samplers at 67 locations across Denver. Sample collection occurred from May 2018 through March 2019, accounting for all meteorological seasons. Exposure models were informed by 83 geospatial covariates, with traffic-related predictors as the strongest and most consistent across models. Model performance was evaluated using 10-fold cross validation and overall, varied by sampling campaign and season, with R ² values ranging from 0 to 0.63. At best, our model predicts Cu and Fe during fall (R ² = 0.56 and 0.63, respectively); whereas it fails to capture species unrelated to traffic year-round (R ² < 0.40). This highlights the influence of missing predictors (e.g., wildfire smoke, atmospheric transport and other meteorological factors) on PM_2.5 concentrations and spatial gradients. Despite limitations, resulting models enable estimation of intraurban metal exposures and support future analyses of long-term health impacts in Denver.

With ∼50 million houses in the US located at the wildland-urban interface (WUI) and wildfire activity on the rise, there is a concern that structure fires might be a key source of air pollutants in WUI regions. We report on structure fire experiments performed as part of the Burning Homes And Structural MAterials (BHASMA) project. More than 70 small-scale experiments were performed on 19 different fuels and fuel complexes, representing lumber, processed wood, insulation, carpet, roofing, electrical sheathing, and flooring. More than 20 large-scale experiments were performed on different sizes and packing densities of a fuel test crib composed of lumber, processed wood, gypsum board, and plastics. Emission factors (EFs) for CO₂, CO, and CH₄ at the same combustion efficiency (CE) did not vary strongly with fuel category or fire scale and were generally consistent with emissions reported in the literature for biomass burning. EFs for PM_2.5, organic aerosol, and soot for non-wood-based fuels under flaming conditions were higher than those from wood-based fuels, with soot emissions being 10-fold higher. Overall, we find that structure fires can be similar to vegetation fires for some pollutants (e.g., CO) but not all pollutants (e.g., soot). Our work contributes to the understanding of emissions and environmental impacts from WUI fires.

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM_2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM_2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m³) and secondary organics (~3–5 μg/m³) contributed to PM_2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM_2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m³).

Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure.

Exposure to fine particulate matter (PM_2.5) from solid-fuel combustion is a major determinant of global morbidity and mortality. However, variations in exposure remain uncertain across many high-risk populations. This work describes personal PM_2.5 exposures among household members (adult men, adult women, and children) in rural sub-Saharan Africa, where biomass fuel is the primary household energy source. We assessed personal PM_2.5 exposures using wearable monitors that combined real-time sensing, time-integrated (gravimetric filter) sampling, and continuous location-activity tracking over 48 h periods. A total of 1280 samples were collected from 579 Rwandan homes over a 15-month period comprising 304 men (aged 23-84 years), 495 women (aged 20-84 years), and 481 children (aged 8-17 years). Linear mixed models, controlling for household, suggested that children were exposed to 14% (CI: 6, 22%) more PM_2.5 than their mothers and 100% (CI: 85, 117%) more than their fathers. Spatiotemporal analyses, aggregated into various microenvironments (e.g., home, school, transit, agricultural fieldwork), reveal that children bore a disproportionate exposure burden from in-home cooking activities compared with their parents. Results from this work indicate that interventions for household energy systems, in conjunction with familial lifestyle and behavior modifications, are necessary to reduce personal PM_2.5 exposures in rural Rwanda, especially among children.

Maintaining long-term participation in citizen science projects is challenging; thus, it is important for project developers to use effective techniques to motivate participants. One approach is to incorporate anthropomorphism (ascribing humanlike qualities to a non-human agent) when designing and deploying technology. In a quasi-experimental study conducted as part of the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) citizen science project, we investigated the relationship between anthropomorphism and motivation to participate in citizen science. Findings showed partial support for the relationship between anthropomorphism and motivation.