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Objectives Approximately 2 billion workers globally are employed in informal settings, which are characterized by substantial risk from hazardous exposures and varying job tasks and schedules. Existing methods for identifying occupational hazards must be adapted for unregulated and challenging work environments. We designed and applied a method for objectively deriving time-activity patterns from wearable camera data and matched images with continuous measurements of personal inhalation exposure to size-specific particulate matter (PM) among workers at an informal electronic-waste (e-waste) recovery site. Methods One hundred and forty-two workers at the Agbogbloshie e-waste site in Accra, Ghana, wore sampling backpacks equipped with wearable cameras and real-time particle monitors during a total of 171 shifts. Self-reported recall of time-activity (30-min resolution) was collected during the end of shift interviews. Images (N = 35,588) and simultaneously measured PM2.5 were collected each minute and processed to identify activities established through worker interviews, observation, and existing literature. Descriptive statistics were generated for activity types, frequencies, and associated PM2.5 exposures. A kappa statistic measured agreement between self-reported and image-based time-activity data. Results Based on image-based time-activity patterns, workers primarily dismantled, sorted/loaded, burned, and transported e-waste materials for metal recovery with high variability in activity duration. Image-based and self-reported time-activity data had poor agreement (kappa = 0.17). Most measured exposures (90%) exceeded the World Health Organization (WHO) 24-h ambient PM2.5 target of 25 µg m−3. The average on-site PM2.5 was 81 µg m−3 (SD: 94). PM2.5 levels were highest during burning, sorting/loading and dismantling (203, 89, 83 µg m−3, respectively). PM2.5 exposure during long periods of non-work-related activities also exceeded the WHO standard in 88% of measured data. Conclusions In complex, informal work environments, wearable cameras can improve occupational exposure assessments and, in conjunction with monitoring equipment, identify activities associated with high exposures to workplace hazards by providing high-resolution time-activity data.

Molybdenum (Mo) is a key cofactor in enzymes used for nitrogen (N) fixation and nitrate reduction, and the low availability of Mo can constrain N inputs, affecting ecosystem productivity. Natural atmospheric Mo aerosolization and deposition from sources such as desert dust, sea‐salt spray, and volcanoes can affect ecosystem function across long timescales, but anthropogenic activities such as combustion, motor vehicles, and agricultural dust have accelerated the natural Mo cycle. Here we combined a synthesis of global atmospheric concentration observations and modeling to identify and estimate anthropogenic sources of atmospheric Mo. To project the impact of atmospheric Mo on terrestrial ecosystems, we synthesized soil Mo data and estimated the global distribution of soil Mo using two approaches to calculate turnover times. We estimated global emissions of atmospheric Mo in aerosols (

Most epidemiologic studies fail to capture the impact of spatiotemporal fluctuations in traffic on exposure to traffic-related air pollutants in the near-road population. Using a case-crossover design and the Research LINE source (R-LINE) dispersion model with spatiotemporally resolved highway traffic data, we quantified associations between primary pollutants generated by highway traffic—particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5), oxides of nitrogen (NOx), and black carbon (BC)—and daily nonaccidental, respiratory, cardiovascular, and cerebrovascular mortality among persons who had resided within 1 km (0.6 mile) of major highways in the Puget Sound area of Washington State between 2009 and 2013. We estimated these associations using conditional logistic regression, adjusting for time-varying covariates. Although highly resolved modeled concentrations of PM2.5, NOx, and BC from highway traffic in the hours before death were used, we found no evidence of an association between mortality and the preceding 24-hour average PM2.5 exposure (odds ratio = 0.99, 95% confidence interval: 0.96, 1.02) or exposure during shorter averaging periods. This work did not support the hypothesis that mortality risk was meaningfully higher with greater exposures to PM2.5, NOx, and BC from highways in near-road populations, though we did incorporate a novel approach to estimate exposure to traffic-generated air pollution based on detailed traffic congestion data.

The intake fraction (iF) expresses population exposure resulting from pollutant emissions. City-wide iFs estimated using simple one-compartment models, which have been used in a number of previous studies, have significant uncertainties and do not capture the intra-urban variation in exposure that is important for estimating health effects associated with traffic-related air pollutants. We present a novel and efficient approach for developing spatially-resolved iF estimates using dispersion modeling for near-road exposures that accounts for the spatial and temporal variation in meteorology, emissions and the population living and working near major roads. Using the new approach, iF estimates are developed for emissions of traffic-related fine particulate matter (PM2.5) in Lisbon, Portugal, and compared to estimates from a one-compartment model. Both methods use local meteorological and population data and represent exposures for a total of 2.8 million people. The new method produces an overall iF value of 16.4 ppm for the Lisbon metropolitan area, over twice that of the one-compartment model (8.1 ppm). Most of the exposure (12.0 ppm) occurs for the subset of the population (1.0 million people) living or working within 500 m of highways and major arterials. The iF for the remainder of the population (1.8 million people) is only 4.3 ppm. The spatially-resolved iF estimate accounts for high concentration areas, which can be densely populated, and accounts for much or most of the exposure from traffic-related emissions. The new method is computationally efficient and can improve estimates of exposure and health impacts occurring in urban areas, leading to more effective urban and transportation planning decisions to mitigate impacts.

PURPOSE: Exposures of nail salon technicians have received attention due to the potentially toxic materials used in nail products, which include volatile organic compounds (VOCs) such as formaldehyde and methyl methacrylate (MMA). This study characterized area and personal concentrations and other indoor air parameters in 17 nail salons in fall and winter seasons in three areas of Michigan. METHODS: VOC samples were analyzed using thermal desorption, gas chromatography and mass spectroscopy, and the VOC composition of 35 nail products (e.g., polish, top coat, base coat) was measured using headspace sampling. Ventilation rates were derived using CO2 concentrations, occupancy and building information, and VOC sources were apportioned by a novel application of chemical mass balance models. RESULTS: We detected ethyl acetate, propyl acetate, butyl acetate, MMA, n-heptane and toluene in most salons, and benzene, d-limonene, formaldehyde, and ethyl methacrylate (EMA) in some salons. While MMA was not measured in the consumer and professional products, and the use of pure MMA in salons has been not been permitted since the 1970’s, MMA was found in air at concentrations from 100 to 36,000 μg/m3 in 15 of 17 salons; thus, its use appears to be commonplace in the industry. Personal measurements, representing exposures to workers and clients, were about twice those of the area measurements for many VOCs. CONCLUSION: This study identifies the products responsible for emissions, shows the widespread presence of MMA, and documents low ventilation rates in some salons. It also demonstrates that “informal” short-term sampling approaches can evaluate chemical exposures in nail salons, providing measurements that can be used to protect a potentially susceptible and vulnerable population. Additional controls, including restrictions on the VOC compositions and improved ventilation, can reduce exposures to salon workers and clients.

PM 2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM 2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM 2.5 in the two cities declined by 3.2–3.6%/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2–5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM 2.5 data reveals that source contributions to PM 2.5 varied as PM 2.5 concentrations decreased, and that the fraction of PM 2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM 2.5 concentrations in urban areas.

Localized variations in traffic volume and speed can influence air pollutant emissions and corresponding concentrations in nearby communities, but most studies have utilized only aggregated traffic activity data. In this study, we compared the estimated influence of highway traffic activity on concentrations of primary oxides of nitrogen (NOx) and fine particulate matter (PM2.5) in communities near highways using a dispersion model informed by highly spatiotemporally-resolved variations of traffic volume and flow compared to the use of Annual Average Daily Traffic (AADT) data at a few locations. We used two sources of traffic activity data on 500 half-mile roadway segments on the five major highways in the Washington State Puget Sound during 2013. The first consisted of vehicle counts available every half-mile and 5 min; the second was traffic information (e.g., AADT) aggregated across the year and roadway network. Using the Motor Vehicle Emissions Simulator (MOVES) and the Research Line source dispersion model (RLINE), we modeled hourly concentrations of primary NOx and PM2.5 generated by highway traffic at nearly 4000 residences within 1 km of major highways. These concentrations were aggregated to daily and annual average concentrations, which were compared by input data source. At most locations, concentrations of primary NOx and PM2.5 modeled using the resolved traffic data had similar spatial and temporal distributions to concentrations predicted using the AADT data. However, several areas showed large differences. For example, 25% of residences within 150 m of a highway had concentrations that differed by more than 19% (8 ppb) for NOx and 32% (0.7 μg/m3) for PM2.5, and the AADT data consistently predicted lower concentrations than the resolved traffic data. Our findings indicate that temporal and spatial variation in traffic patterns can result in complex spatiotemporal variations of air pollutant concentrations that can be captured with the use of dispersion modeling with the appropriate inputs. The use of spatiotemporally resolved traffic activity data can improve exposure estimates and help reduce exposure measurement error in epidemiological studies, especially in communities near highly congested highways.

Efforts to reduce energy consumption and greenhouse gas emissions in the United States have led to widespread interest in energy-efficient retrofits in the residential sector. Weatherization retrofits, such as air sealing and insulation, lower residential energy consumption, resulting in reduced fuel and electricity-related emissions, improved ambient air quality, and avoided climate and health impacts. However, retrofits without adequate ventilation may worsen indoor air quality (IAQ) and lead to adverse health effects and costs that offset anticipated economic benefits. Building energy management and climate action plans often omit these potential consequences. This study presents a novel framework that quantifies health and energy costs and benefits related to residential energy-efficient retrofits. Energy savings from reduced consumption were estimated using historical energy prices. Reductions in ambient emissions of fine particulate matter (PM2.5) precursors were translated to avoided health impacts using health damage functions. Indoor PM2.5-related health impacts were estimated by applying PM2.5 concentration-response functions to changes in IAQ. Health impacts were monetized using health impact assessment techniques and compared to retrofit-related savings. For a modeled gas-heated midrise multifamily building in Boston, MA, weatherization retrofits without ventilation or filtration retrofits led to household health costs that far exceeded energy savings and population-level health savings, given increases in indoor PM2.5 concentrations (3.1 μg/m3 and 20.4 μg/m3 for standard and high-performance retrofits, respectively). In contrast, weatherization coupled with ventilation and filtration retrofits resulted in large savings. Overall, this study reinforces the need to quantify residential health impacts in assessments of energy-efficient retrofits.

Background: Traffic varies dramatically by time and place, within and between highways. These variations likely influence the distribution of traffic-generated pollutants in nearby communities but ...

Background: A large proportion of the US population lives and works near highways where levels of traffic-generated air pollutants are the highest. While these pollutants have been linked to advers...

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (