Cumulative Exposures to Environmental and Socioeconomic Risk Factors in Milwaukee County, Wisconsin.

The environmental justice literature demonstrates consistently that low-income and minority communities are disproportionately exposed to environmental hazards. In this case study, we examined cumulative multipollutant, multidomain, and multimatrix environmental exposures in Milwaukee County, Wisconsin for the year 2015. We identified spatial hot spots in Milwaukee County both individually (using local Moran's I) and through clusters (using K-means clustering) across a profile of environmental pollutants that span regulatory domains and matrices of exposure, as well as socioeconomic indicators. The cluster with the highest exposures within the urban area was largely characterized by low socioeconomic status and an overrepresentation of the Non-Hispanic Black population relative to the county as a whole. In this cluster, average pollutant concentrations were equivalent to the 78th percentile in county-level blood lead levels, 67th percentile in county-level NO2, 79th percentile in county-level CO, and 78th percentile in county-level air toxics. Simultaneously, this cluster had an average equivalent to the 62nd percentile in county-level unemployment, 70th percentile in county-level population rate lacking a high school diploma, 73rd percentile in county-level poverty rate, and 28th percentile in county-level median household income. The spatial patterns of pollutant exposure and SES indicators suggested that these disparities were not random but were instead structured by socioeconomic and racial factors. Our case study, which combines environmental pollutant exposures, sociodemographic data, and clustering analysis, provides a roadmap to identify and target overburdened communities for interventions that reduce environmental exposures and consequently improve public health.

AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds.

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution.

Particulate matter (PM) air pollution is a major health hazard. The health effects of PM are closely linked to particle size, which governs its deposition in (and penetration through) the respiratory tract. In recent years, low-cost sensors that report particle concentrations for multiple-sized fractions (PM1.0, PM2.5, PM10) have proliferated in everyday use and scientific research. However, knowledge of how well these sensors perform across the full range of reported particle size fractions is limited. Unfortunately, erroneous particle size data can lead to spurious conclusions about exposure, misguided interventions, and ineffectual policy decisions. We assessed the linearity, bias, and precision of three low-cost sensor models, as a function of PM size fraction, in an urban setting. Contrary to manufacturers' claims, sensors are only accurate for the smallest size fraction (PM1). The PM1.0-2.5 and PM2.5-10 size fractions had large bias, noise, and uncertainty. These results demonstrate that low-cost aerosol sensors (1) cannot discriminate particle size accurately and (2) only report linear and precise measures of aerosol concentration in the accumulation mode size range (i.e., between 0.1 and 1 µm). We recommend that crowdsourced air quality monitoring networks stop reporting coarse (PM2.5-10) mode and PM10 mass concentrations from these sensors.

Higher mold levels found in in the deteriorated housing in the Sun Valley neighborhood of Denver, Colorado compared to other Denver housing and higher rates of health insurance claims for some diseases documented for the Sun Valley residents compared to other Denver residents.

The Sun Valley Homes public housing in Denver, Colorado (CO) will be replaced because of its deteriorated condition. Our goal was to document the mold contamination and particulate matter (PM2.5) concentrations in Sun Valley homes and the circulatory and respiratory health of Sun Valley compared to all Denver residents (total 2761 and 1,049,046, respectively) based on insurance claims data for 2015 to 2019. Mold contamination in Sun Valley homes (n = 49) was quantified using the Environmental Relative Moldiness Index (ERMI) scale. Indoor PM2.5 concentrations were measured in Sun Valley homes (n = 11) using time-integrated, filter-based samples and quantified using gravimetric analysis. Outdoor PM2.5 concentrations data were obtained from a near-by United States Environmental Protection Agency monitoring station. In Sun Valley homes, the average ERMI value was 5.25 compared to -1.25 for other Denver homes. The PM2.5 median concentration inside Sun Valley homes was 7.6 µg/m3 (interquartile range - 6.4 µg/m3). The ratio of indoor to outdoor concentrations of PM2.5 was 2.3 (interquartile range - 1.5). In the last five years, ischemic heart disease was significantly more likely for Denver compared to Sun Valley residents. However, acute upper respiratory infections, chronic lower respiratory diseases and asthma were all significantly more likely for Sun Valley than Denver residents. Since the process of replacing and occupying the new housing will take several years, the next phase of the study will not occur until that process is complete.

Cross-sectional study of household solid fuel use and renal function in older adults in China.

BACKGROUND: Emerging evidence links outdoor air pollution and declined renal function but the relationship between household air pollution and renal function is not well understood. METHODS: Using cross-sectional data from the multi-provincial INTERMAP-China Prospective Study, we collected blood samples and questionnaire information on stove use and socio-demographic factors. We calculated estimated glomerular filtration rate (eGFR) from serum creatinine to assess renal function. Participants with eGFR <60 mL/min per 1.73 m2 were defined as having chronic kidney disease (CKD) in this analysis. Generalized estimating equations were used to estimate the association of household fuel with renal function and prevalent CKD in models adjusting for confounders. RESULTS: Among the 646 enrolled adults (40-79y; 56% female), one-third exclusively used clean fuel (gas and electric) cookstoves and 11% of northern China participants (n = 49 of 434) used only clean fuel heaters, whereas the rest used solid fuel. In multivariable models, use of solid fuel cookstoves was associated with 0.17 ml/min/1.73 m2 (95% CI: -0.30, 0.64) higher eGFR and 19% (0.86, 1.64) higher prevalence of CKD than exclusive clean fuel use. Greater intensity of solid fuel use was associated with 0.25 ml/min/1.73 m2 (-0.71, 0.21) lower eGFR per 5 stove-use years, though the confidence intervals included the null, while greater current intensity of indoor solid fuel use was associated with 1.02 (1.00, 1.04) higher prevalent CKD per 100 stove-use days per year. Larger associations between current solid fuel use and intensity of use with lower eGFR and prevalent CKD were observed among participants in southern China, those with hypertension or diabetes (eGFR only), and females (CKD only), through these groups had small sample sizes and some confidence intervals included the null. CONCLUSION: We found inconsistent evidence associating household solid fuel use and renal function in this cross-sectional study of peri-urban Chinese adults.

Effects of indoor and outdoor temperatures on blood pressure and central hemodynamics in a wintertime longitudinal study of Chinese adults.

OBJECTIVES: We aimed to estimate the effects of indoor and outdoor temperature on wintertime blood pressure (BP) among peri-urban Beijing adults. METHODS: We enrolled 1279 adults (ages: 40-89 years) and conducted measurements in two winter campaigns in 2018-2019 and 2019-2020. Study staff traveled to participant homes to administer a questionnaire and measure brachial and central BP. Indoor temperature was measured in the 5 min prior to BP measurement. Outdoor temperature was estimated from regional meteorological stations. We used multivariable mixed-effects regression models to estimate the within-individual and between-individual effects of indoor and outdoor temperatures on BP. RESULTS: Indoor and outdoor temperatures ranged from 0.0 to 28 °C and -14.3 to 6.4 °C, respectively. In adjusted models, a 1 °C increase in indoor temperature was associated with decreased SBP [-0.4 mmHg, 95% confidence interval (CI): -0.7 to -0.1 (between-individual; brachial and central BP); -0.5 mmHg, 95% CI: -0.8 to -0.2 (within-individual, brachial BP); -0.4 mmHg, 95% CI: -0.7 to -0.2 (within-individual, central BP)], DBP [-0.2 mmHg, 95% CI:-0.4 to -0.03 (between-individual); -0.3 mmHg, 95% CI: -0.5 to -0.04 (within-individual)], and within-individual pulse pressure [-0.2 mmHg, 95% CI: -0.4 to -0.04 (central); -0.3 mmHg, 95% CI: -0.4 to -0.1 (brachial)]. Between-individual SBP estimates were larger among participants with hypertension. There was no evidence of an effect of outdoor temperature on BP. CONCLUSION: Our results support previous findings of inverse associations between indoor temperature and BP but contrast with prior evidence of an inverse relationship with outdoor temperature. Wintertime home heating may be a population-wide intervention strategy for high BP and cardiovascular disease in China.

Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown.

The coronavirus (COVID-19) lockdown in China is thought to have reduced air pollution emissions due to reduced human mobility and economic activities. Few studies have assessed the impacts of COVID-19 on community and indoor air quality in environments with diverse socioeconomic and household energy use patterns. The main goal of this study was to evaluate whether indoor and community air pollution differed before, during, and after the COVID-19 lockdown in homes with different energy use patterns. Using calibrated real-time PM2.5 sensors, we measured indoor and community air quality in 147 homes from 30 villages in Beijing over 4 months including periods before, during, and after the COVID-19 lockdown. Community pollution was higher during the lockdown (61 ± 47 µg/m3 ) compared with before (45 ± 35 µg/m3 , p < 0.001) and after (47 ± 37 µg/m3 , p < 0.001) the lockdown. However, we did not observe significantly increased indoor PM2.5 during the COVID-19 lockdown. Indoor-generated PM2.5 in homes using clean energy for heating without smokers was the lowest compared with those using solid fuel with/without smokers, implying air pollutant emissions are reduced in homes using clean energy. Indoor air quality may not have been impacted by the COVID-19 lockdown in rural settings in China and appeared to be more impacted by the household energy choice and indoor smoking than the COVID-19 lockdown. As clean energy transitions occurred in rural households in northern China, our work highlights the importance of understanding multiple possible indoor sources to interpret the impacts of interventions, intended or otherwise.

Detailed Investigation of the Contribution of Gas-Phase Air Contaminants to Exposure Risk during Indoor Activities.

Analytical capabilities in atmospheric chemistry provide new opportunities to investigate indoor air. HOMEChem was a chemically comprehensive indoor field campaign designed to investigate how common activities, such as cooking and cleaning, impacted indoor air in a test home. We combined gas-phase chemical data of all compounds, excluding those with concentrations <1 ppt, with established databases of health effect thresholds to evaluate potential risks associated with gas-phase air contaminants and indoor activities. The chemical composition of indoor air is distinct from outdoor air, with gaseous compounds present at higher levels and greater diversity─and thus greater predicted hazard quotients─indoors than outdoors. Common household activities like cooking and cleaning induce rapid changes in indoor air composition, raising levels of multiple compounds with high risk quotients. The HOMEChem data highlight how strongly human activities influence the air we breathe in the built environment, increasing the health risk associated with exposure to air contaminants.

Socioeconomic and Demographic Associations with Wintertime Air Pollution Exposures at Household, Community, and District Scales in Rural Beijing, China.

The Chinese government implemented a national household energy transition program that replaced residential coal heating stoves with electricity-powered heat pumps for space heating in northern China. As part of a baseline assessment of the program, this study investigated variability in personal air pollution exposures within villages and between villages and evaluated exposure patterns by sociodemographic factors. We randomly recruited 446 participants in 50 villages in four districts in rural Beijing and measured 24 h personal exposures to fine particulate matter (PM2.5) and black carbon (BC). The geometric mean personal exposure to PM2.5 and BC was 72 and 2.5 µg/m3, respectively. The variability in PM2.5 and BC exposures was greater within villages than between villages. Study participants who used traditional stoves as their dominant source of space heating were exposed to the highest levels of PM2.5 and BC. Wealthier households tended to burn more coal for space heating, whereas less wealthy households used more biomass. PM2.5 and BC exposures were almost uniformly distributed by socioeconomic status. Future work that combines these results with PM2.5 chemical composition analysis will shed light on whether air pollution source contributors (e.g., industrial, traffic, and household solid fuel burning) follow similar distributions.

Household air pollution from solid fuel use as a dose-dependent risk factor for cognitive impairment in northern China.

The relationship between exposure to household air pollution (HAP) from solid fuel use and cognition remains poorly understood. Among 401 older adults in peri-urban northern China enrolled in the INTERMAP-China Prospective Study, we estimated the associations between exposure to HAP and z-standardized domain-specific and overall cognitive scores from the Montreal Cognitive Assessment. Interquartile range increases in exposures to fine particulate matter (53.2-µg/m3) and black carbon (0.9-µg/m3) were linearly associated with lower overall cognition [- 0.13 (95% confidence interval: - 0.22, - 0.04) and - 0.10 (- 0.19, - 0.01), respectively]. Using solid fuel indoors and greater intensity of its use were also associated with lower overall cognition (range of point estimates: - 0.13 to - 0.03), though confidence intervals included zero. Among individual cognitive domains, attention had the largest associations with most exposure measures. Our findings indicate that exposure to HAP may be a dose-dependent risk factor for cognitive impairment. As exposure to HAP remains pervasive in China and worldwide, reducing exposure through the promotion of less-polluting stoves and fuels may be a population-wide intervention strategy to lessen the burden of cognitive impairment.

Household Air Pollution and Blood Pressure, Vascular Damage, and Subclinical Indicators of Cardiovascular Disease in Older Chinese Adults.

BACKGROUND: Limited data suggest that household air pollution from cooking and heating with solid fuel (i.e., coal and biomass) stoves may contribute to the development of hypertension and vascular damage. METHODS: Using mixed-effects regression models, we investigated the associations of household air pollution with blood pressure (BP) and vascular function in 753 adults (ages 40-79 years) from 3 diverse provinces in China. We conducted repeated measures of participants' household fuel use, personal exposure to fine particulate air pollution (PM2.5), BP, brachial-femoral pulse wave velocity (bfPWV), and augmentation index. Ultrasound images of the carotid arteries were obtained to assess intima-media thickness (CIMT) and plaques. Covariate information on sociodemographics, health behaviors, 24-h urinary sodium, and blood lipids was also obtained. RESULTS: Average estimated yearly personal exposure to PM2.5 was 97.5 µg/m3 (SD: 79.2; range: 3.5-1241), and 65% of participants cooked with solid fuel. In multivariable models, current solid fuel use was associated with higher systolic (2.4 mm Hg, 95% CI: -0.4, 4.9) and diastolic BP (1.4 mm Hg, 95% CI: -0.1, 3.0) and greater total area of plaques (1.7 mm2, 95% CI: -6.5, 9.8) compared with exclusive use of electricity or gas stoves. A 1 - ln(µg/m3) increase in PM2.5 exposure was associated with higher systolic (1.5 mm Hg, 95% CI: 0.2, 2.7) and diastolic BP (1.0 mm Hg, 95% CI: 0.4, 1.7) and with greater CIMT (0.02 mm, 95% CI: 0.00, 0.04) and total area of plaques (4.7 mm2, 95% CI: -2.0, 11.5). We did not find associations with arterial stiffness, except for a lower bfPWV (-1.5 m/s, 95% CI: -3.0, -0.0) among users of solid fuel heaters. CONCLUSIONS: These findings add to limited evidence that household air pollution is associated with higher BP and with greater CIMT and total plaque area.

Design and Testing of a Low-Cost Sensor and Sampling Platform for Indoor Air Quality.

Americans spend most of their time indoors at home, but comprehensive characterization of in-home air pollution is limited by the cost and size of reference-quality monitors. We assembled small "Home Health Boxes" (HHBs) to measure indoor PM2.5, PM10, CO2, CO, NO2, and O3 concentrations using filter samplers and low-cost sensors. Nine HHBs were collocated with reference monitors in the kitchen of an occupied home in Fort Collins, Colorado, USA for 168 h while wildfire smoke impacted local air quality. When HHB data were interpreted using gas sensor manufacturers' calibrations, HHBs and reference monitors (a) categorized the level of each gaseous pollutant similarly (as either low, elevated, or high relative to air quality standards) and (b) both indicated that gas cooking burners were the dominant source of CO and NO2 pollution; however, HHB and reference O3 data were not correlated. When HHB gas sensor data were interpreted using linear mixed calibration models derived via collocation with reference monitors, root-mean-square error decreased for CO2 (from 408 to 58 ppm), CO (645 to 572 ppb), NO2 (22 to 14 ppb), and O3 (21 to 7 ppb); additionally, correlation between HHB and reference O3 data improved (Pearson's r increased from 0.02 to 0.75). Mean 168-h PM2.5 and PM10 concentrations derived from nine filter samples were 19.4 µg m-3 (6.1% relative standard deviation [RSD]) and 40.1 µg m-3 (7.6% RSD). The 168-h PM2.5 concentration was overestimated by PMS5003 sensors (median sensor/filter ratio = 1.7) and underestimated slightly by SPS30 sensors (median sensor/filter ratio = 0.91).

Chemical Investigation of Household Solid Fuel Use and Outdoor Air Pollution Contributions to Personal PM2.5 Exposures.

In communities with household solid fuel use, transitioning to clean stoves/fuels often results in only moderate reductions in fine particulate matter (PM2.5) exposures; the chemical composition of those exposures may help explain why. We collected personal exposure (men and women) and outdoor PM2.5 samples in villages in three Chinese provinces (Shanxi, Beijing, and Guangxi) and measured chemical components, including water-soluble organic carbon (WSOC), ions, elements, and organic tracers. Source contributions from chemical mass balance modeling (biomass burning, coal combustion, vehicles, dust, and secondary inorganic aerosol) were similar between outdoor and personal PM2.5 samples. Principal component analysis of organic and inorganic components identified analogous sources, including a regional ambient source. Chemical components of PM2.5 exposures did not differ significantly by gender. Participants using coal had higher personal/outdoor (P/O) ratios of coal combustion tracers (picene, sulfate, As, and Pb) than those not using coal, but no such trend was observed for biomass burning tracers (levoglucosan, K+, WSOC). Picene and most levoglucosan P/O ratios exceeded 1 even among participants not using coal and biomass, respectively, indicating substantial indirect exposure to solid fuel emissions from other homes. Contributions of community-level emissions to exposures suggest that meaningful exposure reductions will likely require extensive fuel use changes within communities.

Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images.

Outdoor ultrafine particles (UFP, <0.1 µm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R2 = 0.54) outperformed the CNN (R2 = 0.47) and land use regression (LUR) models (R2 = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R2 = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.

Personal exposure to PM2.5 of indoor and outdoor origin in two neighboring Chinese communities with contrasting household fuel use patterns.

The Chinese government is replacing high-polluting, household coal heating stoves with electricity- and natural gas-powered heaters to improve ambient air quality. The evaluation of the intervention impact on outdoor PM2.5 and personal exposure in community level are in their initial stages. We compared outdoor air pollution and personal exposure in two neighboring communities (~2 km) in northern China with contrasting household fuel use patterns: one where residents primarily used coal for cooking and heating ("coal village") and one where natural gas was the dominant fuel ("gas village"). We collected 24-h outdoor gravimetric PM2.5 samples in each village and concurrently measured 48-h integrated exposures among 71 participants from 41 and 30 households in the coal and gas villages, respectively. PM2.5 samples were analyzed for mass and chemical composition. Daily outdoor PM2.5 concentrations in the coal village (mean ± standard deviation: 109 ± 41 µg/m3) were, on average, 1.3 ± 0.2 times higher than in the gas village (88 ± 38 µg/m3). However, personal PM2.5 exposures were lower in the coal village (116 ± 121 µg/m3 versus 156 ± 106 µg/m3). PM2.5 species that can serve as tracers for residential coal combustion (e.g., S, Se, Zn, Pb, etc.) and dust (Al, Ca, Mg, Fe, Si and Ti) were higher in the coal village, and the proportion of personal PM2.5 of outdoor origin in the coal village was nearly 2 times higher than the gas village. Our results suggest that ambient PM2.5 and its chemical composition can vary at relatively small spatial scales and may reflect community-level differences in the household energy use. However, personal PM2.5 exposures may not mirror between-village differences in outdoor air pollution if only PM2.5 mass is evaluated. Individual chemical composition of PM2.5 exposure can provide important insight in future studies on the effectiveness of source-targeted air quality interventions.

Determinants of personal exposure to PM2.5 and black carbon in Chinese adults: A repeated-measures study in villages using solid fuel energy.

Exposure to air pollution is a leading health risk factor. The variance components and contributions of indoor versus outdoor source determinants of personal exposure to air pollution are poorly understood, especially in settings of household solid fuel use. We conducted a panel study with up to 4 days of repeated measures of integrated gravimetric personal exposure to PM2.5 and black carbon in 787 men and women (ages 40-79) living in peri-urban villages in northern (Beijing and Shanxi) and southern (Guangxi) China. We simultaneously measured outdoor PM2.5 and collected questionnaire data on sociodemographic characteristics and indoor pollution sources including tobacco smoking and solid fuel stove use. We obtained over 2000 days of personal exposure monitoring which showed higher exposures in the heating season (geometric mean (GM): 108 versus 65 µg/m3 in the non-heating season for PM2.5) and among northern participants (GM: 90 versus 59 µg/m3 in southern China in the non-heating season for PM2.5). We used mixed-effects models to estimate within- and between-participant variance components and to assess the determinants of exposures. Within-participant variance in exposure dominated the total variability (68-95%). Outdoor PM2.5 was the dominant variable for explaining within-participant variance in exposure to PM2.5 (16%). Household fuel use (PM2.5: 8%; black carbon: 10%) and smoking status (PM2.5: 27%; black carbon: 5%) explained the most between-participant variance. Indoor sources (solid fuel stoves, tobacco smoking) were associated with 13-30% higher exposures to air pollution and each 10 µg/m3 increase in outdoor PM2.5 was associated with 6-8% higher exposure. Our findings indicate that repeated measurements of daily exposure are likely needed to capture longer-term exposures in settings of household solid fuel use, even within a single season, and that reducing air pollution from both outdoor and indoor sources is likely needed to achieve measurable reductions in exposures to air pollution.

A feasibility study of metabolic phenotyping of dried blood spot specimens in rural Chinese women exposed to household air pollution.

BACKGROUND: Exposure-response studies and policy evaluations of household air pollution (HAP) are limited by current methods of exposure assessment which are expensive and burdensome to participants. METHODS: We collected 152 dried blood spot (DBS) specimens during the heating and non-heating seasons from 53 women who regularly used biomass-burning stoves for cooking and heating. Participants were enrolled in a longitudinal study in China. Untargeted metabolic phenotyping of DBS were generated using ultra-high performance liquid chromatography coupled with mass spectrometry to exemplify measurement precision and assessment for feasibility to detect exposure to HAP, evaluated by season (high pollution vs. low pollution) and measured personal exposure to fine particulate matter <2.5 µm diameters (PM2.5) and black carbon (BC) in the 48-h prior to collecting the DBS specimen. RESULTS: Metabolites e.g., amino acids, acyl-carnitines, lyso-phosphorylcholines, sphinganine, and choline were detected in the DBS specimens. Our approach is capable of detecting the differences in personal exposure to HAP whilst showing high analytical reproducibility, coefficient of variance (CV) <15%, meeting the U.S. Food and Drug Administration criteria. CONCLUSIONS: Our results provide a proof of principle that high-resolution metabolic phenotypic data can be generated using a simple DBS extraction method thus suitable for exposure studies in remote, low-resource settings where the collection of serum and plasma is logistically challenging or infeasible. The analytical run time (19 min/specimen) is similar to most global phenotyping methods and therefore suitable for large-scale application.

A High-throughput, Robotic System for Analysis of Aerosol Sampling Filters.

The determination of accumulated mass on filter-based aerosol samples is the basis for many forms of scientific research and regulatory monitoring of air quality. However, gravimetric analysis of air sampling filters is tedious, time-intensive, and prone to human error. This work describes the development of an Automated Air Analysis Facility (AIRLIFT) for high-throughput gravimetric mass and optical black carbon measurements of filter-based aerosol samples. The AIRLIFT consists of a sealed environmental enclosure, a 6-axis articulating robotic arm, a programmable control system, a filter weighing apparatus, and an optical system for the determination of aerosol black carbon via light attenuation. The system actively monitors microbalance stability and chamber relative humidity. Digital imaging and QR code scanning support sample tracking and data logging. Performance metrics for temperature and humidity control and weight stability were found to meet or exceed minimum requirements set forth by the US Environmental Protection Agency. The AIRLIFT is capable of analyzing approximately 260 filters per day while reducing the required personnel time by a factor of ~4.

Impacts of stove/fuel use and outdoor air pollution on chemical composition of household particulate matter.

Biomass combustion for cooking and heating releases particulate matter (PM2.5 ) that contributes to household air pollution. Fuel and stove types affect the chemical composition of household PM, as does infiltration of outdoor PM. Characterization of these impacts can inform future exposure assessments and epidemiologic studies, but is currently limited. In this study, we measured chemical components of PM2.5 (water-soluble organic matter [WSOM], ions, black carbon, elements, organic tracers) in rural Chinese households using traditional biomass stoves, semi-gasifier stoves with pelletized biomass, and/or non-biomass stoves. We distinguished households using one stove type (traditional, semi-gasifier, or LPG/electric) from those using multiple stoves/fuels. WSOM concentrations were higher in households using only semi-gasifier or traditional stoves (31%-33%) than in those with exclusive LPG/electric stove (13%) or mixed stove use (12%-22%). Inorganic ions comprised 14% of PM in exclusive LPG/electric households, compared to 1%-5% of PM in households using biomass. Total PAH content was much higher in households that used traditional stoves (0.8-2.8 mg/g PM) compared to those that did not (0.1-0.3 mg/g PM). Source apportionment revealed that biomass burning comprised 27%-84% of PM2.5 in households using biomass. In all samples, identified outdoor sources (vehicles, dust, coal combustion, secondary aerosol) contributed 10%-20% of household PM2.5 .