Brentuximab vedotin with dacarbazine or nivolumab as frontline cHL therapy for older patients ineligible for chemotherapy.

ABSTRACT: Older patients with advanced-stage classical Hodgkin lymphoma (cHL) have inferior outcomes compared with younger patients, potentially due to comorbidities and frailty. This noncomparative phase 2 study enrolled patients aged ≥60 years with cHL unfit for conventional chemotherapy to receive frontline brentuximab vedotin (BV; 1.8 mg/kg) with dacarbazine (DTIC; 375 mg/m2) (part B) or nivolumab (part D; 3 mg/kg). In parts B and D, 50% and 38% of patients, respectively, had ≥3 general comorbidities or ≥1 significant comorbidity. Of the 22 patients treated with BV-DTIC, 95% achieved objective response, and 64% achieved complete response (CR). With a median follow-up of 63.6 months, median duration of response (mDOR) was 46.0 months. Median progression-free survival (mPFS) was 47.2 months; median overall survival (mOS) was not reached. Of 21 patients treated with BV-nivolumab, 86% achieved objective response, and 67% achieved CR. With 51.6 months of median follow-up, mDOR, mPFS, and mOS were not reached. Ten patients (45%) with BV-DTIC and 16 patients (76%) with BV-nivolumab experienced grade ≥3 treatment-emergent adverse events; sensory peripheral neuropathy (PN; 27%) and neutropenia (9%) were most common with BV-DTIC, and increased lipase (24%), motor PN (19%), and sensory PN (19%) were most common with BV-nivolumab. Despite high median age, inclusion of patients aged ≤88 years, and frailty, these results demonstrate safety and promising durable efficacy of BV-DTIC and BV-nivolumab combinations as frontline treatment, suggesting potential alternatives for older patients with cHL unfit for initial conventional chemotherapy. This trial was registered at www.clinicaltrials.gov as #NCT01716806.

Impact of pancreas transplantation alone on kidney function: A multicenter clinical cohort study.

Pancreas transplantation alone (PTA) is a ß cell replacement option for selected patients with type 1 diabetes mellitus; concerns have been raised regarding deterioration in kidney function (KF) after PTA. This retrospective multicenter study assessed actual impact of transplantation and immunosuppression on KF in PTA recipients at three Transplant Centers. The primary composite endpoint 10 years after PTA was >50% eGFR decline, eGFR < 30 mL/min/1.73 m2 , and/or receiving a kidney transplant (KT). Overall, 822 PTA recipients met eligibility. Median baseline and 10-year eGFR (mL/min/1.73 m2 ) were 76.3 (58.1-100.8) and 51.3 (35.3-65.9), respectively. Primary composite endpoint occurred in 98 patients (53.5%) with 45 experiencing a >50% decrease in eGFR by 10 years post-transplant, 38 eGFR < 30 mL/min/1.73 m2 and 49 requiring KT. KF declined most significantly within 6 months post-PTA, more often in females and patients with better preserved GFR up to 5 years with 11.6% kidney failure at 10 years. Patient survival and death-censored graft survival were both 68% at 10 years with overall graft thrombosis rate 8%. KF declined initially after PTA but stabilized with further slow progression. In conclusion, prospective intervention studies are needed to test renal sparing interventions while gathering more granular data.

Blood Pressure Effect of Traffic-Related Air Pollution : A Crossover Trial of In-Vehicle Filtration.

BACKGROUND: Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes. OBJECTIVE: To determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. DESIGN: Randomized crossover trial. (ClinicalTrials.gov: NCT05454930). SETTING: In-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016. PARTICIPANTS: Normotensive persons aged 22 to 45 years (n = 16). INTERVENTION: On 2 days, on-road air was entrained into the vehicle. On another day, the vehicle was equipped with high-efficiency particulate air (HEPA) filtration. Participants were blinded to the exposure and were randomly assigned to the sequence. MEASUREMENTS: Fourteen 3-minute periods of blood pressure were recorded before, during, and up to 24 hours after a drive. Image-based central retinal arteriolar equivalents (CRAEs) were measured before and after. Brachial artery diameter and gene expression were also measured and will be reported separately. RESULTS: Mean age was 29.7 years, predrive systolic blood pressure was 122.7 mm Hg, predrive diastolic blood pressure was 70.8 mm Hg, and drive duration was 122.3 minutes (IQR, 4 minutes). Filtration reduced particle count by 86%. Among persons with complete data (n = 13), at 1 hour, mean diastolic blood pressure, adjusted for predrive levels, order, and carryover, was 4.7 mm Hg higher (95% CI, 0.9 to 8.4 mm Hg) for unfiltered drives compared with filtered drives, and mean adjusted systolic blood pressure was 4.5 mm Hg higher (CI, -1.2 to 10.2 mm Hg). At 24 hours, adjusted mean diastolic blood pressure (unfiltered) was 3.8 mm Hg higher (CI, 0.02 to 7.5 mm Hg) and adjusted mean systolic blood pressure was 1.1 mm Hg higher (CI, -4.6 to 6.8 mm Hg). Adjusted mean CRAE (unfiltered) was 2.7 µm wider (CI, -1.5 to 6.8 µm). LIMITATIONS: Imprecise estimates due to small sample size; seasonal imbalance by exposure order. CONCLUSION: Filtration of TRAP may mitigate its adverse effects on blood pressure rapidly and at 24 hours. Validation is required in larger samples and different settings. PRIMARY FUNDING SOURCE: U.S. Environmental Protection Agency and National Institutes of Health.

Evaluation of the effects of elevated CO2 concentrations on the growth of cassava storage roots by destructive harvests and ground penetrating radar scanning approaches.

Cassava (Manihot esculenta Crantz) production will need to be improved to meet future food demands in Sub-Saharan Africa. The selection of high-yielding cassava cultivars requires a better understanding of storage root development. Additionally, since future production will happen under increasing atmospheric CO2 concentrations ([CO2 ]), cultivar selection should include responsiveness to elevated [CO2 ]. Five farmer-preferred African cassava cultivars were grown for three and a half months in a Free Air CO2 Enrichment experiment in central Illinois. Compared to ambient [CO2 ] (~400 ppm), cassava storage roots grown under elevated [CO2 ] (~600 ppm) had a higher biomass with some cultivars having lower storage root water content. The elevated [CO2 ] stimulation in storage root biomass ranged from 33% to 86% across the five cultivars tested documenting the importance of this trait in developing new cultivars. In addition to the destructive harvests to obtain storage root parameters, we explored ground penetrating radar as a nondestructive method to determine storage root growth across the growing season.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models.

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology.

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs.

BACKGROUND: Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. OBJECTIVES: We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. METHODS: We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. RESULTS: Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. DISCUSSION: Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Traffic-related Air Pollution and Lung Cancer Incidence: The California Multiethnic Cohort Study.

Rationale: Although the contribution of air pollution to lung cancer risk is well characterized, few studies have been conducted in racially, ethnically, and socioeconomically diverse populations. Objectives: To examine the association between traffic-related air pollution and risk of lung cancer in a racially, ethnically, and socioeconomically diverse cohort. Methods: Among 97,288 California participants of the Multiethnic Cohort Study, we used Cox proportional hazards regression to examine associations between time-varying traffic-related air pollutants (gaseous and particulate matter pollutants and regional benzene) and lung cancer risk (n = 2,796 cases; average follow-up = 17 yr), adjusting for demographics, lifetime smoking, occupation, neighborhood socioeconomic status (nSES), and lifestyle factors. Subgroup analyses were conducted for race, ethnicity, nSES, and other factors. Measurements and Main Results: Among all participants, lung cancer risk was positively associated with nitrogen oxide (hazard ratio [HR], 1.15 per 50 ppb; 95% confidence interval [CI], 0.99-1.33), nitrogen dioxide (HR, 1.12 per 20 ppb; 95% CI, 0.95-1.32), fine particulate matter with aerodynamic diameter <2.5 µm (HR, 1.20 per 10 µg/m3; 95% CI, 1.01-1.43), carbon monoxide (HR, 1.29 per 1,000 ppb; 95% CI, 0.99-1.67), and regional benzene (HR, 1.17 per 1 ppb; 95% CI, 1.02-1.34) exposures. These patterns of associations were driven by associations among African American and Latino American groups. There was no formal evidence for heterogeneity of effects by nSES (P heterogeneity > 0.21), although participants residing in low-SES neighborhoods had increased lung cancer risk associated with nitrogen oxides, and no association was observed among those in high-SES neighborhoods. Conclusions: These findings in a large multiethnic population reflect an association between lung cancer and the mixture of traffic-related air pollution and not a particular individual pollutant. They are consistent with the adverse effects of air pollution that have been described in less racially, ethnically, and socioeconomically diverse populations. Our results also suggest an increased risk of lung cancer among those residing in low-SES neighborhoods.

Clinical Impact of the Refit CKD-EPI 2021 Creatinine-Based eGFR Equation.

BACKGROUND: The National Kidney Foundation recently endorsed the refit Chronic Kidney Disease Collaboration (CKD-EPI) equation for estimated glomerular filtration rate (eGFR) using creatinine, age and sex [2021 eGFRCr(AS)] without a coefficient for race. We evaluated the impact of adopting the 2021 eGFRCr(AS) equation or a variation of the 2009 CKD-EPI eGFR equation without race [2009 CKD-EPI eGFRCr(ASR-NB)] compared to the original CKD-EPI eGFR [2009 eGFRCr(ASR)]. METHODS: The studied population included patients with a clinically ordered iothalamate clearance (n = 33 889). Bias was assessed as the difference between measured and estimated GFR, P30 was defined as the percentage of estimates within 30% of measured GFR, and concordance was determined according to relevant clinical thresholds. RESULTS: Among Black patients, the median bias for 2009 eGFRCr(ASR), 2009 eGFRCr(ASR-NB), and 2021 eGFRCr(AS) was -1.32 mL min-1 (1.73 m2)-1 (95CI -2.46 to -0.26), -8.81 mL min-1 (1.73 m2)-1 (95CI -9.93 to -7.58), and -6.08 mL min-1 (1.73 m2)-1 (95CI -7.18 to -4.92), respectively. The median bias among non-Black patients was -0.15 m min-1 (1.73 m2)-1 (95CI -0.84 to -0.08) for 2021 eGFRcr(AS) compared to -3.09 mL min-1 (1.73 m2)-1 (95CI -3.17 to -3.03) for the 2009 eGFRCr(ASR). P30 and concordance were not significantly different in either racial group. The net reclassification improvement at a measured GFR <20 mL min-1 (1.73 m2)-1 was 6.4% (95CI 0.36 to 12.4) for Black patients and -5.1% (95CI -6.0 to -4.1) for non-Black patients using the 2021 eGFRCr(AS) equation. CONCLUSIONS: Overall, the change in reported eGFR was minimal. However, these changes led to significant reclassification improvements at lower eGFR, which will indirectly improve equitable access to CKD resources.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign.

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 µg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Ultra-selective carbon nanotubes for photoacoustic imaging of inflamed atherosclerotic plaques.

Disruption of vulnerable atherosclerotic plaques often leads to myocardial infarction and stroke, the leading causes of morbidity and mortality in the United States. A diagnostic method that detects high-risk atherosclerotic plaques at early stages could prevent these sequelae. The abundance of immune cells in the arterial wall, especially inflammatory Ly-6Chi monocytes and foamy macrophages, is indicative of plaque inflammation, and may be associated with plaque vulnerability. Hence, we sought to develop a new method that specifically targets these immune cells to offer clinically-relevant diagnostic information about cardiovascular disease. We combine ultra-selective nanoparticle targeting of Ly-6Chi monocytes and foamy macrophages with clinically-viable photoacoustic imaging (PAI) in order to precisely and specifically image inflamed plaques ex vivo in a mouse model that mimics human vulnerable plaques histopathologically. Within the plaques, high-dimensional single-cell flow cytometry (13-parameter) showed that our nanoparticles were almost-exclusively taken up by the Ly-6Chi monocytes and foamy macrophages that heavily infiltrate plaques. PAI identified inflamed atherosclerotic plaques that display ~6-fold greater signal compared to controls (P<0.001) six hours after intravenous injection of ultra-selective carbon nanotubes, with in vivo corroboration via optical imaging. Our highly selective strategy may provide a targeted, non-invasive imaging strategy to accurately identify and diagnose inflamed atherosclerotic lesions.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions.

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

Distinct Ultrafine Particle Profiles Associated with Aircraft and Roadway Traffic.

The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO2, and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10-1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.

Source Apportionment of Environmental Combustion Sources using Excitation Emission Matrix Fluorescence Spectroscopy and Machine Learning.

The link between particulate matter (PM) air pollution and negative health effects is well-established. Air pollution was estimated to cause 4.9 million deaths in 2017 and PM was responsible for 94% of these deaths. In order to inform effective mitigation strategies in the future, further study of PM and its health effects is important. Here, we present a method for identifying sources of combustion generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy and machine learning (ML) algorithms. PM samples were collected during a health effects exposure assessment panel study in Seattle. We use archived field samples from the exposure study and the associated positive matrix factorization (PMF) source apportionment based on X-ray fluorescence and light absorbing carbon measurements to train convolutional neural network and principal component regression algorithms. We show EEM spectra from cyclohexane extracts of the archived filter samples can be used to accurately apportion mobile and vegetative burning sources but were unable to detect crustal dust, Cl-rich, secondary sulfate and fuel oil sources. The use of this EEM-ML approach may be used to conduct PM exposure studies that include source apportionment of combustion sources.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models.

Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM2.5 was 1.73 µg/m3 for the LSTM model and in the range of 2.20-4.71 µg/m3 for the other models when regulatory ambient PM2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM2.5 information, the developed models still demonstrated relatively high skill in predicting the PM2.5 levels in well-mixed indoor air.

Using Vehicles' Rendezvous for In Situ Calibration of Instruments in Fleet Vehicle-Based Air Pollution Mobile Monitoring.

This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0-140 m for UFP monitors and 5 different buffers within 0-90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10-80 m for UFP monitors and 0-30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6-14% and a normalized mean absolute deviation of 4-8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.

Acute exposure to traffic-related air pollution alters antioxidant status in healthy adults.

BACKGROUND: Exposure to traffic-related air pollution is associated with an increased risk of cardiovascular and respiratory disease. Evidence suggests that inhaled pollutants precipitate these effects via multiple pathways involving oxidative stress. OBJECTIVE: Postulating that a decrease in circulating antioxidant levels reflect an oxidative response, we investigated the effect of inhaled diesel exhaust (DE) on the ratio of reduced to oxidized glutathione (GSH/GSSG) in healthy adults, and whether pre-exposure antioxidant supplementation blunted this response. We also examined exposure-related changes in antioxidant/stress response leukocyte gene expression (GCLc, HMOX-1, IL-6, TGFß) and plasma IL-6 levels. METHODS: Nineteen nonsmoking adults participated in a double-blind, randomized, four-way crossover study. Each subject completed 120-min exposures to filtered air and DE (200 µg/m3), with and without antioxidant pretreatment. Antioxidant comprised 1000 mg ascorbate for 7 days and 1200 mg N-acetylcysteine 1 day prior to exposure, with 1000 mg and 600 mg, respectively, administered 2 h prior to exposure. Whole blood glutathione was measured pre- and post-exposure; plasma IL-6 and mRNA expression were quantified pre, during and post exposure. RESULTS: Diesel exhaust exposure was associated with significantly decreased GSH/GSSG (p = 0.001) and a 4-fold increase in IL-6 mRNA (p = 0.01) post exposure. Antioxidant pretreatment did not significantly mediate the effect of DE exposure on GSH/GSSG, though appeared to decrease the effect of exposure on IL-6 mRNA expression. CONCLUSIONS: Acute DE inhalation induced detectable oxidative effects in healthy adults, which were not significantly attenuated by the selected antioxidant pre-treatment. This finding supports the premise that oxidative stress is one mechanism underlying the adverse effects of traffic-related air pollution.

Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments.

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1-3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving "gold standard" measurements is needed to investigate accuracy.

Probabilistic predictive principal component analysis for spatially misaligned and high-dimensional air pollution data with missing observations.

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM2.5), in which data is usually not measured at all study locations. PM2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lower-dimensional representative scores of such multi-pollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multi-pollutant data tends to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.