Benzotriazole (BTR) and benzothiazole (BTH) derivatives are high-production-volume chemicals that are mainly used as corrosion inhibitors, and are widely distributed in the environment. BTR derivatives are found in plastics, dishwasher detergents, dry cleaning equipment, and de-icing/anti-icing fluids. BTH derivatives are found in rubber materials, herbicides, slimicides, algicides, fungicides, photosensitizers, azo dyes, drugs, de-icing/anti-icing fluids, and food flavors. However, exposure of humans to BTRs and BTHs is still not known. In this study, six BTRs (1H-benzotriazole, 1-hydroxy-benzotriazole, 4- and 5-hydroxy-benzotriazole [mixture of two isomers], tolyltriazole, xylyltriazole [or 5,6-dimethyl-1H-benzotriazole], and 5-chloro-benzotriazole) and six BTHs (benzothiazole, 2-morpholin-4-yl-benzothiazole, 2-hydroxy-benzothiazole, 2-methylthio-benzothiazole, 2-amino-benzothiazole, and 2-thiocyanomethylthio-benzothiazole) were determined in human urine collected from general populations in seven countries (the U.S., Greece, Vietnam, Korea, Japan, China, and India). The median urinary concentration of the sum of five BTRs (Σ5BTRs; 4- and 5-hydroxy-benzotriazole were not included) ranged from 0.2 (Korea) to 2.8 (India)ng/mL among the countries studied, with the highest concentration of 24.5ng/mL found in a sample from China. Xylyltriazole was found more frequently in urine from all five Asian countries than in urine from the U.S. and Greece. The median concentration of the sum of the six BTHs (Σ6BTHs) ranged from 3.6 (U.S.) to 10.9 (Japan)ng/mL among the countries studied, with a maximum detection rate of 100% in urine samples from Vietnam; BTH was the predominant derivative, accounting for, on average, 43% of the Σ6BTH concentration. Based on the concentrations and detection rates of several BTR and BTH derivatives in urine, possible metabolic transformation pathways of these compounds were presented and human exposure doses calculated. The estimated daily intake doses of BTRs and BTHs were on the order of a few to few tens of micrograms per day.

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors' operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson's r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R² = 0.96, RMSE = 1.15 µg/m³ for daily averaged PM_2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R² = 0.40, RMSE = 4.49 µg/m³). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R² = 0.74-0.95, RMSE = 2.46-0.84 µg/m³). Applying the Seattle model to the other regions resulted in decreased performance (R² = 0.67-0.84, RMSE = 3.41-1.67 µg/m³), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.

Finely resolved PM2.5 exposure measurements at the level of individual participants or over a targeted geographic area can be challenging due to the cost, size and weight of the monitoring equipment. We propose re-purposing the low-cost, portable and lightweight Shinyei PPD42NS particle counter as a particle counting device. Previous field deployment of this sensor suggests that it captures trends in ambient PM2.5 concentrations, but important characteristics of the sensor response have yet to be determined. Laboratory testing was undertaken in order to characterize performance. The Shinyei sensors, in-line with a TSI Aerosol Particle Sizer (APS) model 3321, tracked particle decay within an aerosol exposure chamber. Test atmospheres were composed of monodisperse polystyrene spheres with diameters of 0.75, 1, 2 3 and 6 um as well as a polydisperse atmosphere of ASHRAE test dust #1. Two-minute block averages of the sensor response provide a measurement with low random error, within sensor, for particles in the 0.75–6μm range with a limit of detection of 1 μg/m3. The response slope of the sensors is idiomatic, and each sensor requires a unique response curve. A linear model captures the sensor response for concentrations below 50 μg/m3 and for concentrations above 50 μg/m³ a non-linear function captures the response and saturates at 800 μg/m³. The Limit of Detection (LOD) is 1 μg/m³. The response time is on the order of minutes, making it appropriate for tracking short-term changes in concentration. When paired with prior evaluation, these sensors are appropriate for use as ambient particle counters for low and medium concentrations of respirable particles (< 100 ug/m³). Multiple sensors deployed over a spatial grid would provide valuable spatio-temporal variability in PM2.5 and could be used to validate exposure models. When paired with GPS tracking, these devices have the potential to provide time and space resolved exposure measurements for a large number of participants, thus increasing the power of a study.

Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. 109 monitoring sites with 75% reported speciation data during the period 2003-2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research.