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|Title: ||Children’s Urinary Environmental Carbon Load:A Novel Marker Reflecting Residential Ambient Air Pollution Exposure?|
|Authors: ||Saenen, Nelly D.|
Roeffaers, Maarten B.J.
Provost, Eline B.
Nawrot, Tim S.
|Issue Date: ||2017|
|Citation: ||American journal of respiratory and critical care medicine, 196(7), p. 873-881|
|Abstract: ||Rationale: Ambient air pollution, including black carbon, entails a serious public health risk because of its carcinogenic potential and as climate pollutant. To date, an internal exposure marker for black carbon particles that have cleared from the systemic circulation into the urine does not exist.
Objectives: To develop and validate a novel method to measure black carbon particles in a label-free way in urine.
Methods: We detected urinary carbon load in 289 children (aged 9–12 yr) using white-light generation under femtosecond pulsed laser illumination. Children’s residential black carbon concentrations were estimated based on a high-resolution spatial temporal interpolation method.
Measurements and Main Results: We were able to detect urinary black carbon in all children, with an overall average (SD) of 98.2 × 105 (29.8 × 105) particles/ml. The urinary black carbon load was positively associated with medium-term to chronic (1 mo or more) residential black carbon exposure: +5.33 × 105 particles/ml higher carbon load (95% confidence interval, 1.56 × 105 to 9.10 × 105 particles/ml) for an interquartile range increment in annual residential black carbon exposure. Consistently, children who lived closer to a major road (≤160 m) had higher urinary black carbon load (6.93 × 105 particles/ml; 95% confidence interval, 0.77 × 105 to 13.1 × 105).
Conclusions: Urinary black carbon mirrors the accumulation of medium-term to chronic exposure to combustion-related air pollution. This specific biomarker reflects internal systemic black carbon particles cleared from the circulation into the urine, allowing investigators to unravel the complexity of particulate-related health effects.|
|ISI #: ||000412383800017|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2018|
|Appears in Collections: ||Research publications|
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