Performance of a microenviromental model for estimating personal NO2 exposure in children

Abstract

A common problem in epidemiological studies on air pollution is exposure misclassification, because investigators often assume exposure is equivalent to outdoor concentrations at participants' homes or at the nearest urban monitor.The aims of this study were: (1) to develop a new microenvironmental exposure model (MEEM), combining time-activity data with modelled outdoor and indoor NO 2 concentrations; (2) to evaluate MEEM against data collected with OgawaÔ personal samplers (OPS); (3) to compare its performance against datasets typically used in epidemiological studies.Schoolchildren wore a personal NO 2 sampler, kept a time-activity diary and completed a questionnaire. This information was used by MEEM to estimate individuals' exposures. These were then compared against concentrations measured by OPS, modelled outdoor concentrations at the children's home (HOME) and concentrations measured at the nearest urban monitoring station (NUM).The mean exposure predicted by MEEM (mean ¼ 19.6 mg m À3 ) was slightly lower than the mean exposure measured by OPS (mean ¼ 20.4 mg m À3 ). The normalised mean bias factor (0.01) and normalised mean absolute error factor (0.25) suggested good agreement. In contrast, the HOME (mean ¼ 31.2 mg m À3 ) and NUM (mean ¼ 28.6 mg m À3 ) methods overpredicted exposure and showed systematic errors.The results indicate that personal exposure can be modelled by MEEM with an acceptable level of agreement, while methods such as HOME and NUM show a poorer performance.

Long-term Exposure to PM 10 and NO 2 in Association with Lung Volume and Airway Resistance in the MAAS Birth Cohort

“…MEEM was used to estimate each child’s exposures during the summer and winter before the review visit at 11 years of age (Mölter et al 2012). We modeled winter and summer exposures separately to capture variation in home indoor air concentrations because of seasonal differences in air exchange rates.…”

“…We evaluated the performance of MEEM using a personal monitoring study of schoolchildren (12–13 years of age) attending a local secondary school in Manchester (Mölter et al 2012). MEEM performed well when compared with NO 2 concentrations measured with personal monitors (Ogawa passive samplers; Ogawa & Co. USA, Inc., Pompano Beach, FL, USA), with a mean prediction error of –0.75 μg/m 3 .…”

“…We have developed a novel microenvironmental exposure model (MEEM) (Mölter et al 2012), which allows for spatial (indoor and outdoor microenvironments) and temporal variability in pollutant concentrations (Mölter et al 2010a, 2010b) and incorporates children’s time–activity patterns to predict personal exposure. The performance of MEEM (for NO 2 ) was evaluated previously through a personal monitoring study of 46 12- to 13-year-old schoolchildren in Manchester, United Kingdom (Mölter et al 2012); we found good agreement between modeled and measured NO 2 concentration (e.g., mean predictor error = –0.75; normalized mean bias factor = 0.04; normalized mean average error factor = 0.27; Spearman’s rank correlation = 0.31, p < 0.05) This performance evaluation also demonstrated that MEEM provided better estimates of exposure than central monitors or an outdoor air pollution model, which tended to overestimate personal exposure levels (Mölter et al 2012).…”

Effects of long-term exposure to PM10and NO2on asthma and wheeze in a prospective birth cohort

“…Children's exposure was estimated using a novel model ‘MEEM’ described in a previous publication 21. The model assumes that children spend the majority of their time in three types of microenvironments (MEs): the child's home (kitchen, living room, bedroom), the child's school and the journey between home and school (see online supplementary table A1, appendix 1).…”

AirSense

“…Ryswyk et al [18] characterized the effect of personal activity patterns on asthmatic children's personal PM2.5 exposure. Mölter et al [10] developed a new micro-environmental exposure model (MEEM) to combine time-activity data with outdoor and indoor air pollutant. Sullivan et al [16] quantified spatiotemporal variability of fine particle concentrations using a combination of the fixed and mobile air pollutant measurements.…”