Daily air pollution levels and asthma : exploring the influence of time-activity patterns on personal exposure in Al Jubail industrial city, Saudi Arabia

Abstract

Title: Daily air pollution levels and asthma; exploring the influence of time-activity patterns on personal exposure in Al Jubail industrial city, Saudi Arabia Introduction: Air pollution is a known risk factor for adverse cardio-respiratory health effects. In the last few years, epidemiological studies have provided evidence that exposure to air pollution can aggravate symptoms in asthmatic patients. Some epidemiological studies have used ambient air pollution levels based on fixed-site monitoring (FSM) data to evaluate the short-term effects of ambient air pollution levels on asthma-related emergency department visits (AEDv) using time-series analysis. In the recent past, technology has greatly improved, making it possible to carry out personal monitoring of indoor and outdoor microenvironments (ME). While the existing literature on time-activity patterns (TAP) and ME exposures for populations in the USA and Europe keeps on growing, little research on this topic has been carried out in the Middle East. This study was designed to (i) investigate the statistical association between exposure to air pollution and AEDv, and (ii) identify factors that influence personal exposure in different ME in Jubail Industrial City, Saudi Arabia. Methods: Daily number of AEDv, air pollution levels (particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), carbon monoxide (CO) nitrogen oxides (NOx)) and weather variables (temperature and relative humidity) were obtained from the Royal Commission of Al Jubail Industrial City for the period between 2007 and 2011. Data were analysed using a time series approach, which involved application of a generalised linear model (GLM). Relative risks (RRs) were estimated using Poisson regression, while controlling weather variables, day of the week and holiday indicator for lag times of 0 - 7 days. RRs and 95% confidence intervals (CIs) in AEDv were calculated with each increment of inter-quartile range (IQR) change of each pollutant. Furthermore, to explore the influence of different ME on personal exposure levels, 27 students aged between 16-18 years were recruited and asked to record their detailed movements using a time-activity diary at 15-minute intervals over a period of 24 hours. The students were asked to carry a small backpack containing ii a personal air monitor to measure their personal exposure to PM2.5, and a GPS device to help identify ME including travelling, outdoors, at school and at home. Results: The association between AEDv and change in the quantity of SO2, NO2, PM2.5 and PM10 remained positive and statistically significantly after adjustment in the multi-pollutants model. The RR (in percent %) of AEDv increased by 5.4% (95% CI: 2.4, 8.5) at lag 2 for SO2 , 3.4% (95% CI: 0.8, 6.1) at lag 3 for NO2, 4.4% (95% CI: 2.4, 6.6) for PM2.5 and 2.2% (95% CI: 1.3, 3.2) for PM10 at lag 0 per IQR change in pollutants, 2.0ppb, 7.6ppb, 36μg/m3 and 140μg/m3 respectively. No significant associations between AEDv and CO were found. The time activity diary revealed that most of the students’ time was spent indoors (88.6%). The FSM median (IQR) PM2.5 level, 51.0μg/m3 (34.0-74.2), was significantly higher than personal median PM2.5 level, which was 30.0μg/m3 (20.9-42.4) (Wilcoxon p-value<0.001). Total personal outdoor median concentration of PM2.5, 44.4μg/m3 (31.1-59.5), was significantly higher than total personal indoor concentration, which was 28.3μg/m3 (19.2-40.2) (p-value<0.001). There was a significant but weak correlation between FSM and personal monitor PM2.5 levels when indoors (Spearman’s rank correlation=0.228, p-value < 0.001 n=544), but not at outdoors microenvironment. Conclusion: Current levels of ambient air pollution were associated with AEDv in Al Jubail. While there appear to be similarities between TAP in this small population sample from the Middle East and Europe/USA, the exposure levels in this industrial city appear to be very high, compared to the WHO air quality guidelines. The validity of FSM data as a proxy for personal exposure to PM needs to be characterised so that the exposure error associated with this proxy measure is better understood.