A novel mathematical model for estimating the relative risk of mortality attributable to the combined effect of ambient fine particulate matter (PM2.5) and cold ambient temperature.

Exposures to ambient fine particulate matter (PM2.5) and cold ambient temperatures have been identified as important risk factors in contributing towards the global mortality from chronic obstructive pulmonary disease (COPD). Despite China currently being the country with the largest population in the world, previous relative risk (RR) models have considered little or no information from the ambient air pollution related cohort studies in the country. This likely provides a less accurate picture of the trend in air pollution attributable mortality in the country over time. A novel relative risk model called pollutant-temperature exposure (PTE) model is proposed to estimate the RR attributable to the combined effect of air pollution and ambient temperature in a population. In this paper, the pollutant concentration-response curve was extrapolated from the cohort studies in China, whereas the temperature response curve was extracted from a study in Yangtze River Delta (YRD) region. The performance of the PTE model was compared with the integrated exposure-response (IER) model using the data of YRD region, which revealed that the estimated relative risks of the PTE model were noticeably higher than the IER model during the winter season. Furthermore, the predictive ability of the PTE model was validated using actual data of Ningbo city, which showed that the estimated RR using the PTE model with 1-month moving average data showed a good result with the trend of actual COPD mortality, indicated by a lower root mean square error (RMSE = 0.956). By considering the combined effect of ambient air pollutant and ambient temperature, the PTE model is expected to provide more accurate relative risk estimates for the regions with high levels of ambient PM2.5 and seasonal variation of ambient temperature.

Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review.

This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.

An investigation into the impact of variations of ambient air pollution and meteorological factors on lung cancer mortality in Yangtze River Delta.

Lung cancer (LC) mortality, as one of the top cancer deaths in China, has been associated with increased levels of exposure to ambient air pollutants. In this study, different lag times on weekly basis were applied to study the association of air pollutants (PM2.5, PM10, and NO2) and LC mortality in Ningbo, and in subpopulations at different age groups and genders. Furthermore, seasonal variations of pollutant concentrations and meteorological variables (temperature, relative humidity, and wind speed) were analysed. A generalised additive model (GAM) using Poisson regression was employed to estimate the effect of single pollutant model on LC mortality in Yangtze River Delta using Ningbo as a case study. It was reported that there were statistically significant relationships between lung cancer mortality and air pollutants. Increases of 6.2% (95% confidence interval [CI]: 0.2% to 12.6%) and 4.3% (95% CI: 0.1% to 8.5%) weekly total LC mortality with a 3-week lag time were linked to each 10 µg/m3 increase of weekly average PM2.5 and PM10 respectively. The association of air pollutants (PM2.5, PM10 and NO2) and LC mortality with a 3-week lag time was also found statistically significant during periods of low temperature (T < 18 °C), low relative humidity (H < 73.7%) and low wind speed (u < 2.8 m/s), respectively. The female population was found to be more susceptible to the exposure to air pollution than the male population. In addition, the population with an age of 50 years or above was shown to be more sensitive to ambient air pollutant. These outcomes indicated that increased risk of lung cancer mortality was evidently linked to exposure to ambient air pollutant on a weekly basis. The impact of weekly variation on the LC mortality and air pollutant levels should be considered in air pollution-related health burden analysis.