BackgroundShort-term exposure to major air pollutants (O3, CO, NO2, SO2, PM10, and PM2.5) has been associated with respiratory risk. However, evidence on the risk of chronic obstructive pulmonary disease (COPD) exacerbations is still limited. The present study aimed at evaluating the associations between short-term exposure to major air pollutants and the risk of COPD exacerbations.MethodsAfter a systematic search up until March 30, 2016, in both English and Chinese electronic databases such as PubMed, EMBASE, and CNKI, the pooled relative risks and 95% confidence intervals were estimated by using the random-effects model. In addition, the population-attributable fractions (PAFs) were also calculated, and a subgroup analysis was conducted. Heterogeneity was assessed by I2.ResultsIn total, 59 studies were included. In the single-pollutant model, the risks of COPD were calculated by each 10 μg/m3 increase in pollutant concentrations, with the exception of CO (100 μg/m3). There was a significant association between short-term exposure and COPD exacerbation risk for all the gaseous and particulate pollutants. The associations were strongest at lag0 and lag3 for gaseous and particulate air pollutants, respectively. The subgroup analysis not only further confirmed the overall adverse effects but also reduced the heterogeneities obviously. When 100% exposure was assumed, PAFs ranged from 0.60% to 4.31%, depending on the pollutants. The adverse health effects of SO2 and NO2 exposure were more significant in low-/middle-income countries than in high-income countries: SO2, relative risk: 1.012 (95% confidence interval: 1.001, 1.023); and NO2, relative risk: 1.019 (95% confidence interval: 1.014, 1.024).ConclusionShort-term exposure to air pollutants increases the burden of risk of COPD acute exacerbations significantly. Controlling ambient air pollution would provide benefits to COPD patients.

Long-term exposure to ambient PM and BC was associated with an elevated risk of cardiovascular mortality. Despite far higher air pollution exposure concentrations, HRs per unit increase in PM were similar to those from recent comparable studies in North America.

Land use regression (LUR) is a common method of predicting spatial variability of air pollution to estimate exposure. Nitrogen dioxide (NO), nitric oxide (NO), fine particulate matter (PM), and black carbon (BC) concentrations were measured during two sampling campaigns (April-May and November-January) in Hong Kong (a prototypical high-density high-rise city). Along with 365 potential geospatial predictor variables, these concentrations were used to build two-dimensional land use regression (LUR) models for the territory. Summary statistics for combined measurements over both campaigns were: a) NO (Mean=106μg/m, SD=38.5, N=95), b) NO (M=147μg/m, SD=88.9, N=40), c) PM (M=35μg/m, SD=6.3, N=64), and BC (M=10.6μg/m, SD=5.3, N=76). Final LUR models had the following statistics: a) NO (R=0.46, RMSE=28μg/m) b) NO (R=0.50, RMSE=62μg/m), c) PM (R=0.59; RMSE=4μg/m), and d) BC (R=0.50, RMSE=4μg/m). Traditional LUR predictors such as road length, car park density, and land use types were included in most models. The NO prediction surface values were highest in Kowloon and the northern region of Hong Kong Island (downtown Hong Kong). NO showed a similar pattern in the built-up region. Both PM and BC predictions exhibited a northwest-southeast gradient, with higher concentrations in the north (close to mainland China). For BC, the port was also an area of elevated predicted concentrations. The results matched with existing literature on spatial variation in concentrations of air pollutants and in relation to important emission sources in Hong Kong. The success of these models suggests LUR is appropriate in high-density, high-rise cities.

Land use regression (LUR) models have been widely used to provide long-term air pollution exposure assessment in epidemiological studies. However, models have rarely offered variables that account for the dispersion environment close to the source (e.g., street canyons, position and dimensions of buildings, road width). This study used newly available data on building heights and geometry to enhance the representation of land use and the dispersion field in LUR. Models were developed for PM10, NO(X), and NO2 for 2008-2011 for London, U.K. A separate set of models using "traditional" land use and traffic indicators (e.g., distance from road, area of housing within circular buffers) were also developed and their performance was compared with "enhanced" models. Models were evaluated using leave-one-out (n - 1) (LOOCV) and grouped (n - 25%) cross-validation (GCV). LOOCV R(2) values were 0.71, 0.50, 0.66 and 0.73, 0.79, 0.78 for traditional and enhanced PM10, NOX, and NO2 models, respectively. GCV R(2) values were 0.71, 0.53, 0.64 and 0.68, 0.77, 0.77 for traditional and enhanced PM10, NO(X), and NO2 models, respectively. Data on building volume within the area common to a 20 m road buffer within a 25 m circular buffer substantially improved the performance (R(2) > 13%) of NO(X) and NO2 LUR models.

The growth of pathogens potentially relevant to respiratory tract infection may be triggered by changes in ambient temperature. Few studies have examined the association between ambient temperature and pneumonia incidence, and no studies have focused on the susceptible elderly population. We aimed to examine the short-term association between ambient temperature and geriatric pneumonia and to assess the disease burden attributable to cold and hot temperatures in Hong Kong, China. Daily time-series data on emergency hospital admissions for geriatric pneumonia, mean temperature, relative humidity, and air pollution concentrations between January 2005 and December 2012 were collected. Distributed-lag nonlinear modeling integrated in quasi-Poisson regression was used to examine the exposure-lag-response relationship between temperature and pneumonia hospitalization. Measures of the risk attributable to nonoptimal temperature were calculated to summarize the disease burden. Subgroup analyses were conducted to examine the sex difference. We observed significant nonlinear and delayed associations of both cold and hot temperatures with pneumonia in the elderly, with cold temperatures having stronger effect estimates. Among the 10.7% of temperature-related pneumonia hospitalizations, 8.7% and 2.0% were attributed to cold and hot temperatures, respectively. Most of the temperature-related burden for pneumonia hospitalizations in Hong Kong was attributable to cold temperatures, and elderly men had greater susceptibility.