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Particulate air pollution and mortality in 38 of China's largest cities: time series analysis.

ABSTRACT: Objectives To estimate the short term effect of particulate air pollution (particle diameter <10 ?m, or PM₁₀) on mortality and explore the heterogeneity of particulate air pollution effects in major cities in China.Design Generalised linear models with different lag structures using time series data.Setting 38 of the largest cities in 27 provinces of China (combined population >200 million).Participants 350?638 deaths (200?912 in males, 149?726 in females) recorded in 38 city districts by the Disease Surveillance Point System of the Chinese Center for Disease Control and Prevention from 1 January 2010 to 29 June 2013.Main outcome measure Daily numbers of deaths from all causes, cardiorespiratory diseases, and non-cardiorespiratory diseases and among different demographic groups were used to estimate the associations between particulate air pollution and mortality.Results A 10 µg/m³ change in concurrent day PM₁₀ concentrations was associated with a 0.44% (95% confidence interval 0.30% to 0.58%) increase in daily number of deaths. Previous day and two day lagged PM₁₀ levels decreased in magnitude by one third and two thirds but remained statistically significantly associated with increased mortality. The estimate for the effect of PM₁₀ on deaths from cardiorespiratory diseases was 0.62% (0.43% to 0.81%) per 10 µg/m³ compared with 0.26% (0.09% to 0.42%) for other cause mortality. Exposure to PM₁₀ had a greater impact on females than on males. Adults aged 60 and over were more vulnerable to particulate air pollution at high levels than those aged less than 60. The PM₁₀ effect varied across different cities and marginally decreased in cities with higher PM₁₀ concentrations.Conclusion Particulate air pollution has a greater impact on deaths from cardiorespiratory diseases than it does on other cause mortality. People aged 60 or more have a higher risk of death from particulate air pollution than people aged less than 60. The estimates of the effect varied across cities and covered a wide range of domain.

SUBMITTER: Yin P

PROVIDER: S-EPMC6287590 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Particulate air pollution and mortality in 38 of China's largest cities: time series analysis.

Yin Peng P He Guojun G Fan Maoyong M Chiu Kowk Yan KY Fan Maorong M Liu Chang C Xue An A Liu Tong T Pan Yuhang Y Mu Quan Q Zhou Maigeng M

BMJ (Clinical research ed.) 20170314

Objectives To estimate the short term effect of particulate air pollution (particle diameter <10 μm, or PM10) on mortality and explore the heterogeneity of particulate air pollution effects in major cities in China.Design Generalised linear models with different lag structures using time series data.Setting 38 of the largest cities in 27 provinces of China (combined population >200 million).Participants 350 638 deaths (200 912 in males, 149 726 in females) ...[more]

PMID: 28292780

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Project description:BackgroundThe systematic evaluation of the results of time-series studies of air pollution is challenged by differences in model specification and publication bias.MethodsWe evaluated the associations of inhalable particulate matter (PM) with an aerodynamic diameter of 10 ?m or less (PM10) and fine PM with an aerodynamic diameter of 2.5 ?m or less (PM2.5) with daily all-cause, cardiovascular, and respiratory mortality across multiple countries or regions. Daily data on mortality and air pollution were collected from 652 cities in 24 countries or regions. We used overdispersed generalized additive models with random-effects meta-analysis to investigate the associations. Two-pollutant models were fitted to test the robustness of the associations. Concentration-response curves from each city were pooled to allow global estimates to be derived.ResultsOn average, an increase of 10 ?g per cubic meter in the 2-day moving average of PM10 concentration, which represents the average over the current and previous day, was associated with increases of 0.44% (95% confidence interval [CI], 0.39 to 0.50) in daily all-cause mortality, 0.36% (95% CI, 0.30 to 0.43) in daily cardiovascular mortality, and 0.47% (95% CI, 0.35 to 0.58) in daily respiratory mortality. The corresponding increases in daily mortality for the same change in PM2.5 concentration were 0.68% (95% CI, 0.59 to 0.77), 0.55% (95% CI, 0.45 to 0.66), and 0.74% (95% CI, 0.53 to 0.95). These associations remained significant after adjustment for gaseous pollutants. Associations were stronger in locations with lower annual mean PM concentrations and higher annual mean temperatures. The pooled concentration-response curves showed a consistent increase in daily mortality with increasing PM concentration, with steeper slopes at lower PM concentrations.ConclusionsOur data show independent associations between short-term exposure to PM10 and PM2.5 and daily all-cause, cardiovascular, and respiratory mortality in more than 600 cities across the globe. These data reinforce the evidence of a link between mortality and PM concentration established in regional and local studies. (Funded by the National Natural Science Foundation of China and others.).

Project description:BACKGROUND:Coarse particulate matter with aerodynamic diameter between 2.5 and [Formula: see text] ([Formula: see text]) air pollution is a severe environmental problem in developing countries, but its challenges to public health were rarely evaluated. OBJECTIVE:We aimed to investigate the associations between day-to-day changes in [Formula: see text] and cause-specific mortality in China. METHODS:We conducted a nationwide daily time-series analysis in 272 main Chinese cities from 2013 to 2015. The associations between [Formula: see text] concentrations and mortality were analyzed in each city using overdispersed generalized additive models. Two-stage Bayesian hierarchical models were used to estimate national and regional average associations, and random-effect models were used to pool city-specific concentration-response curves. Two-pollutant models were adjusted for fine particles with aerodynamic diameter [Formula: see text] ([Formula: see text]) or gaseous pollutants. RESULTS:Overall, we observed positive and approximately linear concentration-response associations between [Formula: see text] and daily mortality. A [Formula: see text] increase in [Formula: see text] was associated with higher mortality due to nonaccidental causes [0.23%; 95% posterior interval (PI): 0.13, 0.33], cardiovascular diseases (CVDs; 0.25%; 95% PI: 0.13, 0.37), coronary heart disease (CHD; 0.21%; 95% PI: 0.05, 0.36), stroke (0.21%; 95% PI: 0.08, 0.35), respiratory diseases (0.26%; 95% PI: 0.07, 0.46), and chronic obstructive pulmonary disease (COPD; 0.34%; 95% PI: 0.12, 0.57). Associations were stronger for cities in southern vs. northern China, with significant differences for total and cardiovascular mortality. Associations with [Formula: see text] were of similar magnitude to those for [Formula: see text] in both single- and two-pollutant models with mutual adjustment. Associations were robust to adjustment for gaseous pollutants other than nitrogen dioxide and sulfur dioxide. Meta-regression indicated that a larger positive correlation between [Formula: see text] and [Formula: see text] predicted stronger city-specific associations between [Formula: see text] and total mortality. CONCLUSIONS:This analysis showed significant associations between short-term [Formula: see text] exposure and daily nonaccidental and cardiopulmonary mortality based on data from 272 cities located throughout China. Associations appeared to be independent of exposure to [Formula: see text], carbon monoxide, and ozone. https://doi.org/10.1289/EHP2711.

Project description:BackgroundProviding infrastructure and land uses to encourage active travel (i.e., bicycling and walking) are promising strategies for designing health-promoting cities. Population-level exposure to air pollution during active travel is understudied.ObjectivesOur goals were a) to investigate population-level patterns in exposure during active travel, based on spatial estimates of bicycle traffic, pedestrian traffic, and particulate concentrations; and b) to assess how those exposure patterns are associated with the built environment.MethodsWe employed facility-demand models (active travel) and land use regression models (particulate concentrations) to estimate block-level (n = 13,604) exposure during rush-hour (1600-1800 hours) in Minneapolis, Minnesota. We used the model-derived estimates to identify land use patterns and characteristics of the street network that are health promoting. We also assessed how exposure is correlated with indicators of health disparities (e.g., household income, proportion of nonwhite residents). Our work uses population-level rates of active travel (i.e., traffic flows) rather than the probability of walking or biking (i.e., "walkability" or "bikeability") to assess exposure.ResultsActive travel often occurs on high-traffic streets or near activity centers where particulate concentrations are highest (i.e., 20-42% of active travel occurs on blocks with high population-level exposure). Only 2-3% of blocks (3-8% of total active travel) are "sweet spots" (i.e., high active travel, low particulate concentrations); sweet spots are located a) near but slightly removed from the city-center or b) on off-street trails. We identified 1,721 blocks (~ 20% of local roads) where shifting active travel from high-traffic roads to adjacent low-traffic roads would reduce exposure by ~ 15%. Active travel is correlated with population density, land use mix, open space, and retail area; particulate concentrations were mostly unchanged with land use.ConclusionsPublic health officials and urban planners may use our findings to promote healthy transportation choices. When designing health-promoting cities, benefits (physical activity) as well as hazards (air pollution) should be evaluated.

Dataset Information

Particulate air pollution and mortality in 38 of China's largest cities: time series analysis.

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Particulate air pollution and mortality in 38 of China's largest cities: time series analysis.

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