Dataset Information

Daily nonaccidental mortality associated with short-term PM_2.5 exposures in Delhi, India.

ABSTRACT: Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM_2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM_2.5.

Objectives

We aimed to analyze the association between short-term PM_2.5 exposures and daily nonaccidental mortality in Delhi, India.

Methods

Using generalized additive Poisson regression models, we examined the association between daily PM_2.5 exposures and nonaccidental mortality between June 2010 and December 2016. Daily exposures to PM_2.5 were estimated using an ensemble averaging technique developed by our research group, and mortality data were obtained from the Municipal Corporations of Delhi and the New Delhi Municipal Council.

Results

Median exposures to PM_2.5 were 91.1 µg/m³ (interquartile range = 68.9, 126.2), with minimum and maximum exposures of 21.4 µg/m³ and 276.7 µg/m³, respectively. Total nonaccidental deaths recorded in Delhi during the study period were 700,512. Each 25 µg/m³ increment in exposure was associated with a 0.8% (95% confidence intervals [CI] = 0.3, 1.3%) increase in daily nonaccidental mortality in the study population and a 1.5% (95% CI = 0.8, 2.2%) increase in mortality among those with 60 years of age or over. The exposure-response relationship was nonlinear in nature, with relative risk rising rapidly before tapering off above 125 µg/m³. Meeting WHO guidelines for acceptable levels of exposure over the study period would have likely averted 17,526 (95% CI = 6,837, 25,589) premature deaths, with older and male populations disproportionately affected.

Discussion

This study provides robust evidence of the impact of short-term exposure to PM_2.5 on nonaccidental mortality with important considerations for various stakeholders including policymakers and physicians. Most importantly, we find that reducing exposures significantly below current levels would substantially decrease the mortality burden associated with PM_2.5.

SUBMITTER: Krishna B

PROVIDER: S-EPMC8367036 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Dataset Information

Daily nonaccidental mortality associated with short-term PM_2.5 exposures in Delhi, India.

Objectives

Methods

Results

Discussion

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Dataset Information

Daily nonaccidental mortality associated with short-term PM2.5 exposures in Delhi, India.

Objectives

Methods

Results

Discussion

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Daily nonaccidental mortality associated with short-term PM_2.5 exposures in Delhi, India.