Dataset Information

Nationwide estimation of daily ambient PM_2.5 from 2008 to 2020 at 1 km² in India using an ensemble approach.

ABSTRACT: High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM_2.5) pollution in India. We developed a model for daily average ambient PM_2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R² of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m³ (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m³ (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R² of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m³. We obtained high spatial accuracy with spatial R² greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m³ with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM_2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM_2.5 across India and to identify areas with exceedingly high levels.

SUBMITTER: Mandal S

PROVIDER: S-EPMC10919890 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach.

Mandal Siddhartha S Rajiva Ajit A Kloog Itai I Menon Jyothi S JS Lane Kevin J KJ Amini Heresh H Walia Gagandeep K GK Dixit Shweta S Nori-Sarma Amruta A Dutta Anubrati A Sharma Praggya P Jaganathan Suganthi S Madhipatla Kishore K KK Wellenius Gregory A GA de Bont Jeroen J Venkataraman Chandra C Prabhakaran Dorairaj D Prabhakaran Poornima P Ljungman Petter P Schwartz Joel J

PNAS nexus 20240227 3

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 an ...[more]

PMID: 38456174

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Project description:Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Project description:Previous studies have shown that long-term exposure to fine particulate matter (PM2.5) increases the morbidity and mortality of pulmonary diseases such as asthma, chronic obstructive pulmonary disease and pulmonary emphysema. Oxidative stress and inflammation play key roles in pulmonary damage caused by PM2.5. Nuclear factor erythroid 2-related factor 2 (Nrf2) could regulate the expression of antioxidant and anti-inflammatory genes and is pivotal for protection against PM2.5-induced oxidative stress. In this study, a real-ambient exposure system was constructed with the outdoor ambient air in north China. Wild-type (WT) and Nrf2-/- (KO) mice were exposed to the real-ambient system for six weeks. After PM2.5 exposure, our data showed that the levels of inflammatory factors and malondialdehyde were significantly increased in WT and KO mice. Moreover, the lung function and pathological phenotype of the WT mice were altered but there was no obvious change in the Nrf2-/- mice. To further explore the potential molecular mechanisms, we performed RNA-sequencing. The RNA-sequence analysis results showed that the CYP450 pathway in the first ten pathways of KEGG was related to the metabolism of PM2.5. In WT and KO mice, the expression of CYP2E1 in the CYP450 pathway showed opposite trends after PM2.5 exposure. The data showed that the expression of the CYP2E1 gene in WT-PM mice increased while it decreased in KO-PM; the expression of the CYP2E1 protein showed a similar trend. CYP2E1 is primarily distributed in the endoplasmic reticulum (ER) where it could metabolize various exogenous substances attached to PM2.5 and produce highly toxic oxidation products closely related to ER stress. Consistently, the expression level of GRP94, a biomarker of ER stress, was increased in WT mice and reduced in KO mice under PM2.5 exposure. Persistent ER stress is a mechanism that causes lung damage under PM2.5 exposure. Nrf2 facilitates lung injury during PM2.5 exposure and CYP2E1 metabolism is involved in this process.

Dataset Information

Nationwide estimation of daily ambient PM_2.5 from 2008 to 2020 at 1 km² in India using an ensemble approach.

Publications

Nationwide estimation of daily ambient PM<sub>2.5</sub> from 2008 to 2020 at 1 km<sup>2</sup> in India using an ensemble approach.

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