Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO), fine particulate matter with diameter ≤ 2.5 µm (PM) and ozone (O), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO, a 10 μg m increase in PM, and a 30-ppb increase in O were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.envint.2024.108810 | DOI Listing |
Ecotoxicol Environ Saf
January 2025
Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, China. Electronic address:
Background: The influence of air pollution on osteoarthritis (OA) remains underexplored.
Methods: We conducted a prospective cohort study in the UK Biobank, estimating exposure levels of particulate matter (PM, PM, PM) nitrogen oxides (NO, NO), and air pollutants exposure score (APES). Cox models assessed associations between air pollution exposure and OA incidence, joint replacement, and survival.
Environ Monit Assess
January 2025
Bhaskaracharya College of Applied Sciences, University of Delhi, New Delhi, Delhi, 110078, India.
This study investigates the spatio-temporal distribution of formaldehyde (HCHO) over the mainland Southeast Asian region (including Northeast India) from 2019 to 2022 using TROPOMI satellite data. HCHO is a key atmospheric trace gas which is influenced by both natural processes and anthropogenic activities. We analyze HCHO levels in relation to atmospheric species including carbon monoxide (CO), nitrogen dioxide (NO), and environmental factors such as land surface temperature (LST), precipitation (PPT), fire radiative power (FRP), and enhanced vegetation index (EVI).
View Article and Find Full Text PDFSci Rep
January 2025
Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, Iran.
Malnutrition and PM pollution remain a pressing global public health concern, especially to vulnerable populations like children under five years old. This study aimed to investigate the correlation between undernutrition in children under five years old and air pollution (exposure to PM) on a global scale. This ecological study evaluated the correlation between undernutrition (wasting and stunting) and air pollution in 123 countries.
View Article and Find Full Text PDFSci Rep
January 2025
Institute for Anthropological Research, 10000, Zagreb, Croatia.
Modelling of pollutants provides valuable insights into air quality dynamics, aiding exposure assessment where direct measurements are not viable. Machine learning (ML) models can be employed to explore such dynamics, including the prediction of air pollution concentrations, yet demanding extensive training data. To address this, techniques like transfer learning (TL) leverage knowledge from a model trained on a rich dataset to enhance one trained on a sparse dataset, provided there are similarities in data distribution.
View Article and Find Full Text PDFBioresour Technol
January 2025
Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China. Electronic address:
Mature compost can reduce gaseous emissions in composting, but its regulation mechanisms via biotic and abiotic functions are largely unknown. This study used fresh and inactivated mature compost as additives in kitchen waste composting to unveil the relevant mechanisms using metagenomic analysis. Results showed that mature compost reduce gaseous emission by improving physiochemical properties and inoculating functional microbes.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!