ArticleBMC public health2026
Modeling and forecasting air pollution for public health protection based on ML and time series models in Gulf Cooperation Council (GCC) countries.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Trends and spatial distribution of pulmonary tuberculosis in China: a surveillance study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundAir pollution is a serious environmental factor associated with higher rates of illness and death from respiratory, cardiovascular, and other non-communicable diseases. Accurate prediction is vital for assessing health risks, which would guide the public health experts and shape sustainable policies.
aimThis study aims to provide a modelling approach by comparing traditional, non-parametric, and machine learning based hybrid models, the first of its kind to forecast PM2.5 levels across the Gulf Cooperation Council (GCC) nations, to inform data-driven environmental health strategies and public policy development.
methodThe study utilized the annual PM2.5 dataset from the World Bank database for GCC countries covering the span of 1991–2021. The traditional models, like ARIMA, Naïve, exponential smoothing, non-parametric model, NPAR, and machine learning based hybrid models, were applied. The model accuracy was evaluated by RMSE, MAE, MAPE, nRMSE, and Diebold–Mariano (DM) test. A Rolling Cross-validation procedure was performed to validate the models. KEY
resultsThe study showed that Qatar consistently showed the highest PM2.5 levels at 87.90 ± 5.78 µg/m³, followed by Bahrain (67.42 ± 4.59 µg/m³) and Kuwait (58.00 ± 5.95 µg/m³). The modelling approach concluded that machine learning based hybrid models performed well across all competing models, with NPAR-NNAR showing the lowest RMSE (KSA = 2.0181, Qatar = 2.2852, Bahrain = 1.3145, and Kuwait = 2.299), while NAIVE-NNAR performed best for UAE = 1.0462 and Oman = 1.6522. The forecasting results showed that GCC countries may experience variations in PM2.5 levels, with Qatar and Bahrain facing the highest concentrations among them in the coming decades. MAJOR IMPLICATION: This is the first multi-country study across the GCC to forecast PM2.5, showing a significant step forward for environmental health planning. The higher accuracy of modelling approaches is important for improving early warning capabilities, anticipating pollution trends that directly affect respiratory and cardiovascular health outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.