ArticleEnvironmental monitoring and assessment2022
Multi-step short-term
Article in Environmental monitoring and assessment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed, 9 citations in OpenAlex.
- Predicting particulate matter (Environmental monitoring and assessment · 2024Article
- Deep-learning architecture for PMEnvironmental science and ecotechnology · 2024Review
- Predicting Particulate Matter (Research square · 2024Article
- Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management.Frontiers in public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Particulate matter is one of the key contributors of air pollution and climate change. Long-term exposure to constituents of air pollutants has exerted serious health implications in both humans and plants leading to a detrimental impact on economy. Among the pollutants contributing to air quality determination, particulate matter has been linked to serious health implications causing pulmonary complications, cardiovascular diseases, growth retardation and ultimately death. In agriculture, crop yield is also negatively impacted by the deposition of particulate matter on stomata of the plant which is alarming and can cause food security concerns. The deleterious impact of air pollutants on human health, agricultural and economic well-being highlights the importance of quantifying and forecasting particulate matter. Several deterministic and deep learning models have been employed in the recent years to forecast the concentration of particulate matter. Among them, deep learning models have shown promising results when it comes to modeling time series data and forecasting it. We have explored recurrent neural networks with LSTM model which shows potential to predict the particulate matter (
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.