Journal of Research in Environmental Health

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Editorial Policy

Advertising Policy

Using artificial intelligence systems to investigate relation between air pollution and acute respiratory symptoms registered at the Emergency Medical Center of Mashhad in 2017

Document Type : Original quantitative and Qualitative Research Article

Authors

1 Deputy Minister of Education, Ministry of Health and Medical Education, Professor of Epidemiology and Biostatistics, Kerman University of Medical Sciences

2 Department of Electronic Engineering and Medical Engineering - Khayyam University - Mashhad - Iran

Abstract

Abstract
Background and Aim: Air pollution is one of the most significant environmental problems that has a remarkable impact on the incidence of cardiovascular disease and associated mortality. It is essential to comprehend air pollution effects and the ways of emission and predict the number of patients with acute respiratory problems to eliminate and reduce air pollutants and associated mortality. This study aimed to investigate the relationship between different air pollutants and the number of cardiovascular disease patients in Mashhad.
Materials and Methods: This study applied a neural network to model and analyze the relationship between CO, NO2, SO2, PM2.5, and PM10 and the number of patients with acute respiratory problems. The inputs were average temperature, humidity, wind direction, and wind speed and the output was the number of people referred per day by gender and age. The data set used included meteorological data from the Iran Meteorological Organization, air pollution data from the Mashhad Meteorological Organization, and the number of daily referrals of heart disease patients to the emergency department of Mashhad.
Results: According to this study, the most effective air pollutants in Mashhad were PM2.5 and PM10, followed by NO2, CO, and SO2, respectively.
Conclusion: Neural networks can be applied in the modeling of the relationship between environmental parameters and cardiovascular disease patients because they have a high ability to model nonlinear phenomena. These models show that the more airborne particles, the more rate of cardiovascular diseases in Mashhad

Keywords

References
1. Wark K, Warner CF. Air pollution: its origin and control. 1981.
2.Seinfeld JH, Pandis SN. Atmospheric chemistry and physics: from air pollution to climate change: John Wiley & Sons; 2012.
3.Ghiasedin M. Air pollution-sources, effectsand control. Tehran: Tehran University Press; 2006. p. 25.
4.D'Amato G, Liccardi G, D'amato M, Cazzola M. Outdoor air pollution, climatic changes and allergic bronchial asthma. European Respiratory Journal. 2002;20(3):763-76.
5.Yang M, Zhou R, Qiu X, Feng X, Sun J, Wang Q, et al. Artificial intelligence-assisted analysis on the association between exposure to ambient fine particulate matter and incidence of arrhythmias in outpatients of Shanghai community hospitals. Environment International. 2020;139:105745.
6.Loomis D, Grosse Y, Lauby-Secretan B, El Ghissassi F, Bouvard V, Benbrahim-Tallaa L, et al. The carcinogenicity of outdoor air pollution. The Lancet Oncology. 2013;14(13):1262-3.
7.Organization WH. Burden of disease from ambient and household air pollution. 2014. 2014.
8.             Naddafi K, Hassanvand MS, Yunesian M, Momeniha F, Nabizadeh R, Faridi S, et al. Health impact assessment of air pollution in megacity of Tehran, Iran. Iranian journal of environmental health science & engineering. 2012;9(1):28.
9.Sefiddashti SE, Sari AA, Ghazanfa S. The Impact of Air Pollution on Emergency Admissions of Heart Attack Cases in Tehran: 2007-2012. Journal of Health and Development. 2015;4(3):190-9.
10.Sicard P, Agathokleous E, De Marco A, Paoletti E, Calatayud V. Urban Population Exposure to Air Pollution in Europe Over the Last Decades. 2020.
11.Tiwary A, Williams I. Air pollution: measurement, modelling and mitigation: CRC Press; 2018.
12.Organization WH. Ambient (outdoor) air quality and health. 2016. Retrieved from World Health Organization: Available from: http://www who int/mediacentre/factsheets/fs313/en/[Last accessed on 2015 Nov 26]. 2016.
13.Hassanvand MS, Naddafi K, Faridi S, Arhami M, Nabizadeh R, Sowlat MH, et al. Indoor/outdoor relationships of PM10, PM2. 5, and PM1 mass concentrations and their water-soluble ions in a retirement home and a school dormitory. Atmospheric Environment. 2014;82:375-82.
14.Kelly FJ, Fussell JC. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmospheric environment. 2012;60:504-26.
15.Ravindra K, Rattan P, Mor S, Aggarwal AN. Generalized additive models: Building evidence of air pollution, climate change and human health. Environment international. 2019;132:104987.
16.Bell ML, Committee HHR. Assessment of the health impacts of particulate matter characteristics. Research Report (Health Effects Institute). 2012(161):5.
17. khazaei s, motesaddi s, etemad k, rashidi y, Gheibipoor h, rohani m. Evaluation of the Association between Air Pollutants and Number of Cases with Severe Acute Respiratory Syndrome Recorded at Emergency Medical Centers in Tehran, Iran in 2013. Journal of health research in community. 2016;2(2):37-43.
18.Geravandi S, Goudarzi G, Soltani F, Dobaradaran S, Salmanzadeh S, Kamaei S, et al. Sulfur dioxide pollutant and its effects on disease incidence and death among the citizens of Bushehr city. ISMJ. 2016;19(4):598-607.
19.Mokhtari M, Miri M, Mohammadi A, Khorsandi H, Hajizadeh Y, Abdolahnejad A. Assessment of Air Quality Index and Health Impact of PM10, PM2.5 and SO2 in Yazd, Iran. Journal of Mazandaran University of Medical Sciences. 2015;25(131):14-23.
20. Tsangari H, Paschalidou A, Kassomenos A, Vardoulakis S, Heaviside C, Georgiou K, et al. Extreme weather and air pollution effects on cardiovascular and respiratory hospital admissions in Cyprus. Science of the Total Environment. 2016;542:247-53.
21. Guo Y, Zeng H, Zheng R, Li S, Barnett AG, Zhang S, et al. The association between lung cancer incidence and ambient air pollution in China: a spatiotemporal analysis. Environmental research. 2016;144:60-5.
22.Zhang S, Li G, Tian L, Guo Q, Pan X. Short-term exposure to air pollution and morbidity of COPD and asthma in East Asian area: A systematic review and meta-analysis. Environmental research. 2016;148:15-23.
23.Tsai S-S, Chang C-C, Liou S-H, Yang C-Y. The effects of fine particulate air pollution on daily mortality: A case-crossover study in a subtropical city, Taipei, Taiwan. International journal of environmental research and public health. 2014;11(5):5081-93.
24.Tao Y, Mi S, Zhou S, Wang S, Xie X. Air pollution and hospital admissions for respiratory diseases in Lanzhou, China. Environmental Pollution. 2014;185:196-201.
25.Demuth HB, Beale MH, De Jess O, Hagan MT. Neural network design: Martin Hagan; 2014.
26.Hamid SA. Primer on using neural networks for forecasting market variables. 2004.
27.Strickland MJ, Lin Y, Darrow LA, Warren JL, Mulholland JA, Chang HH. Associations between ambient air pollutant concentrations and birth weight: a quantile regression analysis. Epidemiology. 2019;30(5):624-32.
28.Long JS. Regression models for categorical and limited dependent variables (Vol. 7). Advanced quantitative techniques in the social sciences. 1997.
29. Long JS, Freese J. Regression models for categorical dependent variables using Stata: Stata press; 2006.
30.Haykin SS. Neural networks and learning machines/Simon Haykin: New York: Prentice Hall; 2009.
31.Ambalavanan N, Carlo WA. Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks. Early human development. 2001;65(2):123-37.
32. Svozil D, Kvasnicka V, Pospichal J. Introduction to multi-layer feed-forward neural networks. Chemometrics and intelligent laboratory systems. 1997;39(1):43-62.
Journal of Research in Environmental Health
Volume 6, Issue 4
March 2021
Pages 332-345
Files
Share
How to cite
Statistics