An Exploration of Features Impacting Respiratory Diseases in Urban Areas

Gryech, Ihsane; Ghogho, Mounir; Mahraoui, Chafiq; Kobbane, Abdellatif

doi:10.3390/ijerph19053095

Cited by 11 publications

(6 citation statements)

References 27 publications

(20 reference statements)

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“…In our prior work [31], a novel approach was developed to create the MoreAir data set, aiming to examine the impact of multiple factors on air pollution levels. This unique data set was initially developed to investigate the potential relationship between environmental conditions and the respiratory health of asthma patients in their living environments.…”

Section: B the Moreair Project And Data Setmentioning

confidence: 99%

“…To address concerns about the accuracy and reliability of the sensors used for air quality monitoring, an extensive evaluation was conducted in an earlier study [31]. Lowcost sensors, while cost-effective, often face challenges related to calibration, operational stability under various meteorological conditions, and intramodel variability.…”

Section: B the Moreair Project And Data Setmentioning

confidence: 99%

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Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study

Berkani,

Gryech,

Ghogho

et al. 2023

IEEE Access

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Artificial Intelligence has the potential to contribute to sustainable cities, life on land, and climate action. Specifically, data-driven AI models can analyze large, interconnected databases to develop joint environmental actions. Air quality plays a pivotal role in both climate action and the development of sustainable cities, but developing countries face challenges due to insufficient monitoring stations and limited access to air quality data sets. This study builds upon the MoreAir project, which established a low-cost air pollution monitoring system and provided the first air quality data set from Morocco. We first exploit and delve into the details of the obtained dataset. Subsequently, we conduct a multi-level comparison of data-driven forecasting models, specifically focusing on short-term forecasting of Particulate Matter concentrations. Four forecasting frameworks are explored, using different combinations of exogenous data and spatio-temporal information. Our findings highlight that Machine Learning models, particularly LightGBM and CatBoost, outperform other models. Overall, our study demonstrates that the inclusion of the spatial dimension along with the diverse exogenous features enhances the models' predictive performance, and provides valuable insights.

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Section: B the Moreair Project And Data Setmentioning

confidence: 99%

Section: B the Moreair Project And Data Setmentioning

confidence: 99%

Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study

Berkani,

Gryech,

Ghogho

et al. 2023

IEEE Access

Self Cite

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“…[6–8] Numerous forms of atmospheric pollutants, such as NO 2 , carbon monoxide, CO 2 , SO 2 , and ozone (O 3 ), have been shown to have a substantial correlation with asthma occurrences and exacerbations. [9–17]…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Numerous forms of atmospheric pollutants, such as NO 2 , carbon monoxide, CO 2 , SO 2 , and ozone (O 3 ), have been shown to have a substantial correlation with asthma occurrences and exacerbations. [9][10][11][12][13][14][15][16][17] It has been extensively observed that elevated ozone levels have an inflammatory impact on the respiratory system, which likely contributes to the progression of asthma. [12,18,19] The connection between low to moderate ozone levels and asthma, on the other hand, has received less attention.…”

Section: Introductionmentioning

confidence: 99%

Low-to-moderate atmospheric ozone levels are negatively correlated with hospital visits by asthma patients

Lee

2022

Medicine

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Asthma is a chronic illness of the airways that affects approximately 300 million individuals worldwide. While it is commonly accepted that high ozone levels exacerbate asthma symptoms, the impact of low to moderate ozone levels on asthma symptoms has received little attention. The purpose of this research was to determine the relationship between hospital visits by asthma patients showing the severity of their symptoms and moderate ozone levels. Statistical analyses were performed on hospital visit big data for asthma patients in Seoul, Korea, collected between 2013 and 2017. The data set includes outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The frequency of hospital visits by asthma patients was evaluated in relation to low ozone levels (< 0.03 ppm) and moderate ozone levels (0.03–0.06 ppm) in the Seoul environment. In comparison to low ozone levels, moderate ozone levels resulted in a reduction in outpatient hospital visits (t = 7.052, P < .001). When ozone levels were low to moderate, there was a negative correlation between ozone levels and outpatient visits (r = −0.281, 95% CI: −0.331 to −0.228). Negative associations were also identified between ozone levels and new hospital admissions (t = 2.909, P < .01; r = −0.125, 95% CI: −0.179 to −0.070) and emergency treatments (t = 2.679, P < .01; r = −0.132, 95% CI: −0.186 to −0.076). Additionally, it was verified that moderate ozone levels one day before the visits resulted in a reduction in outpatient visits (t = 5.614, P < .001; r = −0.207, 95% CI: −0.259 to −0.153). A strong relationship was identified between moderate atmospheric ozone levels and a reduction in asthma patient hospital visits.

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“…Air pollution is described as the presence of substances in the air that are harmful to humans and linked to a higher risk of diseases [1][2][3][4][5], such as asthma, as a result of increased airway hyperresponsiveness. Various types of air pollutants, such as particulate matter (PM), nitrogen dioxide (NO 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ), have been found to trigger or exacerbate asthma attacks [6][7][8][9][10][11][12][13]. It is widely accepted that these air pollutants are emitted from various sources, including vehicles, manufacturing industries, and waste deposits.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

Lee

Hyun

et al. 2022

Toxics

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Asthma is a chronic respiratory disorder defined by airway inflammation, chest pains, wheezing, coughing, and difficulty breathing that affects an estimated 300 million individuals globally. Although various studies have shown an association between air pollution and asthma, few studies have used statistical and machine learning algorithms to investigate the effect of each individual air pollutant on asthma. The purpose of this research was to assess the association between air pollutants and the frequency of hospital visits by asthma patients using three analysis methods: linear correlation analyses were performed by Pearson correlation coefficients, and least absolute shrinkage and selection operator (LASSO) and random forest (RF) models were used for machine learning-based analyses to investigate the effect of air pollutants. This research studied asthma patients using the hospital visit database in Seoul, South Korea, collected between 2013 and 2017. The data set included outpatient hospital visits (n = 17,787,982), hospital admissions (n = 215,696), and emergency department visits (n = 85,482). The daily atmospheric environmental information from 2013 to 2017 at 25 locations in Seoul was evaluated. The three analysis models revealed that NO2 was the most significant pollutant on average in outpatient hospital visits by asthma patients. For example, NO2 had the greatest impact on outpatient hospital visits, resulting in a positive association (). In hospital admissions of asthma patients, CO was the most significant pollutant on average. It was observed that CO exhibited the most positive association with hospital admissions (I = 3.329). Additionally, a significant time lag was found between both NO2 and CO and outpatient hospital visits and hospital admissions of asthma patients in the linear correlation analysis. In particular, NO2 and CO were shown to increase hospital admissions at lag 4 in the linear correlation analysis. This study provides evidence that PM2.5, PM10, NO2, CO, SO2, and O3 are associated with the frequency of hospital visits by asthma patients.

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An Exploration of Features Impacting Respiratory Diseases in Urban Areas

Cited by 11 publications

References 27 publications

Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study

Data Driven Forecasting Models for Urban Air Pollution: MoreAir Case Study

Low-to-moderate atmospheric ozone levels are negatively correlated with hospital visits by asthma patients

Machine Learning-Based Analyses of the Effects of Various Types of Air Pollutants on Hospital Visits by Asthma Patients

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