Air Quality Index (AQI) Classification using CO and NO&lt;inf&gt;2&lt;/inf&gt; Pollutants: A Fuzzy-based Approach

Teologo, Antipas T.; Dadios, Elmer P.; Baldovino, Renann G.; Neyra, Romano Q.; Javel, Irister M.

doi:10.1109/tencon.2018.8650344

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…The study aimed to provide a comprehensive assessment of the emission pollutants from these factories and their impact on the health of nearby communities. It also aimed to analyze the prevalence of particulate matters for different age groups, including infants (0-5), young (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), middle-aged (25-60), and old age group (>61). The research was conducted in six health centers located within a radius of 2 to 40 km from the cement factories.…”

Section: Ambient Air Quality Datamentioning

confidence: 99%

“…The Air Quality Index (AQI) is a scale ranging from 0 to 500, providing a measure of air pollution and associated health risks [18]. A higher AQI indicates greater pollution and increased health concerns, with values over 300 signifying hazardous conditions.…”

Section: Pollutant Air Quality Index (Aqi)mentioning

confidence: 99%

“…Cases, individuals with the outcome, and controls, those without the outcome, were identified. Respondents' ages were categorized into infant (0-5), young (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), middle-aged (25-60), and old (≥ 61) years. Due to the small proportion of the old and infant age groups, only the young and middle-aged groups were considered for odds ratio calculation.…”

Section: Relationship Between Exposure To Emission and Health Outcomementioning

confidence: 99%

“…But, a compressive study on assessments of emission pollutants emitted from clinker producing cement factories at various distances as a national level was not calculated. Similarly, the prevalence effect of particulate matters for the disaggregation age group of infants (0-5), young (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), middle age group (25-60) and old age group (>61) for clinker producing cement factories of the country were not studied in detail.…”

Section: Introductionmentioning

confidence: 99%

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Ethiopian Cement Plants: Examining Emissions and Their Impact on Community Well-being

Firdissa,

Degefa,

Mulugeta

et al. 2024

Preprint

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In the realm of cement production, clinker-producing factories emerge as both energy and material-intensive entities, emitting a substantial array of primary pollutants into the environment compared to their non-clinker producing counterparts. Our study delves into Ethiopian clinker-producing cement factories, unraveling the concentration of primary pollutants and their repercussions on neighboring communities. Venturing beyond mere statistics, we meticulously measured particulate matter (PM2.5 and PM10) and gaseous emissions (CO2, N2O, SO2) at various units within the cement factories and in nearby residential areas. The findings reveal an ambient air quality riddled with concern; PM2.5 levels ranged from 137 to 814 μg/m3, PM10 from 195 to 886 μg/m3 within the cement factories, and PM2.5 (113 to 196 μg/m3) and PM10 (125 to 453 μg/m3) in the adjacent village. Alarmingly, these particulate matter emissions exceeded the limits set by Ethiopian ambient air quality guidelines (150 μg/m3). Unraveling the health implications, respiratory diseases were notably high among individuals aged 25 to 60, followed closely by those aged 6 to 24. In contrast, the age groups 0–5 and > 61 exhibited lesser susceptibility, as infants and the elderly spent more time at home, thus reducing exposure to emissions. Our study underscores the urgent need for legal measures, calling for the establishment of buffer zones between cement factories and settlements. This proactive step, if enforced, holds the promise of mitigating cement particulate emissions and safeguarding the well-being of communities residing in proximity to these industrial giants.

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Section: Ambient Air Quality Datamentioning

confidence: 99%

Section: Pollutant Air Quality Index (Aqi)mentioning

confidence: 99%

Section: Relationship Between Exposure To Emission and Health Outcomementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ethiopian Cement Plants: Examining Emissions and Their Impact on Community Well-being

Firdissa,

Degefa,

Mulugeta

et al. 2024

Preprint

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“…Collecting AQI data is key for monitoring pollution problems. To solve the AQI problem, various approaches have been used for data mining, including artificial neural network (ANN), genetic algorithm (GA), decision tree (DT), random forest (RF), and support vector machine (SVM) [10][11][12][13][14][15][16][17][18][19][20]. Each method has a single basic point of view and provides a general performance analysis of air quality indicators, but it is difficult to distinguish the best method.…”

Section: Introductionmentioning

confidence: 99%

Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules

Lee

Huang

et al. 2019

Applied Sciences

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Air pollution has an ongoing devastating impact on the planet, damaging ecosystems, depleting natural resources, and endangering human health. This paper proposes a new intelligent algorithm that includes parameter optimization and decision rules to forecast and analyze of urban air quality. Through analysis of 24-h daily air quality data provided by the Beijing Air Quality Monitoring Station, simulated annealing (SA) and a decision tree (DT) emerge as the key factors. We prove that in the investigated algorithm, SA and DT can be used to make decision rules and achieve better accuracy for classification. We find that SA can be used to adjust the best parameter settings for the DT. Simulation results show that the accuracy of the proposed algorithm for classification is far better than other existing approaches.

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Emissions from Ethiopian clinker-producing cement factories and their impact on community well-being

Firdissa,

Degefa,

Mulugeta

et al. 2024

Discov Appl Sci

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Air Quality Index (AQI) Classification using CO and NO<inf>2</inf> Pollutants: A Fuzzy-based Approach

Cited by 8 publications

References 7 publications

Ethiopian Cement Plants: Examining Emissions and Their Impact on Community Well-being

Ethiopian Cement Plants: Examining Emissions and Their Impact on Community Well-being

Urban Air Quality Analysis and Forecast Based on Intelligent Algorithm with Parameter Optimization and Decision Rules

Emissions from Ethiopian clinker-producing cement factories and their impact on community well-being

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