Machine learning algorithms for predicting air pollutants

Boonphun, Jirat; Kaisornsawad, Chalat; Wongchaisuwat, Papis

doi:10.1051/e3sconf/201912003004

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Consequently, the rise in machine learning (ML) and statistical models offers a promising solution to these challenges. Models, such as the Bayesian geostatistical model, Linear Regression (LR), and Random Forest (RF), have the advantage of discovering concealed data patterns without the need for deep knowledge of the physico-chemical characteristics of pollutants, thus enhancing the computational efficiency [1,8,9]. Such models are particularly crucial in light of increasing research that underscores the detrimental health effects of air pollution.…”

Section: Introductionmentioning

confidence: 99%

Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo

Azizah,

Mohamed,

Sulistyorini

et al. 2024

Environ Anal Health Toxicol

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The repercussions of climate change have profound implications for human health, leading to the emergence of climate-related diseases. Recognizing this, the Ministry of Environment and Forestry in Indonesia has established the Climate Village Program to tackle and mitigate the impact of climate change. Despite the implementation of this program, Larangan Village in Candi Sub-district, Sidoarjo Regency, continues to struggle with cases of climate-related diseases. This research aims to assess the impact of waste management on the prevalence of climate-related diseases in Larangan Village. Using a quantitative, analytical, observational approach with a cross-sectional design, the study concentrates on two neighborhoods within Larangan Village. The population consists of 330 families in the 2nd neighborhood (Non-Climate Village Program) and 344 families in the 9th neighborhood (Climate Village Program), with a sample size of 109. The variables under investigation encompass waste management and climate-related diseases. Data collection involves conducting interviews and distributing questionnaires among community members in both neighborhoods. The collected data undergo analysis using Multigroup Analysis SEM PLS with a significance level of 0.05. The findings reveal a significant negative correlation between waste management and climate-related diseases, suggesting that enhanced waste management is linked to a reduced incidence of such diseases in the community.

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Section: Introductionmentioning

confidence: 99%

Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo

Azizah,

Mohamed,

Sulistyorini

et al. 2024

Environ Anal Health Toxicol

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“…Nevertheless, the emergence of new research and technologies to analyze and predict air quality can be used to solve air pollution problems in Bishkek. For example, in developed countries machine learning (ML) technologies have been employed to predict air pollution in cities (Boonphun et al, 2019;Chang et al, 2020;Junuz, 2018;Nabavi et al, 2019). Czernecki et al (2021) mentioned that using ML based models can be cheaper and more accurate than the presently used, computationally demanding, numerical weather prediction models with chemical modules.…”

Section: Introductionmentioning

confidence: 99%

Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek

Isaev

Ajikeev²,

Shamyrkanov³

et al. 2022

Aerosol Air Qual. Res.

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During recent years, severe air-pollution problems have garnered worldwide attention due to their effects on human health and the environment. Air pollution in Bishkek, Kyrgyz Republic, is an ever-increasing problem with little research conducted on the impact of air pollutants on public health. We evaluate the performance of several machine learning algorithms applied to air quality and meteorology datasets and compare prediction accuracies of Bishkek air quality given its significant public importance. Data on 16 synoptic atmospheric process were collected by Kyrgyzhydromet from 2016 to 2018 and used to train and build a forecasting model. The model was then tested using data collected in 2020. Climate change in Bishkek and the impact on air pollution was assessed via the frequency of days characterized by daytime temperature inversions and air stagnation. Atmospheric stability increased from 2015 to 2020 with ongoing climate change leading to more temperature inversions. About 80%-90% of days with temperature inversions are associated with winter heating seasons and these numbers increased two-fold during the past 5 years. The impact of lockdown during May 2020) on air quality in Bishkek is also shown. During the lockdown period, CO, NO, NO2, SO 2 , and PM 2.5 decreased by 64%, 1.5%, 75%, 24%, and 54%, respectively, compared to concentrations of these pollutants in 2019. Where identified, emissions from vehicles make up a significant part of the air pollution.

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“…Monitoring can help determine the source and strength of the pollutant, and the data can be fed into the HVAC system to take necessary action. Some models, including machine learning algorithms, have also been developed to forecast IAQ based on meteorological parameters and indoor measurements [9]. Such preventive models may further create an effective means to provide a comfortable indoor environment.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Occupancy Behaviour Approach Towards the Improvement of the Indoor Air Quality Within Building Spaces

Tien,

Mohammadi,

Zhong

et al. 2021

Preprint

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This study presents a vision-based deep learning approach for detecting and recognising occupant' activities and window opening behaviour to help control the heating, ventilation, and air-conditioning (HVAC) system according to space's actual thermal and ventilation requirements. A convolutional neural network (CNN) model was developed, trained, and deployed to a camera for real-time detection. The results of an experimental test within the case study building indicated an overall detection accuracy of 92.72% for occupancy activities and 87.74% for window operations. Real-time detection and recognition provided the generation of the deep learning influenced profiles (DLIP) used as input for building energy simulation to evaluate the impact of the approach on energy demand and indoor air quality. The present work assesses the importance of the proposed approach for predicting indoor air quality and comfort while optimising building HVAC operations to provide an effective demandcontrolled ventilation strategy.

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Machine learning algorithms for predicting air pollutants

Cited by 9 publications

References 20 publications

Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo

Analysis of waste management effect on the climate related disease in Larangan Village, Sidoarjo

Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek

Deep Learning-based Occupancy Behaviour Approach Towards the Improvement of the Indoor Air Quality Within Building Spaces

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