GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Tella, Abdulwaheed; Balogun, Abdul‐Lateef

doi:10.1007/s11356-021-16150-0

Cited by 32 publications

(12 citation statements)

References 83 publications

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“…Some scholars at home and abroad have conducted qualitative analysis research on factors affecting air quality from the perspective of the environment, society, and economic activity, considering various factors such as waste incineration, vehicle exhaust emissions, population growth, coal combustion, industrial waste gas discharge and industrial flue gas dust. These studies confirmed that air pollution results from environmental degradation that has been majorly generated from urban population growth, industrial activities, and road fleet 1 . Industrial waste gas discharge is the main cause of air pollution in developing countries 2 .…”

Section: Introductionsupporting

confidence: 67%

“…Some scholars also look forward to the research on air quality prediction, pointing out that the existing research on the impact of industrial waste gas emissions on air quality is qualitative analysis, and the air quality prediction research ignores the emission information of pollution sources 1 – 3 . Some extensive studies can be further conducted to gasses emission estimating and its impact on the surrounding environments 16 .…”

Section: Introductionmentioning

confidence: 99%

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Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

Liu

Wang²,

et al. 2022

Sci Rep

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With the rapid economic growth, air quality continues to decline. High-intensity pollution emissions and unfavorable weather conditions are the key factors for the formation and development of air heavy pollution processes. Given that research into air quality prediction generally ignore pollutant emission information, in this paper, the random forest supervised learning algorithm is used to construct an air quality prediction model for Zhangdian District with industrial waste gas daily emissions and meteorological factors as variables. The training data include the air quality index (AQI) values, meteorological factors and industrial waste gas daily emission of Zhangdian District from 1st January 2017 to 30th November 2019. The data from 1st to 31th December 2019 is used as the test set to assess the model. The performance of the model is analysed and compared with the backpropagation (BP) neural network, decision tree, and least squares support vector machine (LSSVM) function, which has better overall prediction performance with an RMSE of 22.91 and an MAE of 15.80. Based on meteorological forecasts and expected air quality, a daily emission limit for industrial waste gas can be obtained using model inversion. From 1st to 31th December 2019, if the industrial waste gas daily emission in this area were decreased from 6048.5 million cubic meters of waste gas to 5687.5 million cubic meters, and the daily air quality would be maintained at a good level. This paper deeply explores the dynamic relationship between waste gas daily emissions of industrial enterprises, meteorological factors, and air quality. The meteorological conditions are fully utilized to dynamically adjust the exhaust gas emissions of key polluting enterprises. It not only ensures that the regional air quality is in good condition, but also promotes the in-depth optimization of the procedures of regional industrial enterprises, and reduces the conflict between environmental protection and economic development.

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Section: Introductionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

Liu

Wang²,

et al. 2022

Sci Rep

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“…The achieved consistency between the applied model ensures that the model is sufficiently accurate to predict possible future earth fissures over the region. Few research studies have been conducted to investigate the performance of RF, XGBoost, NB, and KNN models in geo-environmental fields (e.g., landslide, air quality, and flash flood) 80,86,[108][109][110][111] . However, it is difficult to directly compare our findings to this research because XGBoost, NB, and KNN models were not previously utilized to predict earth fissures.…”

Section: Discussionmentioning

confidence: 99%

Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen

Al-Masnay

Al‐Areeq

Ullah

et al. 2022

Sci Rep

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Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.

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“…Cities increasingly use technological solutions, including IoT and multiple sensors, to monitor the urban environment, transitioning into Smart Cities with the objective of improving citizens’ quality of life [ 43 , 44 ]. Data stemming from Smart City applications have been used, for example, to predict air quality [ 45 ], analyse transportation to improve road safety [ 46 ], and have the potential to inform urban planning and policy design to build healthier and more sustainable cities [ 47 ].…”

Section: Big Data Sources and Uses In Urban Public Health And Digital...mentioning

confidence: 99%

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

Gutierrez

Lindegger

Heravi

et al. 2023

IJERPH

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The emergence of big data science presents a unique opportunity to improve public-health research practices. Because working with big data is inherently complex, big data research must be clear and transparent to avoid reproducibility issues and positively impact population health. Timely implementation of solution-focused approaches is critical as new data sources and methods take root in public-health research, including urban public health and digital epidemiology. This commentary highlights methodological and analytic approaches that can reduce research waste and improve the reproducibility and replicability of big data research in public health. The recommendations described in this commentary, including a focus on practices, publication norms, and education, are neither exhaustive nor unique to big data, but, nonetheless, implementing them can broadly improve public-health research. Clearly defined and openly shared guidelines will not only improve the quality of current research practices but also initiate change at multiple levels: the individual level, the institutional level, and the international level.

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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms

Cited by 32 publications

References 83 publications

Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

Air quality prediction models based on meteorological factors and real-time data of industrial waste gas

Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen

Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action

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