IoT based monitoring of air quality and traffic using regression analysis

Martín-Baos, José Ángel; Rodríguez-Benítez, Luis; García-Ródenas, Ricardo; Li, Jun

doi:10.1016/j.asoc.2021.108282

Cited by 28 publications

(11 citation statements)

References 54 publications

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“…A multitude of studies has explored the application of the Internet of Things (IoT) in tracking traffic flow and monitoring air quality. For instance, a study in 2022 introduced an inexpensive IoT-based system for tracking traffic flow and determining the air quality index (AQI) 21 . This study utilized machine learning methods, which eliminated the need for complex calibration, allowing the measurement of pollutant gases and accurate determination of AQI.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Tripathi,

Aruna,

Parida

et al. 2024

Sci Rep

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The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m3, 27.89 µg/m3, 26.17 µg/m3, and 27.24 µg/m3, respectively) due to the dry nature of clay materials. Additionally, the research establishes four ML models—Decision Tree (DT), Gradient Boosting Regression (GBR), Random Forest (RF), and Linear Regression (LR)—for predicting PM pollutant concentrations. Notably, Random Forest demonstrates superior performance with the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) at 1.079 and 1.497, respectively. This comprehensive solution, combining IoT-based monitoring and ML-based prediction, contributes to sustainable mining practices, safeguarding worker well-being, and preserving the environment.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Tripathi,

Aruna,

Parida

et al. 2024

Sci Rep

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“…There are methods for calculating vehicle emission forecasts, which are based on AI recognizing vehicles from a video camera. This technology can estimate in real time the emissions from motor transport in a transport flow by using motion vectors as input data [46,47]. An example of corrupting data on the emissions from different types of vehicles is the story of Volkswagen, the German motor vehicle manufacturer, which in 2007 introduced a new diesel engine labeled "clean diesel" as a marketing ploy in the US and Europe.…”

Section: Validity Of Other Input Data Underlying the Modelmentioning

confidence: 99%

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

Krupnova

Rakova

Bondarenko

et al. 2022

BDCC

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The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice.

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“…Many researchers have done work for fire prevention and detection, but not on the reasonable practical scale A high version of the wildfire preventer has been proposed to be implemented. The referred articles [6,7] have implemented a node MCU for transferring data thought to use this because it has a lot of advantages. But an extra module for Wi-Fi is not preferable to designing a power supply circuit.…”

Section: Introductionmentioning

confidence: 99%

A Design and Development of the Smart Forest Alert Monitoring System Using IoT

Krishnamoorthy¹,

Asif²,

Kumar³

et al. 2023

Journal of Sensors

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Forest is one of the main sources of living organisms. Its needs start from the human breath to usage of the wood. But due to many reasons, area occupied by the forest is reducing every year. The reasons behind these environmental impacts are natural disasters (forest fires), deforestation activities, and unlawful actions. Forest fire could be creating most serious threat to wild animals and resources of human welfares. The primary phenomenon of the wild fire occurrence is circumstance hotness of the forest. The dry and hot atmosphere caused the fire in the forest. The deforestation and smuggling activities are also tridents to the available forest. The main consideration of this article is to detect wildfires in advance and protect forest resources from social crimes through advanced sensor integration in the IoT (Internet of Things) environment. A smart forest alert monitoring system has been proposed in this article to avoid forest mishap over by automated self-decision-making protective actions such as parameter measures and alert and implementing the harm mitigation actions related to the hot temperature, humidity, smoke, and smuggling of trees. All the sensors work as per the algorithm designed by the specific application of IoT (Internet of Things). The accurate predictions of the forest fire events and ensuring the forest safety have been tested and verified by a conducted case study on the real forest zone environment.

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IoT based monitoring of air quality and traffic using regression analysis

Cited by 28 publications

References 54 publications

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

A Design and Development of the Smart Forest Alert Monitoring System Using IoT

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