Environmental issues: emissions, pollution control, assessment, and management

Nelson, Peter F.

doi:10.1016/b978-0-12-824327-5.00015-6

Cited by 2 publications

References 55 publications

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Enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System

Galván,

Zagal,

Mata

et al. 2023

Communications in Computer and Information Science

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We present and approach for monitoring and built a dataset of regional historical air quality data in Mexico City. We design a hybrid air quality network prototype that combines mobile and stationary sensors to collect street-level data on particulate matter (PM2.5 and PM10). The network is composed of mobile monitoring modules, both stationary at street level and mounted on vehicles, to capture a comprehensive sample of particulate matter behavior in specific areas. Collected data is transmitted using IoT network and processed using machine learning techniques, to generate predictive models to forecast air quality at street level. This approach is an additional improvement to current monitoring capabilities in Mexico City by providing granular street-level data. The system provides a regional and periodic perspective on air quality, enhancing the understanding of pollution levels and supporting informed decision-making to enhance public health and well-being. This research represents a solution for environmental monitoring in urban environments to know how the behavior from pollution levels in air is. The experiments show the effectiveness, and the model of forecast has an overall performance around 81% that is acceptable for the small geographical area testing. As future work is required to include a major number of nodes to collect data from a big geographical coverage and test with other models and algorithms.

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Enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System

Galván,

Zagal,

Mata

et al. 2023

Communications in Computer and Information Science

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Driving factors and key paths of greenhouse gas and air pollutants synergistic control using hybrid multiple-criteria decision-making

Guo,

Li,

Xie

et al. 2024

Front. Environ. Sci.

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Humanity has consumed a large amount of energy and resources to maintain the rapid development of the economy and society, causing greenhouse gas and air pollutants to rise continuously, generating enormous pressures for the sustainable development of many cities. It is economical to control greenhouse gas and air pollutants from the synergy perspective. To identify the key driving factors involved in synergistic control, this paper uses the pressure-state-response (PSR) model to design a performance evaluation model of greenhouse gas and air pollutants synergistic control (GASC) utilizing pressure, state, and response dimensions. The performance evaluation factor system of GASC comprises three primary aspects and 18 criteria. The analytic hierarchy process (AHP) was used to determine the weight of each factor in the evaluation system. The technique for order preference by similarity to an ideal solution (TOPSIS) method was used to calculate the ranking of the synergistic control effects of the four representative provinces in China. We use Importance-performance analysis (IPA) to analyze the performance of driving factors of synergistic control in the province with the lowest ranking from 2016 to 2020. The research shows that in Northeast China, represented by Liaoning province, the government’s response should include changing the support strategy for the new energy consumer, introducing synergistic control standards and policies, and making flexible adjustments to the supply chain. The research provides a scientific basis for the performance evaluation of GASC and decision-making support for lean response strategies.

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Environmental issues: emissions, pollution control, assessment, and management

Cited by 2 publications

References 55 publications

Enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System

Enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System

Driving factors and key paths of greenhouse gas and air pollutants synergistic control using hybrid multiple-criteria decision-making

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