Measurement and practices for hazardous waste management

Jha, Rupali; Dwivedi, Shipra; Modhera, Bharat

doi:10.1016/b978-0-12-824344-2.00011-2

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…Hazardous waste incineration is the deliberate combustion of waste materials at elevated temperatures. This technique decomposes perilous substances and diminishes the quantity of waste [33]. Advanced incineration methods, such as high-temperature incineration and waste-to-energy incineration, reduce emissions and harness energy from the process.…”

Section: Waste Management Technologies and Regulatory Frameworkmentioning

confidence: 99%

Introductory Chapter: Foundations and Challenges in Hazardous Waste Management

M. Saleh,

I. Hassan,

F. Aglan

2024

Advances and Challenges in Hazardous Waste Management

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Section: Waste Management Technologies and Regulatory Frameworkmentioning

confidence: 99%

Introductory Chapter: Foundations and Challenges in Hazardous Waste Management

M. Saleh,

I. Hassan,

F. Aglan

2024

Advances and Challenges in Hazardous Waste Management

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“…According to the waste states, waste can be divided into solid waste (Jha et al 2022 ), hazardous waste (Jha et al 2022 ), liquid waste (Mekonnen 2012 ), organic waste (Sharma et al 2019 ), and recyclable waste (Ren et al 2018 ). Solid waste is mainly generated from human activities such as manufacturing, agriculture, and mining, and the treatment methods include recycling, incineration, and landfill (Bhatt et al 2018 ).…”

Section: Waste Types and Productionmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

Fang

Chen

et al. 2023

Environ Chem Lett

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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“…e ZWC is a holistic waste management system that requires a reliable performance measurement mechanism. Life cycle analysis (LCA) is widely applied to better understand the environmental performance of solid waste management [22][23][24]. Material flow analysis is another frequently used method to quantify the performance of waste management.…”

Section: Introductionmentioning

confidence: 99%

Measuring Zero-Waste City Performance of a Coal Resource-Based Area in China with MCDM Approach

Zhao

Yang

Duan

2022

Mathematical Problems in Engineering

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The zero-waste city is one of the most visionary initiatives for solving waste problems by enhancing the life cycle solid waste management system. However, the development level of ZW city construction remains unclear. Resource-based areas tend to produce large amounts of industrial solid waste, and achieving a zero-waste city is difficult due to insufficient resource utilization capacity. This study proposed a practical integrated MCDM approach to assess the performance of the ZW city and applied the approach to a typical coal resource-based province in China. The spatiotemporal characteristics and factors influencing the performance of zero-waste cities in each city were further explored. The performance levels increased during the study period; however, the growth rate of most cities is slow. Spatially, the performance levels of zero-waste cities gradually decreased from south to north, showing a radiation pattern with Taiyuan at its core. Challenges toward zero-waste city development were further identified, including heavy industrial structure, widespread underutilization of industrial solid waste, inadequate management of hazardous solid waste, low rate of urban domestic waste classification, and ineffective treatment. The approach gives a holistic and broader picture of the zero-waste management performance, which enables us to identify the challenges in promoting zero-waste cities.

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Measurement and practices for hazardous waste management

Cited by 10 publications

References 3 publications

Introductory Chapter: Foundations and Challenges in Hazardous Waste Management

Introductory Chapter: Foundations and Challenges in Hazardous Waste Management

Artificial intelligence for waste management in smart cities: a review

Measuring Zero-Waste City Performance of a Coal Resource-Based Area in China with MCDM Approach

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