ArticlePDF AvailableLiterature Review

Artificial intelligence for waste management in smart cities: a review

May 2023
Environmental Chemistry Letters 21(4)

DOI:10.1007/s10311-023-01604-3

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CC BY 4.0

Authors:

Zhonghao Chen

Zhejiang University

Ahmed I. Osman

Queen's University Belfast

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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.

Application of artificial intelligence in waste management. The figure illustrates five key aspects: waste type and generation, the use of artificial intelligence in waste management, artificial intelligencebased optimization of waste transportation, the role of artificial intelligence in detecting and reducing illegal dumping and waste treatment

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A neural network for predicting biogas volume using four input attributes. The diagram has eight hidden layers and one output layer to induce biogas prediction. It consists of three layers: input, hidden, and output

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(continued) 2. The predictor exclusivity and cross- prediction methods should be applied to the analysis of artificial neural network Significantly improve the accuracy of large-scale forecasting Ghanbari et al. (2023); Wu et al. (2020)

…

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Environmental Chemistry Letters

https://doi.org/10.1007/s10311-023-01604-3

REVIEW ARTICLE

Artiﬁcial intelligence forwaste management insmart cities: areview

BingbingFang1· JiachengYu1· ZhonghaoChen1· AhmedI.Osman2 · MohamedFarghali3,4· IkkoIhara3·

EssamH.Hamza5· DavidW.Rooney2· Pow‑SengYap1

Received: 15 April 2023 / Accepted: 24 April 2023

Abstract

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.

Keywords Artiﬁcial intelligence· Waste management· Chemical analysis· Optimization· Cost eﬃciency

Introduction

Rapid urbanization, population growth, and economic devel-

opment have increased waste generated worldwide in recent

years. According to the latest statistics, 2.01 billion tonnes

of municipal solid waste was generated globally in 2016.

This ﬁgure is expected to increase to 3.4 billion tonnes by

2050 (Kaza etal. 2018). Unfortunately, 33% of solid waste

is managed correctly and disposed of in illegal dumpsites

or unmonitored landﬁlls (Kaza etal. 2018). Improper waste

disposal poses many environmental and health risks, such

as groundwater contamination, land degradation, increased

cancer incidence, child mortality, and congenital disabilities

(Triassi etal. 2015). In the past, waste management practices

were more rudimentary, with a small group of individuals

collecting garbage from the streets and depositing it in desig-

nated areas (Brancoli etal. 2020). Once the trucks were full,

the waste was left in these designated areas. However, with

the advent of artiﬁcial intelligence, the waste management

Bingbing Fang, Jiacheng Yu and Mohamed Farghali have

contributed equally to this work.

Disclaimer: The views and opinions expressed in this review do not

necessarily reﬂect those of the European Commission or the Special

EU Programmes Body (SEUPB).

* Ahmed I. Osman

aosmanahmed01@qub.ac.uk

* Mohamed Farghali

mohamed.farghali@aun.edu.eg

* Pow-Seng Yap

PowSeng.Yap@xjtlu.edu.cn

1 Department ofCivil Engineering, Xi’an Jiaotong-Liverpool

University, Suzhou215123, China

2 School ofChemistry andChemical Engineering, Queen’s

University Belfast, David Keir Building, Stranmillis Road,

BelfastBT95AG, NorthernIreland, UK

3 Department ofAgricultural Engineering

andSocio-Economics, Kobe University, Kobe657-8501,

Japan

4 Department ofAnimal andPoultry Hygiene &

Environmental Sanitation, Faculty ofVeterinary Medicine,

Assiut University, Assiut71526, Egypt

5 Electric andComputer Engineering Department, Aircraft

Armament (A/CA), Military Technical College, Cairo, Egypt

Environmental Chemistry Letters

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industry is experiencing signiﬁcant transformation toward

achieving sustainability and proﬁtability.

Artiﬁcial intelligence is a rapidly advancing technology

that is gaining popularity in various industries, particularly

waste management (Abdallah etal. 2020). The incorpora-

tion of artiﬁcial intelligence and robotics in the design and

operation of urban waste treatment plants can revolutionize

how solid waste is managed, leading to increased operational

eﬃciency and more sustainable waste management practices

(Goutam Mukherjee etal. 2021; Yigitcanlar and Cugurullo

2020). Several developed countries, including Austria, Ger-

many, New Zealand, the USA, the UK, Japan, Singapore,

Switzerland, South Korea, and Canada, have already begun

to adopt artiﬁcial intelligence technologies to maximize

resource utilization, eﬃciency, and recycling opportunities

throughout the solid waste management cycle (Soni etal.

2019). Artiﬁcial intelligence technologies, particularly for

sorting and treating solid waste, are increasingly critical in

waste management (Andeobu etal. 2022; Wilts etal. 2021).

Therefore, artiﬁcial intelligence is critical in develop-

ing sustainable waste management models, particularly for

transitioning to a “zero waste circular economy” while

considering social, economic, and environmental factors

(Osman etal. 2022). Waste management should be consid-

ered when examining the problems facing diﬀerent geo-

graphic areas and economic sectors, including smart cities.

For instance, researchers have proposed various models for

sustainable waste management, such as a model for meg-

acities that considers waste treatment, recycling, and reuse

options (Liamputtong 2009). To choose the best location

for solid waste management system components, another

model was developed by researchers that take into account

uncertain waste generation rates, facility running costs,

transportation costs, and revenue (Chadegani etal. 2013).

Based on the England panel data, Liu etal. (2017) inves-

tigated data on landﬁll, waste management, and environ-

mental safety in England, including the reasons for illegal

dumping (Goutam Mukherjee etal. 2021; Yigitcanlar and

Cugurullo 2020). In addition, Zhang etal. (2019) empha-

sized the need for a new school of management thought to

transition to a “zero waste circular economy.”

To summarize, this paper provides an overview of

waste types, their generation, and associated issues, as

well as explores various applications of artiﬁcial intel-

ligence in waste management. These applications include

intelligent bin systems, waste-sorting robots, sensor-based

waste monitoring, and predictive models of waste gen-

eration. Additionally, the paper discusses how artiﬁcial

intelligence can help monitor and track waste materials

throughout the recycling process, optimize the logistics

and transportation of recycled waste, identify and reduce

illegal dumping and waste treatment practices, and ana-

lyze the chemical composition of waste. One of the unique

contributions of this paper is the combination of artiﬁcial

intelligence and waste chemical analysis to improve the

process of converting waste into energy. Figure1 shows

the key concepts of artiﬁcial intelligence in waste manage-

ment and the content of this article.

Fig. 1 Application of artiﬁcial intelligence in waste management. The

ﬁgure illustrates ﬁve key aspects: waste type and generation, the use

of artiﬁcial intelligence in waste management, artiﬁcial intelligence-

based optimization of waste transportation, the role of artiﬁcial intel-

ligence in detecting and reducing illegal dumping and waste treatment

practices, and the use of artiﬁcial intelligence to analyze the chemical

composition of waste. This optimized representation provides a clear

and concise overview of the main themes discussed in this review,

highlighting the potential of artiﬁcial intelligence to revolutionize

waste management practices

Environmental Chemistry Letters

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Waste types andproduction

Waste is a major environmental issue due to its poten-

tial to contaminate air, water, and soil. Its generation is

mainly attributed to human activities such as industrial

production, construction, agricultural activities, pollution

emissions, consumption, and waste disposal (Chen etal.

2023; Ukaogo etal. 2020). This can lead to environmental

pollution, health risks, economic losses, and the loss of

resources and costs associated with waste management. To

address these issues, governments and organizations have

implemented waste management strategies such as recy-

cling, composting, reusing, using renewable energy, and

adopting green technologies. Moreover, public education

and awareness campaigns can help reduce the amount of

waste generated and encourage individuals to make more

sustainable choices (Osman etal. 2023).

Waste classiﬁcation principles vary and may involve

categorization based on the waste’s material, state, or

source (Peng etal. 2023). Based on the waste sources,

research has demonstrated that industrial waste is the pri-

mary source of waste (Gaur etal. 2020). This waste is

mainly composed of volatile compounds, wastewater, slag,

and scrap generated during industrial production, which

contain many hazardous substances, such as heavy metals,

organic pollutants, and radioactive materials, leading to

severe environmental pollution (Patel etal. 2022; Tawﬁk

etal. 2022). Additionally, organic waste is generated by

agricultural, animal, and wastewater treatment waste and

the food industry. This type of waste can be used for value-

added purposes, composting, or sent to landﬁlls (Ren etal.

2018). Organic waste from natural environments may

also be hazardous, as it contains toxic substances, such

as ammonia and chlorine, which can cause air, water, and

soil pollution.

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). Solid waste types include

construction, household, industrial, hazardous, electronic,

medical, and agricultural waste (Peng etal. 2023; Vyas

etal. 2022). Hazardous waste contains toxic, ﬂammable,

combustible, radioactive, and corrosive waste, mostly from

electronic and biomedical waste (Akpan and Olukanni

2020). Hexavalent chromium liquid, mercury liquid waste,

corrosive and alkaline liquid waste, cyanide liquid waste,

and heavy metal liquid waste are all examples of liquid

waste. In particular, corrosive and alkaline liquid waste

makes up 12.4%, organic liquid waste 32.2%, and heavy

metal liquid waste 47.9% (Ho and Chen 2018). If appro-

priate control measures are not taken, most liquid waste is

hazardous industrial waste, which can signiﬁcantly nega-

tively aﬀect the environment and public health (Tong and

Elimelech 2016). Recyclable waste is refuse that can be

removed from the waste stream and used as a raw material

to create new products like paper, glass bottles, and ceram-

ics (Fenta 2017). Some refuse recycling techniques include

biological re-treatment, energy recovery, and physical re-

treatment (Waheeg etal. 2022).

To summarize, waste types mainly include solid waste,

hazardous waste, liquid waste, organic waste, and recyclable

waste. The main sources are individuals, industry, agricul-

ture, and transportation. Treatment methods include recol-

lection, incineration, landﬁll, biological, and pyrolysis.

Articial intelligence inwaste management

The utilization of artiﬁcial intelligence has the potential to

bring about a revolution in municipal waste management by

enhancing the eﬀectiveness of waste collection, processing,

and classiﬁcation. Artiﬁcial intelligence-based technologies

like intelligent garbage bins, classiﬁcation robots, predic-

tive models, and wireless detection enable the monitor-

ing of waste bins, predict waste collection, and optimize

the performance of waste processing facilities. The details

are shown in Table1. By leveraging artiﬁcial intelligence,

municipalities bin reduce costs, improve safety, and reduce

environmental impacts associated with waste management.

Smart bin systems

Conventional garbage bins solely collect waste, and sanita-

tion workers must carry out manual inspections to assess

the trash level in the bins. This approach is not eﬃcient for

routine waste disposal inspections. Moreover, due to the fre-

quent ﬁlling of the containers, disease-causing organisms

and insects tend to breed on them (Noiki etal. 2021). There-

fore, designing intelligent garbage bin monitoring systems

to manage garbage is essential in constructing smart cities.

Numerous research studies on intelligent garbage bins

have focused on two key functions: automatic waste clas-

siﬁcation and monitoring. These studies oﬀer a potential

solution for cities to achieve an eﬀective garbage collection

system. An intelligent garbage bin can be created by utiliz-

ing a system on a chip produced by the Espressif systems

(ESP 8266) module, automatically detecting objects and

setting thresholds within the bin. The information gathered

can then be transmitted to another node for further analysis

and processing (Praveen etal. 2020b). For example, Praveen

etal. (2020a) designed a garbage bin with two main pins:

Environmental Chemistry Letters

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Table 1 Application of artiﬁcial intelligence to waste management. The main applications of artiﬁcial intelligence in waste management include intelligent garbage bins, garbage-sorting robots,

and prediction models. Categorize and compare the sum of key information to the conclusions drawn

Type Measure Key information Results/beneﬁts References

Smart garbage bin Sensor network 1. Garbage bin monitoring

2. Collect data

3. Analyze information

4. Road planning

Used to collect municipal waste Khan etal. (2021); Muyunda and Ibrahim

(2017); Neetha etal. (2017)

Ultrasonic sensors 1. Garbage will not overﬂow

2. The lid will open automatically

3. Automatic detection of garbage levels

Digital garbage bin Praveen etal. (2020a); Wijaya etal.

(2017)

Ultrasonic sensors Internet of things 1. Garbage separation

2. Connect to the internet

3. Garbage level monitoring

Garbage bin network Karnalim etal. (2020); Saranya etal.

(2020);Mustafa and Azir (2017)

Ultrasonic sensors

Red external sensor

1. Identify garbage

2. Move straight lines

3. Garbage level monitoring

The garbage bin can be moved auto-

matically in front of people

Pawar etal. (2018); Rajathi G etal.

(2020)

Garbage-sorting robot Height map Near-red extra-specular

spectral image

1. Separate concrete, bricks/blocks, and

mortar

2. Automatically grasp objects

3. Throw into the corresponding recy-

cling area

4. Handle heavy objects without prior

treatment

The sorting eﬃciency can reach 2028

selections/hour, and the accuracy of

line identiﬁcation is almost 100%

Kshirsagar etal. (2022); Xiao etal.

(2020)

Computer vision simultaneous localiza-

tion and mapping

1. It can successfully avoid obstacles

and carry out automatic patrol

2. Detect recyclables

3. Generate a three-dimensional cir-

cumferential map of robot positioning,

navigation, and path planning

The applicability of recycling robots has

been expanded, and the robustness has

been improved

Feng etal. (2022); Wang etal. (2020)

Deep learning simultaneous localization

and mapping

Map reconstruction, navigation,

repositioning, waste detection, and

sequencing

The error diﬀerence is small Bobulski and Kubanek (2021b); Chang

etal. (2020); Chen etal. (2022b); Zhou

etal. (2021)

Computer vision Hyperspectral image 1. Adjust the gripper

2. No need for additional electrical gas

or pneumatic connection to drive the

gripper

Three pannings and angle grabs Leveziel etal. (2022); Liu etal. (2021a);

Yang etal. (2022b)

Environmental Chemistry Letters

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the trigger pin connected to the sensor and the echo pin. An

ultrasonic sensor is placed at the top and bottom of the cover.

Rajathi G etal. (2020) designed a robot garbage bin with

two sensors installed at the bottom, which moves along a

straight line. An obstacle sensor is embedded on one side of

it, which can sense black and emit a buzzer sound to indicate

that the garbage has stopped storing for some time. In addi-

tion, an ultrasonic sensor can be placed at the bin’s edge to

detect the waste level (Mbom etal. 2022). The status of the

container will be updated on the web page via the wireless

ﬁdelity module, showing whether it is full or empty. Some

researchers design bins that separate and monitor garbage

using Arduino and wireless ﬁdelity (Samann 2017). It has an

automatic metal and non-metal separator. Using NodeMCU,

the bin’s water level can be monitored in real time and sent

to the cloud for further analysis and processing (Saranya

etal. 2020).

In summary, the research on smart garbage bins mainly

focuses on automatically monitoring the garbage ﬁlling level

and notifying users in time. The information is primarily

received by sensors and transmitted through the network.

Intelligent bin systems can potentially increase the eﬃciency

of garbage collection, reduce the spread of diseases, and

enhance the city’s overall environment. However, the cost of

implementing smart garbage bins is relatively high, making

it challenging to promote them widely. To address this issue,

the government could consider funding policies to reduce

the cost of smart garbage bins, making them more accessible

to the general public. Furthermore, the regular operation of

these bins can be aﬀected by environmental factors such as

temperature and humidity. Thus, dedicated personnel must

regularly check and maintain the garbage bins. Therefore,

it is crucial to focus on developing and promoting smart

garbage bins in the future.

Waste‑sorting robots

Garbage classiﬁcation is strongly recommended for munici-

pal solid waste management, and using robots can substan-

tially enhance the eﬃciency of garbage classiﬁcation. How-

ever, robots require advanced visual and operational skills

to function in highly heterogeneous, complex, and unpre-

dictable industrial environments for garbage classiﬁcation

(Koskinopoulou etal. 2021). Recent research has focused

on improving the accuracy and eﬃciency of garbage clas-

siﬁcation robots, which requires the development of better

sensors and cameras to identify diﬀerent types of waste, as

well as improved artiﬁcial intelligence algorithms for clas-

sifying waste. Utilizing hyperspectral images to locate the

target region of interest is a promising approach (Xiao etal.

2020). Based on previous research, robots can cope with

complex ﬁeld conditions by adding simultaneous localiza-

tion and mapping technology and instance segmentation

Table 1 (continued)

Type Measure Key information Results/beneﬁts References

Predictive model for

waste production

Predictor exclusivity Integrated empiri-

cal pattern decomposition

1. High-precision municipal solid waste

prediction model was obtained

2. The predictor exclusivity and cross-

prediction methods should be applied

to the analysis of artiﬁcial neural

network

Signiﬁcantly improve the accuracy of

large-scale forecasting

Ghanbari etal. (2023); Wu etal. (2020)

Random forest algorithm Support vector

machines

A radio frequency model is proposed

that can improve prediction perfor-

mance using small classiﬁcation

datasets

Use small categorical datasets to

improve prediction performance

Abbasi and El Hanandeh (2016); Adnan

etal. (2017); Cha etal. (2021); Cha

etal. (2020)

Principal component analysis 1. Convert category variables into con-

tinuous variables

2. Predictive performance has been

improved

The average information on the waste

generation rate of the observed values

was 1165.04kg/square meter, and the

predicted value was 1161.52kg/square

meter

Cha etal. (2023); Cha etal. (2022);

Minoglou and Komilis (2018)

Gradient enhancement regression model A gradient-enhanced regression model

was developed to predict weekly waste

production

Presents waste production trends in New

York and collects comprehensive data

Johnson etal. (2017); Sunayana etal.

(2021)

Environmental Chemistry Letters

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methods. They can automatically collect construction and

demolition waste (Wang etal. 2020). Deep learning technol-

ogy, such as instance segmentation, can accurately detect

the contours of all objects in an image, including construc-

tion and demolition waste (Chen etal. 2022b). Given the

complexity of construction sites and the large amounts of

construction waste generated, manual collection and clas-

siﬁcation are often ineﬃcient and pose safety risks. Con-

sequently, the recovery of construction waste has become a

research focus. (Yang etal. 2023b).

Researchers are currently investigating methods of inte-

grating waste-sorting robots into existing waste management

systems, such as utilizing robots to sort waste before it is

sent to landﬁlls. In this regard, studies have suggested a par-

allel robot model, with the primary concept revolving around

a gripper that is fully integrated into a 4-degree-of-freedom

similar robot structure (Leveziel etal. 2022). Researchers are

also exploring using visual sensors to improve the perfor-

mance of waste-sorting robots. For instance, a waste-sorting

robot has been developed using deep learning techniques

and optical sensors that can accurately identify and classify

diﬀerent types of waste (Mao etal. 2022).

In conclusion, trash-sorting robots have the potential

to signiﬁcantly increase waste management eﬀectiveness,

decrease labor expenses, and boost refuse classiﬁcation

precision. However, some argue that waste-sorting robots

are impractical due to their high installation and mainte-

nance costs compared to traditional waste-sorting methods.

Nonetheless, researchers are exploring more affordable

ways of creating waste-sorting robots, such as utilizing less

expensive materials or designing robots operating in diverse

settings. Additionally, eﬀorts are being made to improve the

robot’s structure, sensors, waste classiﬁcation algorithms,

and robotic arms to make them more eﬀective and eﬃcient.

Waste-sorting machines will continue to be of great interest

and play a signiﬁcant part in real-world uses in the future.

Sensor‑based waste monitoring

Sensor-based waste monitoring is a technology that utilizes

sensors to track the amount of waste generated, identify the

sources of waste, and measure the eﬀectiveness of waste

management strategies in a speciﬁc area. Wireless sensor

network is a network composed of many self-organized

wireless sensors installed in the network to monitor the

physical or environmental parameters of the system (Gurram

etal. 2022). As illustrated in Fig.2, a typical wireless sen-

sor network architecture for solid waste treatment systems

includes various sensors, such as temperature, humidity,

Fig. 2 Uses of artiﬁcial intelligence in the garbage bin and waste

robotic sorting. These include real-time garbage bin monitoring to

optimize waste collection routes and prevent bin overﬂows. Addition-

ally, intelligent garbage sorting can improve recycling eﬃciency and

reduce contamination. In contrast, robotic waste sorting can utilize

robotic arms to sort waste in recycling facilities, increasing the speed

and accuracy of sorting while reducing the need for manual labor.

Artiﬁcial intelligence can also be used for predictive maintenance to

anticipate when waste-sorting equipment will require maintenance,

reducing downtime and extending equipment lifespans. Lastly, waste

management optimization using artiﬁcial intelligence can consider

factors such as traﬃc, weather, and population density to enhance the

eﬃciency of waste collection and processing

Environmental Chemistry Letters

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odor, infrared, gas, and sound sensors. Increase waste man-

agement eﬃcacy.

These sensors can be used to monitor parameters in

real time, thus better controlling the waste treatment pro-

cess. For instance, an electronic nose can be developed

using sensors to quantify odor concentration in real time to

help treat wastewater (Burgués etal. 2021). Additionally,

Sivaprakasam etal. (2020) proposed using a non-contact

microwave sensor for insitu process monitoring of nuclear

waste glass melts in cold crucible induction melting fur-

naces. In addition, they designed infrared sensors to deter-

mine the ﬁlling level of the carriages, gas sensors to detect

hazardous gases, temperature, humidity sensors, and sound

sensors to monitor noise pollution.

An intelligent garbage bin monitoring system based on

a Zigbee network structure was suggested by Karthikeyan

etal. (2017), in which the terminal nodes installed on the

garbage bins detect the level of unﬁlled. Raaju etal. (2019)

suggested using a solar energy collection device to power

the wireless sensor network to increase the nodes’ lifespan.

The major drawback of this system is its inability to dis-

play real-time ﬁlling levels of the garbage bin. Jino Ramson

etal. (2017) suggested a solar-powered wireless monitor-

ing unit with a sensor to measure the level of the can when

it is empty and transmits the information to solar-powered

wireless monitoring unit to address this problem. A self-

powered, direct-connection wireless sensor network solid

waste management system will be created by sending the

data collected from numerous sensor nodes to the central

monitoring station for analysis and visualization (Ramson

etal. 2021). Additionally, a progressive bar is created in

the graphical user interface to symbolize the garbage bin’s

dynamic unﬁlled level.

To summarize, the development of wireless sensor net-

works has been rapid, and there have been numerous studies

on the application of sensors in waste monitoring, mainly

through monitoring the level of garbage and then utilizing

the network to notify users (Joshi etal. 2022). Many stud-

ies have reported applying diﬀerent machine learning-based

methods in waste management to predict and optimize

municipal solid waste generation, detection, collection, clas-

siﬁcation, and properties. The mechanism is shown in Fig.3.

Models topredict waste generation

Research on waste generation prediction models has

recently gained increasing attention, and various models

have been proposed to predict better the amount of waste

generated (Kolekar etal. 2016). These models include sta-

tistical, machine learning, deep learning, and fuzzy models.

Artiﬁcial intelligence algorithms are considered the most

advanced models for reliable waste generation prediction, as

they possess unique capabilities (e.g., data input, learning,

and prediction) (Coskuner etal. 2020).

Artiﬁcial neural networks are one of the nonlinear models

widely used for modeling various urban waste management

processes due to their robustness, fault tolerance, and ability

to describe the complex relationships between variables in

multi-variable systems (Cha etal. 2020). Machine learn-

ing algorithms such as artiﬁcial neural networks multilayer

perceptron, support vector regression algorithms, linear

regression algorithms, decision tree algorithms, and genetic

algorithms can be used to develop models with better pre-

dictive performance on small datasets composed mainly of

categorical variables (Cha etal. 2022, 2017; Golbaz etal.

2019). For cities with historical records, past historical data

can be referred to and integrated with multiple datasets to

establish a gradient boosting regression model. For exam-

ple, Johnson etal. (2017) developed a short-term prediction

model for garbage generation in New York, achieving an

average accuracy of 88%. To analyze urban waste prediction

models in cities with limited historical data, a method that

includes all predictive factors can be utilized to evaluate

regional diﬀerences and their inﬂuence on waste prediction.

Fig. 3 A typical wireless sensor

network structure for a solid

waste management system. A

sensor is installed on the gar-

bage bin. When garbage enters,

the sensor can obtain informa-

tion such as smell, weight, and

humidity to classify the trash.

At the same time, it can detect

the environment of garbage bins

and monitor the ﬁlling level of

garbage bins. Users can monitor

the status of garbage bins on

the platform in real time as the

information is uploaded through

the internet

Environmental Chemistry Letters

1 3

By assessing the model’s dependence on predictive factors,

the impact of regional diﬀerences on urban waste prediction

can be analyzed (Wu etal. 2020).

In conclusion, due to the rapid development of artiﬁcial

intelligence, it has been widely used in waste generation

prediction models. Artificial intelligence systems com-

monly used in waste management include artiﬁcial neural

networks, support vector regression, linear regression, deci-

sion trees, and genetic algorithms. Among these, artiﬁcial

neural networks have been widely implemented in waste

generation prediction applications, followed by support vec-

tor machines.

Monitoring andtracking waste materials

Artiﬁcial intelligence technologies can be used to facilitate

more eﬃcient and eﬀective waste classiﬁcation and recy-

cling. Machine learning techniques can be employed to iden-

tify the type of waste, such as plastics, metals, paper, and

other materials, for more accurate and eﬃcient recycling

(Chen 2022). Artiﬁcial intelligence-based systems can also

monitor the recycling process for anomalies, such as incor-

rect material classiﬁcation or material contamination, and

alert the relevant personnel to take corrective measures.

Furthermore, artiﬁcial intelligence can optimize the recy-

cling process by analyzing the data from the recycling pro-

cess and suggesting improvements (Pouyanfar etal. 2022).

Additionally, artiﬁcial intelligence can be essential in meas-

uring and tracking waste (Ponis etal. 2023). This can help

to ensure that materials are recycled most eﬃciently and

eﬀectively.

Artiﬁcial intelligence can signiﬁcantly improve the eﬃ-

ciency of environmental pollution information acquisition

(Liu etal. 2021b). With the development of big data tech-

nology, the application of artiﬁcial intelligence can quickly

improve the eﬃciency of information acquisition. Artiﬁcial

intelligence has powerful perception capabilities, which

can more eﬀectively identify the source of environmen-

tal information and make basic judgments on the current

environmental situation. For example, artiﬁcial intelligence

can identify the location and size of noise pollution sources

through sound recognition and present the noise situation

in the area through spectrum analysis, so decision-makers

can intuitively understand the noise distribution (Pan etal.

2018). Moreover, artiﬁcial intelligence can also achieve

good environmental management (Zhu etal. 2022). By

installing intelligent terminals in each garbage bin, various

state information can be obtained in real time and further

analyzed and processed. For example, it can detect whether

the garbage bin is complete and use gas sensors to classify it

into recyclable and non-recyclable categories (Rabano etal.

2018). This can reduce labor costs and improve classiﬁcation

eﬃciency, thus achieving good environmental management.

The utilization of artiﬁcial intelligence in waste recycling

has gained widespread application. This includes optimizing

waste collection truck routes, identifying waste management

facilities, simulating the waste transformation process, and

integrating various technologies such as radio frequency

identiﬁcation (Zhou and Piramuthu 2013), a global posi-

tioning system (Hidalgo-Crespo etal. 2022), and geographic

information system (Zewdie and Yeshanew 2023) to moni-

tor solid waste collection trucks and containers. Moreover,

machine learning and image processing techniques have

been combined with these technologies to automatically

detect the level of containers (Vitorino de Souza Melaré

etal. 2017). However, further research is still needed to

explore integrating remote sensing technologies and arti-

ﬁcial intelligence. On the one hand, it is necessary to use

remote sensing technologies to quickly update the data of

domestic waste and realize the dynamic planning of urban

household waste management with a business system that

can be continuously updated and run for a long time. In addi-

tion, it is crucial to further incorporate the expertise accu-

mulated by specialists in this ﬁeld into the system to achieve

intelligent decision support for garbage management.

In summary, artiﬁcial intelligence’s rapid advancement

has enabled its waste monitoring application to become a

new research direction. Developing artiﬁcial intelligence

platforms for waste monitoring is a highly sought-after

research topic. The artiﬁcial intelligence platform receives

data collected by monitors and sensors, which is then trans-

mitted to the artiﬁcial intelligence server. The data train,

optimize, and predict on the platform, generating intelligent

waste management prediction models. These models can

improve the quality and eﬃciency of pollution tracing and

environmental problem-solving, providing eﬀective solu-

tions to environmental challenges.

Chemical analysis ofwaste using articial

intelligence

Recent applications of machine learning have attracted con-

siderable interest in areas including waste-to-energy con-

version (Ahmad etal. 2015), biochar for metal and organic

compound adsorption (Ascher etal. 2022; Dubdub and

Al-Yaari 2021), municipal solid waste treatment (Goutam

Mukherjee etal. 2021), and the oxidation of micropollutants

(Ascher etal. 2022). Table2 summarizes the latest applica-

tions of artiﬁcial intelligence in waste chemistry.

Prediction ofpyrolysis conditions forplastic

recycling

There is currently insuﬃcient capability in the world’s waste

management systems to securely dispose of or recycle all

Environmental Chemistry Letters

1 3

waste plastics, which inevitably increases the number of

waste plastics dumped into the environment (Osman etal.

2023). Each year, the seas are thought to receive 8 million

tonnes of microplastics and 1.5 million tonnes of main

microplastics (Lau etal. 2020). For billions of years, waste

polymers can deteriorate in the environment. Due to inef-

fective pre- and/or post-user management and widespread

landﬁlling of refuse plastics, pyrolysis as a conversion tech-

nique can overcome severe ecological and environmental

obstacles. Machine learning methods can be used to forecast

the continuous and non-catalytic process products of refuse

plastic pyrolysis.

Through data ingestion and experience, machine learn-

ing can instantly improve to correlate inputs to matching

reactions and comprehend the mathematical relationships

between intricate processes and algorithms (Ascher etal.

2022). Several algorithms have been recently reported to

simulate pyrolysis or gasiﬁcation processes. These include

artiﬁcial neural networks, tree-based algorithms such as

decision trees and random forests, and support vector

Table 2 Application of artiﬁcial intelligence in waste chemistry. This

table summarizes 13 articles and divides the application of artiﬁcial

intelligence in waste chemistry into three parts, mainly waste pyroly-

sis, carbon emission prediction, and energy conversion. Key informa-

tion is also summarized

Field Key information Reference

Waste pyrolysis Linking inputs to corresponding responses can improve

machine learning by understanding the mathematical

relationships between complex processes and algo-

rithms. The study includes input variables such as feed

capacity (kg/h), pyrolysis temperature (°C), and steam

residence time (s). Raw material traits, including the

composition of diﬀerent plastic types, ﬁnal analysis,

and particle size (mm), are also considered input fac-

tors in the research

Ascher etal. (2022); Cheng etal. (2020); Mutlu and

Yucel (2018); Yaka etal. (2022)

These models can identify and evaluate catalysts that

optimize hydrogen generation while minimizing

carbon dioxide yield. Additionally, these models can

be utilized to optimize hetero-catalyst loading during

hydrothermal gasiﬁcation and replicate the sodium

hydroxide-catalyzed hydrothermal gasiﬁcation of

waste biomass to investigate the environmental impact

of the process

Estimate carbon emissions The makeup of the carbon source (biochar, fossil, and

inert) is essential in deciding greenhouse gas emis-

sions from solid waste burning. Machine learning

techniques, such as random forests and support vector

machines, can uncover latent connections and forecast

the properties of solid refuse groups. The carbon con-

tent of biological sources and fossils can be calculated

in terms of mass using infrared spectroscopy and

machine learning, allowing researchers to evaluate the

eﬀect of solid refuse burning on decreasing carbon

emissions and saving substantial labor and reagents

Guo etal. (2021); Schwarzböck etal. (2018); Wang etal.

(2021); Yuan etal. (2021)

In biological processing, machine learning algorithms

can be used to separate impurities from raw materials,

compost, and solid digests. This can help reduce pos-

sible environmental risks and improve the proﬁtability

of compost and anaerobic digestion products

Waste-to-bioenergy Porous carbon produced from biomass refuse is a

complex material extensively used in sustainable

waste management and carbon capture. Biogas power

generation is a form of sustainable energy fed by

biological refuse produced by people and animals. It

is part of the circular economy and is regarded as one

of the most energy-eﬃcient and ecologically beneﬁcial

bioenergy production technologies. However, biogas

power production necessitates a lengthy response time

and a complicated input, with no means to integrate

machine learning and deep learning models

Chiu etal. (2022); Huang and Koroteev (2021); Kalhor

and Ghandi (2019); Liu and Karimi (2020); Zaied etal.

(2023)

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machines (Mutlu and Yucel 2018; Yaka etal. 2022). Artiﬁ-

cial neural network methods are the most popular machine

learning techniques, for example, in the context of pyrolysis

simulation by using tar, coke, and permanent gas interac-

tions to model biomass gasiﬁcation. Artiﬁcial neural net-

work methods outperform realistic gas balance models that

predict gasiﬁcation products (Cheng etal. 2020). Tree-based

methods and support vector machines have been success-

fully implemented in waste management. Cheng etal. (2020)

proposed combining random forest-based predictive models

with life cycle assessment and economic analysis to evaluate

diﬀerent pyrolysis feedstocks comprehensively. In addition,

support vector machines have been widely used in pyroly-

sis prediction tasks. For example, support vector machines

perform better than artiﬁcial neural networks at R2, and root

means a squared error in predicting pyrolysis biochar yield

(Cao etal. 2016).

The input factors of particle size (millimeters) allowfor

the classiﬁcation of the composition of various plastic types,

such as polyethylene, polypropylene, polystyrene, polyvi-

nyl chloride, and polyethylene terephthalate, in the research

(Osman etal. 2023). Additionally, the factors included the

ashless chemical components of carbon, hydrogen, oxygen,

nitrogen, and chlorine. Sulfur was not chosen, however,

because it is typically insigniﬁcant compared to the elements

mentioned above. As an additional reactor, working factors,

feed capacity (kilograms/hour), pyrolysis temperature (Cel-

sius), and steam residence time (seconds) were taken into

account. Since they were only mentioned in a few carefully

chosen sources, heating and carrier gas ﬂow rates were not

chosen as input factors. Where ranges were reported in the

authorities, the mean values of the input variables were used

in the study (Cheng etal. 2023).

In the hydrothermal gasiﬁcation of waste biomass, recent

research suggested a machine learning model for ﬁltering

and choosing catalysts. The writers used principal compo-

nent analysis and split the dataset into three subcategories:

non-catalysts, alkali metal catalysts, and transition metal

catalysts (Li etal. 2021a). The created model yielded

encouraging results when selecting and screening catalysts

to maximize hydrogen production and decrease carbon diox-

ide production during hydrothermal gasiﬁcation of discarded

biomass. Comparable research used machine learning and a

technology decision support system to optimize heterogene-

ous catalyst input during hydrothermal gasiﬁcation (Gopira-

jan etal. 2021). The model demonstrated that catalyst addi-

tion had a favorable impact on hydrogen generation.

Artiﬁcial neural network techniques employing Leven-

berg–Marquardt and Bayesian regularization algorithms

were utilized to analyze the environmental eﬀects of waste

biomass hydrothermal gasiﬁcation with sodium hydroxide

as a catalyst. Fózer etal. (2021) showed that a machine

learning-based model could optimize and predict catalyst

composition with a variance value of 0.965. Using sodium

catalysts also increases the process’s ability to cause global

warming. Future studies should focus on the eﬀectiveness

of catalysts in hydrothermal gasiﬁcation processes and their

eﬀects on the atmosphere.

In conclusion, machine learning methods are often con-

sidered “black boxes,” making it challenging to apply them

to study pyrolysis mechanisms and pathways comprehen-

sively. Therefore, future research should integrate machine

learning models with traditional modeling methods, such as

kinetic studies, to provide more comprehensive information

on reaction processes and routes. Some authors have sug-

gested using feature alignment to evaluate the behavior and

applicability of various input factors (Ascher etal. 2022).

Additionally, future studies should clarify the opaque nature

of machine learning algorithms to make them more acces-

sible and facilitate the quick learning of the relationships

between input and goal variables. To create more complete

models, integrated predictive models should be developed

to forecast critical aspects of relevant variables.

Identifying modern andfossil carbon

Accurately measuring carbon emissions is essential to dis-

tinguish between the number of carbon sources in solid

waste, including biogenic and fossil carbon. To this end,

the three groups of carbon sources, namely the “biogenic

carbon group,” “fossil carbon group,” and “inert carbon

group,” are commonly used. Carbon dioxide outputs from

paper, food refuse, and timber by the Bureau of Coast and

Geodetic Survey are typically considered carbon neu-

tral. However, carbon dioxide emissions from the Freight

Classiﬁcation Guide System are linked to climate change

(Schwarzböck etal. 2018). Therefore, the ﬁndings may be

overestimated if biomass-derived carbon emissions are not

considered when calculating greenhouse gas emissions from

solid refuse incineration. Hence, the Freight Classiﬁcation

Guide System and the Bureau of Coast and Geodetic Survey

shares are crucial markers to evaluate how well solid refuse

burning reduces carbon emissions. Machine learning, a tool

for data extraction that identiﬁes patterns, has shown prom-

ise in resolving challenging environmental issues (He etal.

2021). Machine learning algorithms such as random forests

and support vector machines have become powerful tools for

identifying hidden relationships that can predict the char-

acteristics of diﬀerent solid waste groups using established

datasets and literature data. However, there are three main

reasons why these methods may not be widely adopted: (1)

limited data size and quality can hamper performance (Li

etal. 2021b); (2) poor interpretability can make it challeng-

ing to extract relevant information (Visser etal. 2022); and

(3) heavy computation requirements can result in extended

processing times (Yan etal. 2021).

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Moreover, many studies have focused on only one or, at

most, two types of models, providing limited data for com-

paring the prediction accuracy of various models based on

the same waste dataset (Wang etal. 2021). In the case of

separately weighted garbage, the reduced total reﬂection

infrared spectra to determine the mass-based amounts of

biogenic and fossil carbon, Fourier-transform infrared, can

be used with a machine learning method. This information

can then be used to determine how solid refuse burning will

aﬀect the reduction of carbon emissions. This can save a lot

of labor and chemicals while producing quick and precise

results. From this viewing point, this strategy has much to

oﬀer regarding environmental and commercial ﬁscal ben-

eﬁts. By separating contaminants like plastics and stones

from feedstock, compost, and solid digestate, the machine

learning algorithm can be used in bioprocessing to lower

possible environmental risks and enhance the proﬁtability

of composting and anaerobic digestion products (Guo etal.

2021).

In summary, accurately measuring carbon emissions from

solid waste requires identifying the sources of biogenic and

fossil carbon. Machine learning algorithms such as random

forests and support vector machines can be used to extract

data to identify carbon sources. Furthermore, combining

Fourier-transform infrared spectroscopy with machine learn-

ing methods can determine the quantity of biogenic and fos-

sil carbon. This approach can save resources and produce

fast and accurate results.

Waste toenergy

Biogas for electricity generation is a renewable energy

source that takes its input from organic waste produced by

humans and animals and is part of a circular economy (Far-

ghali etal. 2022; Salguero-Puerta etal. 2019). According

to the International Energy Agency, this century will see

a two- to threefold rise in energy consumption, using up

many resources. According to data from 2020, the global

energy supply was 584,523,552 exajoules. Of this supply,

29.47% was derived from oil, 26.80% from coal, 23.68%

from natural gas, 9.84% from biofuels, and 5.21% from

renewable sources (Farghali etal. 2023). Encouraging

renewable energy to generate power is one of the primary

growth areas. Among the various methods of energy pro-

duction, biogas power generation is considered one of the

most energy-eﬃcient and ecologically favorable bioenergy

production technologies. Biogas resources have high utiliza-

tion rates, especially in the circular economy. For example,

in Europe, biogas energy production has exceeded 6 million

tonnes of oil, with a yearly growth rate of over 20% (Chiu

etal. 2022). However, producing biogas needs complicated

inputs and slow reaction times. Although researchers have

recently begun using predictive models to analyze the input

factors for biogas production, key factors have not been

adequately analyzed, resulting in inconsistent output. To

maximize biogas output, machine learning was incorporated

to analyze and identify the key variables that signiﬁcantly

inﬂuence results, obviating the need for intricate computa-

tions and lowering the risk of mistakes (Farghali etal. 2023;

Pence etal. 2023).

Currently, there are limited approaches that combine deep

learning, machine learning, and neural networks for produc-

ing methane, as illustrated in Fig.4. Some ongoing research

is focused on developing a deep learning model for predict-

ing faults in internal combustion engine power production,

which can aid biogas plants in adjusting their maintenance

and troubleshooting plans (Liu and Karimi 2020). Addi-

tionally, several studies have worked to develop an artiﬁ-

cial neural networks model to forecast the optimum biogas

output using particular sources like sugarcane bagasse and

bovine manure (Ghatak and Ghatak 2018) and anaerobic co-

digestion of palm mill wastewater (Zaied etal. 2023). Biogas

generation studies and study ﬁndings use deep learning to

analyze important waste and output forecasts compared to

other renewable energy sources. Studies have looked into

and made predictions about refuse gathering (Huang and

Koroteev 2021) and some on output prediction, but waste

inputs and outputs are rarely combined. Previous research

focused on predictive models using machine learning tech-

niques like artiﬁcial neural networks, k-nearest neighbors,

and support vector machines. However, the eﬀect of time on

capability has not been considered. To make predictions, this

research uses a time series model. According to a literature

study, extended short-term memory deep neural networks

can handle numerous variables, which makes them helpful

for resolving issues with time series forecasting (Bouktif

etal. 2018).

In conclusion, methane gas power generation is a renew-

able energy source whose input comes from organic waste

produced by humans and animals and is part of the circular

economy. Machine learning and deep learning models are

used to analyze and identify key variables that signiﬁcantly

aﬀect methane output. Extended short-term memory deep

neural networks are used to predict waste input and output.

Logistics, transportation andrecycling

Logistics and transportation of refuse are essential compo-

nents of waste management. Moreover, the waste’s logistics

and transportation system is a critical hub connecting the

waste source and treatment (Xia etal. 2022). However, the

current waste logistics and transportation systems suﬀer

from several shortcomings. Firstly, the costs associated with

waste logistics and transport are prohibitively high, particu-

larly during the collection phase. According to Sulemana

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1 3

etal. (2018), transportation costs incurred during waste col-

lection account for approximately 70–80% of the total waste

management costs. Secondly, personnel constraints lead to

ineﬃciencies, such as disorganized collection plans and

inadequate vehicles (Andeobu etal. 2022). Therefore, solu-

tions based on artiﬁcial intelligence have been developed

and implemented to optimize waste logistics and transporta-

tion processes (Abdallah etal. 2020). This involves optimiz-

ing waste transportation and logistics from four perspectives:

transportation distance, transportation cost, transportation

time, and eﬃciency.

Akdaş etal. (2021) introduced a method for vehicle rout-

ing using an ant colony optimization algorithm. First, collect

110 points of a particular city in Turkey. Then, convert the

coordinates of the points and import them into the data-

base. Visualize these points on a map and create a distance

matrix. Finally, the ant colony optimization algorithm deter-

mines the shortest route on the distance matrix. Researchers

found that the 10th iteration of the ant colony optimization

algorithm can reduce the transportation distance by 13% to

reach the optimal solution. In addition, other studies have

shown that using Dijkstra and Tabu search algorithms can

also reduce the distance of waste transportation (Rızvanoğlu

etal. 2019). First, it used the Dijkstra algorithm to calculate

the shortest distance between two coordinates. The Tabu

search algorithm is to determine the fastest path between two

coordinates. In the subsequent experiment, there were 200

waste collection points in a certain turkey area, 16,106m.

The two cases of transportation distance and 80 waste collec-

tion points, 5497m transportation distance, are optimized.

Rızvanoğlu etal. (2019) conﬁrmed that Dijkstra and Tabu

search algorithms could reduce transportation distance by

28%. In general,upon implementing the ant colony opti-

mization algorithm, waste transport distances were reduced

by 13% on average. Additionally, utilizingthe Dijkstra-Tabu

search algorithmreduced the waste transport distance by

28%.

Babaee Tirkolaee etal. (2019) presented the simulated

annealing algorithm for generating initial values based on

a random algorithm. The simulated annealing algorithm is

used for optimization based on obtaining the initial value.

An area in Iran with 330 square kilometers and 43 recycling

nodes is optimized using the simulated annealing algorithm

(Babaee Tirkolaee etal. 2019). The simulated annealing

algorithm reduced the total cost by 13.3%.

Amal etal. (2018) introduced genetic algorithms for opti-

mizing vehicle routing. First, use a geographic information

system to get the solution, including the vehicle’s route,

subsequently utilizing the genetic algorithm to optimize the

vehicle’s route. Finally, use ArcGIS and Python scripts to

represent the optimal solution. Later, in an experiment in

a city in Tunisia, after ten iterations of the genetic algo-

rithm, the running time was reduced from 15.2 to 10.91h.

After optimizing the genetic algorithm, the running time

of the vehicle was reduced by 28.22% (Amal etal. 2018).

In addition, parallel annealing algorithms are also used to

optimize vehicle collection paths (Zhang etal. 2020). Using

the parallel annealing algorithm to optimize the waste col-

lection path in Xuanwu District, Beijing, Zhang etal. (2020)

found that the optimized scheme of the parallel annealing

Fig. 4 A neural network for

predicting biogas volumeusing

four input attributes. The

diagram has eight hidden layers

and one output layer to induce

biogas prediction. It consists of

three layers: input, hidden, and

output

Environmental Chemistry Letters

1 3

algorithm can reduce the time by 12% compared with the

original scheme. However, this research also has certain

limitations. For example, the collection of vehicle speed is

a ﬁxed value, which is impossible. Overall, the genetic and

parallel annealing algorithms can reduce the transportation

time by 28.22% and 12%, respectively. However, the opti-

mization of the parallel annealing algorithm is limited by a

ﬁxed vehicle speed.

Akhtar etal. (2017) proposed an improved backtracking

search algorithm on capable vehicle routing problems mod-

eled under smart bins. Use the backtracking search algo-

rithm to optimize based on the original route. At the same

time, the data provided by the smart garbage bin is used to

ﬁnd the optimal range to reduce the number of garbage bins,

thereby minimizing the distance. After four days of simula-

tion experiments, Akhtar etal. (2017) found that the eﬃcacy

of waste collection increased by 36.78%. Algorithms and

models can be developed further if more constraints and

uncertainties are considered.

Furthermore, Nowakowski etal. (2020) proposed a har-

mony search algorithm for optimizing vehicle collection

routes. After applying the harmony search algorithm to

optimize vehicle routes for collecting electronic waste in

a region of Poland, Nowakowski etal. (2020) found that

the harmony search algorithm could increase the number

of collection points visited by 5.4%. In summary, the back-

tracking search algorithm can increase eﬃciency by 36.78%,

and the harmony search algorithm can increase the num-

ber of access points by 5.4%. Table3 summarizes the opti-

mization of artiﬁcial intelligence for waste logistics and

transportation.

Illegal dumping andwaste disposal

Illegal dumping refers to disposing of waste and garbage in

areas not designated for waste disposal, including private

and public areas (Liu etal. 2017; Lu 2019; Niyobuhungiro

and Schenck 2022). Illegal waste dumping can signiﬁcantly

impact the surrounding ecosystem, create social problems,

and pose risks to human health (Lu 2019; Niyobuhungiro

and Schenck 2021). With the rapid growth of the global

economy, the quantity of waste generated annually is ris-

ing, and crimes leading to illegal dumping are also increas-

ing (Du etal. 2021). Hence, governments worldwide must

address the problem of illegal dumping as a critical waste

management issue (Du etal. 2021; Niyobuhungiro and

Schenck 2021). Identifying illegal dumping is an inte-

gral part of the process of dealing with illegal dumping.

Municipalities have also adopted various methods to identify

Table 3 Artiﬁcial intelligence optimization for waste logistics and

transportation. The type of artiﬁcial intelligence refers to which

artiﬁcial intelligence technology is used for optimization. The type

of waste refers to what kind of waste is transported. Transportation

distance, cost, and time reduction refer to the reduction of the trans-

portation process after artiﬁcial intelligence optimization compared

to before optimization. The increase in collection eﬃciency and the

number of collection points refer to the increase in the transportation

process after artiﬁcial intelligence optimization compared to before

optimization. “–” stands for unmentioned

Types of artiﬁ-

cial intelligence

Type of waste Proportion of

reduction in

transport dis-

tance

Percentage of

cost reduction

Percentage of

time reduction

Increased ratio

in garbage col-

lection eﬃciency

Increased ratio

in the number of

collection points

References

Ant colony

optimization

algorithm

Solid waste 13% – – – – Akdaş etal.

(2021)

Dijkstra’s

algorithm and

Tabu search

algorithm

Municipal waste 28% – – – – Rızvanoğlu etal.

(2019)

Simulated

annealing

algorithm

Municipal waste – 13.35% – – – Babaee Tirkolaee

etal. (2019)

Genetic algo-

rithm

Solid waste 7.84% – 28.22% – – Amal etal. (2018)

Backtracking

search algo-

rithm

Municipal waste 36.8% – – 36.78% – Akhtar etal.

(2017)

Harmony search

algorithm

Electronic waste – – – – 5.4% Nowakowski etal.

(2020)

Parallel simu-

lated annealing

algorithm

Solid waste – – 12% – – Zhang etal.

(2020)

Environmental Chemistry Letters

1 3

illegal dumping. For example, the South Korean govern-

ment has installed cameras in areas where illegal dumping

is concentrated to monitor and arranged for supervisors to

patrol. However, installing cameras and arranging person-

nel to patrol requires many human and material resources,

but it has not achieved good results (Kim and Cho 2022).

As artiﬁcial intelligence technology advances, researchers

have explored its potential to detect illegal dumping. The

following section will discuss artiﬁcial intelligence’s use in

identifying illegal dumping cases.

Shahab and Anjum (2022) used a multipath convolutional

neural network model to identify and localize waste areas

to identify illegal dumping. Because no data set for illegal

dumping was identiﬁed in this model, Shahab and Anjum

(2022) devised a quantitative investigation procedure. After

training a multipath convolutional neural network model

with 9000 pictures, the researchers used 3000 additional pic-

tures to test the model’s classiﬁcation accuracy, which was

found to be 98.33%. Additionally, Thotapally (2022) pro-

posed a method that utilizes the faster regions with a convo-

lutional neural network features target detection framework

and the residual network algorithm as a convolutional layer

to identify street photos captured by surveillance cameras

to determine the cleanliness of the street. Facts have proved

that using the residual network algorithm as a convolutional

layer method improves the accuracy of target detection and

positioning. To sum up, both multipath convolutional neu-

ral network mode and residual network algorithm can judge

whether there is waste dumping behavior through pictures.

Among them, the accuracy rate of multipath convolutional

neural network mode recognition is 98.33%.

Based on deep neural networks, Kim and Cho (2022) pro-

posed a technique for monitoring illicit dumping that meas-

ures the distance between dumpers and garbage bags. The

illegal dumping monitoring technology uses an openpose

algorithm to identify the joints of the dumper and type of

trash bag according to the “you only look once” (YOLO)

model by detecting the distance between the dumper and

trash bag to determine whether there is illegal dumping.

After a series of experiments, the accuracy rate of the ille-

gal dumping monitoring system is 93%. Additionally, since

illegally dumped waste is often transported by trucks, Du

(2020) suggested capturing images of waste-carrying trucks

through monitoring systems and then using the YOLO algo-

rithm to detect and determine whether they are involved in

illegal dumping by comparing shape, color, and movement

changes. In summary, the “you only look once” (YOLO)

model algorithm can be used to distinguish whether there is

an illegal dumping of humans and vehicles. The accuracy

rate, when used to identify humans, is 93%.

Takahashi etal. (2022) used drones to collect river chan-

nel images and the faster regions with convolutional neural

network features (faster r-convolutional neural network)

model to train these images. Artiﬁcial intelligence can eﬀec-

tively identify garbage in pictures. However, using the faster

r-convolutional neural network model to identify garbage in

the city requires more training for a faster r-convolutional

neural network model. Moreover, Youme etal. (2021) pro-

posed using the single-shot detector algorithm (SSDA) to

identify the pictures collected by drones. The SSDA can

identify the location of garbage.

Nevertheless, the SSDA also has certain limitations. For

example, only less litter can be identiﬁed in some covered

regions such as wood areas. In addition, Padubidri etal.

(2022) demonstrated using a basic convolutional neural

network classiﬁcation model and a residual block classiﬁ-

cation model to identify and report illegal dumping sites

in high-resolution aerial imagery. With some limitations,

recognition errors may occur and may not be suitable for

recognizing low-resolution aerial images. In short, a faster

r-convolutional neural network model and SSDA can iden-

tify garbage in the drone to shoot images. Deep learning

models identify illegal leaning points in high-resolution

aerial imagery. Nevertheless, there are some shortcomings

in the above three methods.

Furthermore, Ulloa-Torrealba etal. (2023) introduced a

method of illegal dumping detection using the random forest

algorithm on the segmented high-resolution earth observa-

tion. However, this method has shortcomings, such as the

inability to identify wastes smaller than 64 square centim-

eters and the high-resolution images not being real time.

Torres and Fraternali (2021) presented residual network 50

and feature pyramid network to identify illegal dumps in

aerial images. After testing, the method can reach 88% accu-

racy. Devesa and Brust (2021) proposed a method based on

neural networks to identify illegal dumping sites in satellite

images. However, this method has some disadvantages, such

as the inability to identify small areas and obtain clear satel-

lite images when there are clouds.

To sum up, the random forest algorithm, residual net-

work 50, feature pyramid network, and neural network are

all three methods that can be used to identify illegal dump-

ing, but there are some shortcomings. Table4 summarizes

eleven methods for artiﬁcial intelligence to identify illegal

dumping.

Waste disposal reduces waste volume and accelerates

waste purification through physical, biochemical, and

pyrolysis gasiﬁcation methods. Waste disposal methods

can be categorized into four main types: waste recycling,

waste incineration, waste composting, and waste landﬁll-

ing (Chen 2022). Waste recycling involves collecting, treat-

ing, and reusing human-generated waste (Erkinay Ozdemir

etal. 2021). Waste incineration uses high-temperature and

high-pressure pyrolysis oxidation to reduce the volume of

waste and eliminate hazardous materials (Chen etal. 2022a).

Waste composting involves the controlled decomposition of

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organic matter in waste and its conversion into substrates and

fertilizers (Aydın Temel etal. 2023; Wei etal. 2022). Waste

landﬁlling involves ﬁlling waste into depressions or large

pits, followed by anti-seepage, drainage, and air-guiding

treatments.

The following sections will discuss using artiﬁcial intel-

ligence in waste disposal concerning these four methods.

The treatment and reuse of waste are two parts of waste recy-

cling, and some researchers also focus on applying artiﬁcial

intelligence in treatment and reuse.

Regarding waste treatment, Ziouzios etal. (2020) trained

a convolutional neural network model to identify diﬀerent

wastes in waste recycling. The convolutional neural network

model can classify wastes into ﬁve categories. After testing,

the accuracy of the convolutional neural network model is

as high as 96.57%. Next, the researchers will further experi-

ment to improve the accuracy of the convolutional neural

network model (Ziouzios etal. 2020). Moreover, the arti-

ﬁcial intelligence technology using transfer learning real-

ized the classiﬁcation of 12 diﬀerent models of smartphones

(Abou Baker etal. 2021).

Regarding waste reuse, Qi etal. (2018) proposed a model

for predicting strength that incorporated boosted regression

tree and particle swarm optimization. The experimental

results show that the strength prediction model can accu-

rately predict the strength and reduce the required mechani-

cal tests. In general, the convolutional neural network model

and transfer learning can be applied to waste classiﬁcation,

while boosted regression trees and particle swarm optimiza-

tion are applied to waste reuse.

Waste composting is widely used as a waste treatment

method for organic matter. However, some problems in

actual operation exist, such as maturity, heavy metals, and

carbon dioxide emissions (Guo etal. 2022; Li etal. 2022;

Sharma etal. 2021; Yang etal. 2023c). Therefore, artiﬁcial

intelligence-based solutions are developed. Sharma etal.

(2021) proposed using artiﬁcial neural network modeling

to improve the maturity parameters of ﬂower waste and cow

manure in vermicomposting. After experiments, Sharma

etal. (2021) found that the fertilizer optimized by artiﬁcial

neural network modeling has enough nutrients to beneﬁt the

growth of plants.

Regarding risk control of heavy metals in livestock and

poultry manure composting, researchers combined machine

learning models such as layer perceptron regression and

support vector regression to predict and optimize heavy

metals in pig manure composting (Guo etal. 2022). After

the experiment, Guo etal. (2022) found that the machine

learning model and genetic algorithm can eﬀectively reduce

the risk of heavy metal pollution in livestock and poultry

manure composting. In addition, Li etal. (2022) created a

new machine learning model to predict carbon dioxide from

green waste composting. A total of six diﬀerent algorithms

were used to predict carbon dioxide, with the random forest

algorithm achieving the maximum prediction precision of

88%.

In summary, artiﬁcial neural networks can optimize com-

post maturity, and machine learning models can be used to

predict heavy metals and carbon dioxide.

Table 4 Artiﬁcial intelligence models identify illegal dumping. Input

data represent the picture recognized by artiﬁcial intelligence. Types

of artiﬁcial intelligence stand for diﬀerent artiﬁcial bits of intelligence

that identify illegal dumping. Output result represents the result of

artiﬁcial intelligence recognition. YOLO is the “you only look once”

model algorithm

Input data Types of artiﬁcial intelligence Output result Reference

Pictures with or without waste Multipath convolutional neural

network model

Determine if the vehicle is dumping

garbage

Shahab and Anjum (2022)

Picture of vehicle YOLO Determine if the vehicle is dumping

garbage

Du (2020)

Images from the drone Faster regions with convolutional

neural network features

Identify the location of garbage Takahashi etal. (2022)

Pictures of the dump and the

garbage

Openpose and YOLO Determine if the dumper is dumping Kim and Cho (2022)

Security camera shot of the street Faster regions with convolutional

neural network features

Determine if the street is clean Thotapally (2022)

Images from the drone Single-shot detector algorithm Identify the location of the waste Youme etal. (2021)

High-resolution aerial images Basic convolutional neural network

classiﬁcation model

Identify illegal dumping sites Padubidri etal. (2022)

High-resolution aerial images Residual block classiﬁcation model Identify illegal dumping sites Padubidri etal. (2022)

High-resolution earth observation Random forest algorithm Identify the location of the waste Ulloa-Torrealba etal. (2023)

Aerial images Residual network 50 and feature

pyramid network

Identify illegal dumps Torres and Fraternali (2021)

Satellite images Neural networks Identify illegal dumping sites Devesa and Brust (2021)

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The waste landﬁll has become the most critical waste

disposal method in most countries because of its large-scale

operation and simple management (Mehrdad etal. 2021).

However, landﬁlls also present problems with siting, lea-

chate, and odors (Abunama etal. 2019; Mohsin etal. 2022;

Xu etal. 2022). Abunama etal. (2019) presented several

models for forecasting leachate production rates in landﬁlls.

These models included single- and double-hidden layer arti-

ﬁcial neural networks, multilinear perceptron, and support

vector machine regression time series algorithms. After

testing, Abunama etal. (2019) proved that the method of

artiﬁcial neural network-multilinear perceptron with double

hidden layers is optimal.

Meanwhile, regarding site selection for waste landﬁlls,

the fuzzy analytic hierarchy process–support vector machine

and fuzzy analytic hierarchy process–random forest inte-

grated models based on a geographic information system

were developed Mohsin etal. (2022). Three landﬁlls were

selected to apply the model to siting landﬁlls in a region of

India. Xu etal. (2022) also constructed an artiﬁcial neural

network model for ethanol, methyl sulﬁde, and dimethyl

disulﬁde. They used a genetic algorithm to predict the odor

emission rate of the landﬁll’s working face. Experiments

show that the prediction accuracy is satisfactory. Overall,

artiﬁcial neural networks can predict landﬁll leachate gen-

eration rates, vector machines and random forest models can

be used for landﬁll siting, and genetic algorithms can be

used to predict odor emission rates.

Although waste incineration is a common method of

waste disposal, an improper operation can lead to adverse

eﬀects and problems. The complexity of waste incineration

modeling arises from its nonlinear nature, strong coupling,

signiﬁcant delays, and high inertia. To overcome these dif-

ﬁculties, Chen etal. (2022a) developed an intelligent mod-

eling approach based on deep learning models, which proved

more accurate and eﬀective in simulating waste incineration

power plants. Wajda and Jaworski (2021) used an ant opti-

mization algorithm after conducting laboratory experiments

and practical tests, achieving satisfactory results. In addition,

Cho etal. (2021) employed artiﬁcial neural networks to opti-

mize the conditions of an incinerator, and their ﬁndings indi-

cated that the model could reduce nitrogenous gas emissions

by 34%. These optimization methods can help improve waste

incineration processes’ eﬃciency and environmental impact.

Identifying andrecovering valuable

resources

The rapid development of the global economy and urbaniza-

tion has increased waste production, posing a serious prob-

lem for modern society (Chen etal. 2020). Governments

mainly rely on landﬁll and waste incineration to manage

waste, especially in developing countries, but improper dis-

posal can cause environmental issues (Ferronato and Tor-

retta 2019). However, the waste contains many recyclable

materials, and recycling can reduce environmental impact

and enable waste reuse (Zhang etal. 2021b). To promote

waste recycling, many countries are implementing waste

classiﬁcation. However, manual classiﬁcation is ineﬃcient

and prone to errors, hindering progress. Researchers are

applying artiﬁcial intelligence to waste identiﬁcation and

classiﬁcation to overcome these obstacles, proposing more

reliable methods (Zhang etal. 2021b).

Zhang etal. (2021c) proposed a two-stage recogni-

tion–retrieval algorithm for waste classiﬁcation. The ﬁrst

stage involves constructing a recognition model to sort waste

into thirteen categories, and the second stage trains a recog-

nition–retrieval model to classify waste into four categories.

However, the algorithm has limitations, such as only iden-

tifying one waste type in mixed waste and low accuracy in

classifying paper, tissue, and fabric. Moreover, Sousa etal.

(2019) introduced a hierarchical deep learning method for

sorting and identifying waste in food trays, which classiﬁes

waste into four categories based on material or ten categories

based on shape using faster regions with convolutional neu-

ral network features. Similarly, Melinte etal. (2020) demon-

strated a deep convolutional neural network that can classify

municipal waste into ﬁve categories with an accuracy rate of

97.63% using single-shot detectors and faster regions with

convolutional neural network features.

Based on the image classiﬁcation model of deep learn-

ing, Zhang etal. (2021b) developed a method for adding

a self-monitoring module to the residual network model.

This model divides waste into six types according to the

material. After experiments, it was found that the model’s

accuracy was 95.87%. However, the model also has some

limitations, such as the small amount of data in the data

set, it is not realistic enough, and the actual situation in real

life is quite diﬀerent. Furthermore, Fahmi and Lubis (2022)

developed a waste recognition system using a convolutional

neural network to classify waste into inorganic and organic

substances, with each group further subdivided into ﬁve sub-

classes. After conducting experiments, the waste recognition

system achieved an average accuracy rate of 90%. Zhang

etal. (2021a) proposed a waste identiﬁcation and classiﬁca-

tion method using transfer learning and convolutional neural

networks. The method categorized waste into ﬁve classes

based on diﬀerent materials, and the model’s classiﬁcation

accuracy was 82% through testing. However, the transfer

learning and convolutional neural network models have

some limitations, such as the simplicity of waste pictures

used in the experiments and the gap between these pictures

and real-life waste.

Shi etal. (2021) proposed a multilayer hybrid convolu-

tion neural network for waste classiﬁcation, achieving an

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accuracy rate of 92.6% for classifying six waste categories

based on material. Meanwhile, Na etal. (2022) employed

image data augmentation and transfer learning to classify

construction waste into ﬁve categories based on material.

However, the model’s quality may be aﬀected by increased

data, and collecting pictures during sunrise and sunset

should be avoided. Ziouzios etal. (2022) developed a waste

detection and classiﬁcation system using convolutional neu-

ral networks, achieving a 92.43% accuracy rate for classify-

ing four waste categories based on material. However, the

system has high hardware costs and energy consumption.

In order to better classify and identify textile waste, Du

etal. (2022) have developed a deep learning model based

on a convolutional neural network to better classify textile

waste. The model can accurately classify textile waste into

13 categories based on material with a recognition time

of fewer than two seconds and an accuracy rate of 95.4%.

Bobulski and Kubanek (2021a) have demonstrated a deep

learning-based classification system that uses a convo-

lutional neural network to classify plastic waste into four

categories based on different materials. Both factories

and households can use the system. Toğaçar etal. (2020)

have proposed a waste classiﬁcation method based on a

convolutional neural network that divides waste into recy-

clable and non-recyclable categories with a precision rate of

99.5%. Furthermore, Thumiki and Khandelwal (2022) have

developed a real-time mobile application that uses image

recognition and a convolutional neural network to classify

waste into six categories based on material and determine

whether it is recyclable or non-recyclable.

In summary, artificial intelligence, particularly deep

learning models based on convolutional neural networks,

has been shown to be eﬀective in classifying waste accord-

ing to their material and shape. These models can accurately

identify and classify diﬀerent types of waste, which can help

with waste management and recycling eﬀorts, as listed in

Table5.

Improving public health andquality oflife

Implementing artiﬁcial intelligence technology to improve

sustainable waste management can help reduce the use of

natural resources without compromising the standard of

living. This ensures a reduction in the generation of solid

waste and its disposal to minimize its impact on health and

Table 5 Artiﬁcial intelligence garbage classiﬁcation and identiﬁ-

cation. The various types of artiﬁcial intelligence refer to diﬀerent

garbage classiﬁcation and identiﬁcation approaches. The classiﬁca-

tion index deﬁnes the type of classiﬁed waste, while the number of

classiﬁcations indicates the number of categories the waste is divided

into. The accuracy rate is the proportion of correct classiﬁcations to

the total number of classiﬁcations made by artiﬁcial intelligence. If

a value is not mentioned in the text, it is represented by a “–” symbol

Types of artiﬁcial intelligence Classiﬁcation index Number of

classiﬁca-

tions

Accuracy rate Reference

Recognition–retrieval model Diﬀerent types of garbage Four 94.71% Zhang etal. (2021c)

Faster regions with convolutional neural net-

work features—material

Material Four 72.8% Sousa etal. (2019)

Faster regions with convolutional neural net-

work features—shape

Shape Ten 73.6% Sousa etal. (2019)

Single-shot detectors Material Five 97.63% Melinte etal. (2020)

Faster regions with convolutional neural net-

work features

Material Five 95.76% Melinte etal. (2020)

Residual network model Material Five 95.87% Zhang etal. (2021b)

Convolutional neural network Organic and inorganic matters Ten 90% Fahmi and Lubis (2022)

Transfer learning and convolutional neural

network

Material Five 82% Zhang etal. (2021a)

Multilayer hybrid convolution neural network Material Six 92.6% Shi etal. (2021)

Image data augmentation and transfer learning Material Five – Na etal. (2022)

Convolutional neural networks Material Four 92.43% Ziouzios etal. (2022)

Convolutional neural network and deep learn-

ing

Material Thirteen 95.4% Du etal. (2022)

Deep learning and convolutional neural

network

Material Four – Bobulski and Kubanek (2021a)

Convolutional neural network Recyclable and non-recyclable Two 99.95% Toğaçar etal. (2020)

Image recognition and convolutional neural

network

Material Six – Thumiki and Khandelwal (2022)

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the environment (Yigitcanlar etal. 2021; Yusoﬀ 2018). The

amount of solid waste generated worldwide is much greater

than that of recyclables, and this trend is expected to con-

tinue (Kabirifar etal. 2021; Khudyakova and Lyaskovskaya

2021). By incorporating artiﬁcial intelligence technology

for intelligent recycling, waste classiﬁcation, and disposal

in developed and developing countries, the municipal solid

waste process can be strengthened, leading to more sustain-

able recycling methods (Tanveer etal. 2020).

To eﬀectively manage solid waste generation, creating

and implementing a strategic roadmap is important (Wath

etal. 2010; Williams 2019). Additionally, accurately predict-

ing the age of solid waste is crucial for achieving eﬃcient

municipal solid waste management, which can be accom-

plished through artiﬁcial intelligence (Yigitcanlar and Cugu-

rullo 2020). Traditional waste-sorting techniques are being

replaced by automated intelligent machines capable of mul-

titasking and sorting large amounts of solid refuse. These

machines are powered by artiﬁcial intelligence, can distin-

guish between diﬀerent types of solid waste, and exhibit

a high degree of autonomy in computer vision programs

(Wirtz etal. 2019).

Urban waste is commonly known as “municipal waste,”

and certain types of waste within this category require special

handling and management due to their explosive, toxic, or pol-

luting nature that poses a risk to public health and living con-

ditions (Olugboja and Wang 2019). Municipal-level hazard-

ous, toxic, or detrimental waste to the quality of life and living

conditions must be managed precisely. This includes waste

generated from operational processes such as combustion ash,

raw sewage, toxic enzyme oil, waste material, scrap metal,

asphalt waste, ceramic waste, slag, gravel, animal manure,

animals, grains, ashes, artiﬁcial waste, materials used in urban

waste management, menstrual waste, and the generation of

menstrual waste, all of which have the potential to cause harm,

toxicity, or infection to public health and the environment.

Intelligent bins exemplify how artiﬁcial intelligence is

employed in municipal solid waste management. Waste man-

agement companies can utilize artiﬁcial intelligence technol-

ogy to monitor garbage bins’ ﬁll levels throughout a city.

Municipalities and recycling companies can optimize their

trash collection schedules, routes, and frequencies (Brynjolfs-

son and Mcafee 2017; Ortega-Fernández etal. 2020). This

optimization reduces the time required to empty the bins

while also reducing labor and fuel costs. Furthermore, artiﬁ-

cial intelligence technology can detect when a bin is full and

distinguish between various waste types. For example, smart

bins can rapidly classify and sort garbage using machine

learning algorithms (Yigitcanlar and Cugurullo 2020).

Artiﬁcial intelligence is critical in solid waste manage-

ment by facilitating classiﬁcation (Gundupalli etal. 2017).

Artiﬁcial intelligence is used for intelligent classiﬁcation

by using cameras to automatically scan and analyze items

on a conveyor belt using deep learning algorithms, similar

to how it is used in manufacturing (Gundupalli etal. 2017;

McKinnon etal. 2017). Recent studies have demonstrated

that artiﬁcial intelligence-powered machines can process up

to 160 recyclable materials per minute, compared to 30 to

40 materials per minute for human workers (Andeobu etal.

2022). Moreover, artiﬁcial intelligence-powered machines

can operate continuously, highlighting deﬁciencies in clas-

siﬁcation and recycling facilities (McKinnon etal. 2017).

Traditionally, solid waste management has been a labor-

intensive process. However, thanks to advances in artiﬁcial

intelligence, computer vision, robotics, and other cutting-

edge technologies, municipalities can now improve public

health and quality of life while reducing costs and eliminat-

ing the need for manual labor.

In summary, using artiﬁcial intelligence in waste man-

agement is becoming increasingly popular. An example is

developing an artiﬁcial intelligence-based hybrid intelli-

gent framework, which optimizes waste management and

improves urban environment monitoring using graph the-

ory and artiﬁcial intelligence technologies (Ihsanullah etal.

2022). By employing diﬀerent approaches and algorithms

based on artiﬁcial intelligence, this system can better accom-

modate various demographic groups, enhance environmen-

tal planning, and improve urban management’s eﬃciency,

accuracy, and performance. Results show that the method

improves the eﬃciency and accuracy of waste processing

compared to other existing methods (Yu etal. 2021).

Since the outbreak of the novel coronavirus (COVID-19)

pandemic, the rapidly increasing amount of medical waste has

posed more signiﬁcant challenges to various regions’ disposal

facilities and management capabilities. Medical waste has the

characteristics of spatial pollution, acute infection, and latent

infection, which can directly endanger human health. It can

also cause severe consequences through soil pollution, water

bodies, and the atmosphere (Yang etal. 2022a). As shown in

Fig.5, coronavirus disease 2019 (COVID-19) changes the

composition of waste and the eﬃciency of waste disposal and

increases the risk of infection in the population. Human inter-

vention in solid waste management can be reduced through

advanced intelligent waste management technologies, such as

machine learning-based image classiﬁcation and reliable item

detection. Speciﬁc materials that improve ecological sustain-

ability after a signiﬁcant outbreak can be eﬀectively recy-

cled. These technological interventions will reduce the risk of

human factor contamination in the waste management cycle,

thus breaking the potential transmission chain of COVID-19

and similar viruses (Rubab etal. 2022).

The proposed medical waste monitoring and assistance

system utilizes artiﬁcial intelligence to perform deep mining

and analysis of personnel disposal behavior in the tempo-

rary storage of medical waste and working areas of medical

and health institutions. The system can intelligently identify

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1 3

illegal and irregular behaviors on the scene and synchro-

nously push alarm information to medical institutions and

law enforcement personnel (Yu etal. 2021). The aim is to

improve the management and disposal of medical waste,

reduce the risks of infectious disease transmission and envi-

ronmental pollution caused by medical waste, and ensure

public health and safety (Lakshmi etal., 2015). By utilizing

the technology of “internet + video surveillance and data

tracking,” artiﬁcial intelligence can intelligently recognize

illegal behaviors related to waste management without the

need for the physical presence of law enforcement personnel.

This can help reduce the risk of infection and transmission

of diseases while also improving the overall eﬃciency of

waste management supervision.

In summary, improper disposal of solid waste can adversely

impact human health and the environment. However, current

solid waste management systems struggle to keep up with

the ever-increasing amount of waste produced globally (Popa

etal. 2017). There are certain recyclable materials that some

municipal solid waste and recycling services cannot pro-

cess. The integration of artiﬁcial intelligence in solid waste

management is a growing trend and has the potential to sig-

niﬁcantly improve sustainable waste management practices

(Khudyakova and Lyaskovskaya 2021). Adopting artiﬁcial

intelligence-driven automation in the management process of

municipal solid waste will provide a sustainable approach to

recycling and disposal (Popa etal. 2017). Thus, implementing

these advancements in solid waste management can promote

a healthier and better quality of life for all.

Designing waste management systems

forsmart cities

In the pursuit of creating environmentally friendly cities,

waste management plays a critical role. Ensuring sustain-

able and livable urban areas requires improving solid waste

management services and reducing waste, as hazardous solid

waste can negatively impact air quality and soil safety (Her-

ath and Mittal 2022). With the rise of smart cities, artiﬁcial

intelligence technology in waste management has become

more prevalent, primarily as a modeling and prediction tool

for simulation and optimization. The most recent applica-

tions have primarily focused on modeling and optimizing

solid waste generation, as well as predictive recycling pro-

cesses, as demonstrated in Table6.

Studies have suggested that integrating waste manage-

ment into future smart cities with entire product lifecycles

could be a potential step toward achieving “zero waste”

(Lee etal. 2021). To reach this goal, three stages must be

Fig. 5 Impact of the coronavirus disease 2019 (COVID-19) on waste

management. The COVID-19 pandemic has signiﬁcantly aﬀected

the composition, timing, and frequency of waste disposal. It has

also increased the risk of infection for the public due to the produc-

tion of masks and medical waste that require manual handling. These

changes in waste volumes have complex and interrelated impacts on

municipal waste management, as depicted in the chart

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Table 6 Artiﬁcial intelligence models and machine learning algorithms for solid waste prediction, reduction, and recycling. Algorithms diﬀer in their information requirements for machine

training, and their sizes vary across diﬀerent algorithms. Each algorithm comes with its unique set of advantages

Input parameters Output parameters Types of artiﬁcial intelligence

technology

Scale Advantage Reference

Vehicle journeys and the monthly

volume of solid waste from the

weight bridge

Estimation of the landﬁll’s size

based on produced and col-

lected solid refuse

Feed-forward back-propagation

neural network

Medium term Artiﬁcial neural networks learn-

ing methods are highly resistant

to noise in a data set’s training

data

Hoque and Rahman (2020)

Generation of municipal solid

refuse in monthly time series

Generation of municipal solid

waste

Support vector machines Short term Function well when there is a

maximum marginal splitting of

groups

Abbasi and El Hanandeh (2016)

Historical statistics on the

weather, historical data on the

amount of household waste

collected, and historical tonnage

data

Weekly municipal solid waste

generation tonnage

Gradient boosting decision tree Short term It can be used for large data

sets, and good for prediction

problems

Johnson etal. (2017)

Individual building attributes,

neighborhood socioeconomic

characteristics, weather, and

selected route levels are col-

lected data

Construction-grade municipal

solid waste generation

Gradient boosting decision tree Medium term The linear regression function

demonstrated a higher correla-

tion coeﬃcient for the training

data set than other models

Kontokosta etal. (2018)

Temperature, pH, stirring, and

time of municipal waste-acti-

vated sludge pretreatment

Enzymatic activity Multilayer perceptron network Short term Selvakumar and Sivashanmugam

(2018)

Biomass sludge ratio, heating

rate, and temperature

Percentage of massive losses Multilayer perceptron network Short term Chen etal. (2017)

Human power, water, electricity,

gas, and transportation

Biodepletion potential, acidi-

ﬁcation potential, and eight

other environmental impact

categories, as well as recycled

materials

Multilayer perceptron network Short term Nabavi-Pelesaraei etal. (2017)

Tea waste volume, pH, polyacry-

lonitrile concentration, sample

and eluate ﬂow, eluate volume,

and lotion concentration

Percentage of extraction of man-

ganese and cobalt

Particle swarm neural network

inverse

Short term Sensitivity analysis can be

performed

Nabavi-Pelesaraei etal. (2017)

Environmental Chemistry Letters

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undertaken: Waste prevention, precise refuse collection,

and functional value recovery from collected waste are all

priorities (Yang etal. 2023a). Furthermore, the Internet of

things waste management networks should be encouraged to

improve the life cycle of products and their recycling value

(Shukla and Hait 2022).

To eﬀectively implement and manage waste models for

smart cities, it is crucial to intelligently estimate solid waste

generation. In this regard, artiﬁcial intelligence technologies

such as artiﬁcial neural networks, support vector machines,

decision trees, and adaptive neuro-fuzzy inference systems

have been increasingly used due to their practical predictive

capabilities for modeling the production of municipal solid

waste (Ihsanullah etal. 2022). Artiﬁcial intelligence-based

models in waste management research are commonly cat-

egorized by the forecast period duration: short term (days

to months), medium term (up to 3–5years), and long term

(years in advance). Recent studies have demonstrated prom-

ising results in utilizing artiﬁcial intelligence with historical

data, such as sociodemographic, economic, and manage-

ment-focused data. Additionally, combining artiﬁcial intel-

ligence with conventional waste management systems can

be achieved by integrating the Internet of things technology

(Ijemaru etal. 2022).

Artificial intelligence techniques have been used in

multiple studies to predict speciﬁc types of solid waste

generation, such as plastic waste (Kumar etal. 2018) and

household packaging waste (Oliveira etal. 2019), and have

proven to be eﬃcient and feasible. However, analyzing and

selecting key metrics is crucial to achieving accurate predic-

tion performance and ensuring the dataset’s completeness

and adequacy. In addition to waste forecasting, automated

waste sorting and management are essential for better waste

recycling. Advanced technologies, particularly the idea of

smart cities, require this model. Artiﬁcial waste separation

is inappropriate for smart cities, so smart waste classiﬁcation

models are typically multilayer convolutional deep learning

models with some physical requirements. These require-

ments include a system with a conveyor belt, a pusher, and

a garbage basket that will collect the garbage pushed by

the hammer according to the waste category (Gondal etal.

2021). Therefore, artiﬁcial intelligence-based models can

accurately forecast and evaluate solid waste generation and

automate waste management and sorting for better recycling,

which is crucial for cutting-edge technologies like smart

cities.

In general, the techniques used in waste management

applications fall into the following four categories (Zhang

etal. 2012):

i. Space technology includes using global navigation

and geographic information systems to track waste

and manage waste collection and disposal.

ii. Identiﬁcation techniques: such as radio frequency

identiﬁcation tags and bar codes which enable eﬃcient

waste tracking and monitoring

iii. Data acquisition technology, such as sensors and

imaging, provides valuable waste generation and

composition data, enabling better waste management

decisions.

iv. Data communications technology, including wireless

ﬁdelity, Bluetooth, and global mobile communication

systems, facilitates communication and data transfer

between waste management systems and stakeholders

The steps to achieve “zero waste” include the follow-

ing three main modules and components (Shukla and Hait

2022):

i. Establishing a framework for collating product life

cycle data.

ii. Encouraging responsible citizenship through innova-

tive waste reduction ideas.

iii. Developing an intelligent infrastructure with sensor-

based technology for adequate garbage segregation,

collection, and recycling.

In summary, smart cities have emerged as a global

model that prioritizes sustainability. With the help of com-

puting, networking, and data management advancements,

institutions have successfully implemented eﬃcient waste

management systems that enhance the quality of life for

citizens. However, the proposed model must be adapted

to diﬀerent demographics, waste types, and social needs

and calibrated through real-world pilot studies. Extensive

research into waste management in smart cities has pro-

duced numerous results.

Process eciency andcost savings

There is great potential to automatically detect waste in

natural settings to improve waste management eﬃciency.

For instance, Zhou etal. (2023) proposed a few-shot waste

detection framework that employs faster regions with con-

volutional neural network features (faster r-convolutional

neural network) to detect waste automatically in natural

settings, thereby improving waste management eﬃciency.

Their experiments revealed that this framework outper-

formed state-of-the-art detectors with 1.68% accuracy.

However, using a faster r-convolutional neural network in

the framework resulted in high computational complex-

ity and slow operation speed. Moreover, Alqahtani etal.

(2020) presented an urban waste management system that

Environmental Chemistry Letters

1 3

employs the cuckoo search algorithm to analyze waste

sources, types, and vehicle capacity to optimize waste

collection.

Experimental results have shown that waste recycling

systems can improve waste management efficiency and

collect waste within 15min. Akhtar etal. (2017) proposed

an enhanced backtracking search algorithm for optimizing

waste collection routes based on the smart bin concept. The

algorithm uses data from the smart garbage bins to identify

the optimal range and reduce the number of garbage bins,

thereby minimizing distance. After four days of simulation

experiments, they reported a 36.78% increase in the eﬃ-

cacy of waste collection. These algorithms and models can

be further developed by considering more constraints and

uncertainties.

Shreyas Madhav etal. (2022) have developed a convolu-

tional neural network-based recognition system for e-waste

classiﬁcation. The system can classify e-waste into eight

categories with 96% accuracy, potentially leading to a

20% cost reduction within ﬁve years if implemented as a

replacement for manual classiﬁcation. To optimize waste

collection, Internet of things (IoT) -based waste manage-

ment software can collect relevant information, and the route

of waste collection vehicles can be optimized using an ant

colony optimization algorithm. Experiments have reported

a 30% reduction in the direct cost of waste collection using

this algorithm (Oralhan etal. 2017). Meanwhile, Babaee

Tirkolaee etal. (2019) proposed a simulated annealing algo-

rithm for generating initial values based on a random algo-

rithm, which was then used for optimization. The algorithm

was applied to an area in Iran with 330 square kilometers

and 43 recycling nodes, reducing the total cost by 13.3%.

In summary, developing the convolutional neural net-

work-based recognition system for e-waste classiﬁcation

can improve accuracy and reduce costs. Optimizing waste

collection routes using algorithms such as ant colony opti-

mization or simulated annealing can lead to signiﬁcant cost

savings.

Over the years, the use of artiﬁcial intelligence technol-

ogy in various waste management ﬁelds has risen. However,

with its increasing use, some potential challenges have also

emerged. Among these is the black box problem of artiﬁcial

intelligence, which arises due to the complexity of internal

structures of most artiﬁcial intelligence models, relatively

independent operation processes, and diﬃculty in estimating

the relative signiﬁcance of each variable, thereby limiting

manual intervention (Ihsanullah etal. 2022). These black

box problems can lead to uncertainty in applying artiﬁcial

intelligence models (Guo etal. 2021).

The training and testing of artiﬁcial intelligence mod-

els, especially those that utilize deep learning and machine

learning, require signiﬁcant data. However, the waste man-

agement industry, particularly in developing countries,

suﬀers from data scarcity and incomplete data, which hinder

current research (Abdallah etal. 2020). Insuﬃcient and out-

dated data can result in overﬁtting and reduced model train-

ing eﬃciency (Guo etal. 2021). One example is the deep

neural network, which relies heavily on extensive testing and

experimentation using large datasets (Ihsanullah etal. 2022).

Artiﬁcial intelligence has been increasingly implemented

in waste management, but researchers often rely on preex-

isting models like faster regions with convolutional neural

network features or convolutional neural networks. However,

a lack of customized artiﬁcial intelligence models designed

speciﬁcally for waste management exists. This requires col-

laboration between waste management and computational

technology teams to develop tailored models (Abdallah etal.

2020).

It can be concluded that implementing artiﬁcial intelli-

gence in waste management has the potential to enhance

eﬃciency and decrease costs. However, challenges like black

box problems, inadequate data, and a lack of customized

artiﬁcial intelligence models for waste management still

exist. Figure6 illustrates three potential obstacles that may

arise in the application of artiﬁcial intelligence in waste

management.

Limitation andprospects

In terms of artiﬁcial intelligence to optimize waste logistics

and transportation, Zhang etal. (2020) pointed out that when

the back-oﬀ algorithm optimizes the path of garbage collec-

tion vehicles, there is a limitation that the collection vehicles

are at a ﬁxed speed, which is impossible in real life.

In terms of artiﬁcial intelligence to identify illegal dump-

ing, Takahashi etal. (2022) proposed that the faster regions

with convolutional neural network features model can only

be used to identify waste near rivers and cannot be used to

identify waste in other complex areas such as cities. Moreo-

ver, there are also certain limitations in using the single-shot

detector algorithm to identify waste in drone images. The

algorithm cannot identify waste in covered areas such as

woods (Youme etal. 2021). Additionally, Padubidri etal.

(2022) proposed a method using a deep learning model

to identify illegal dumping sites in high-resolution aerial

images, which may suﬀer from identiﬁcation errors and is

not suitable for identifying low-resolution aerial images.

Regarding the use of artiﬁcial intelligence for waste

identiﬁcation and sorting, the waste images used to train

transfer learning and convolutional neural network mod-

els for waste classiﬁcation are much less complex than

real-world waste, leading to reduced accuracy rates in

practical applications (Zhang etal. 2021a). Additionally,

researchers have explored using image data augmentation

and transfer learning to identify and classify construction

Environmental Chemistry Letters

1 3

waste. Increasing the amount of data reduces the quality

of the model. Considering the picture quality, it is best to

avoid sunrise and sunset when collecting pictures. In addi-

tion, waste detection and classiﬁcation systems based on

convolutional neural networks have high hardware costs

and energy consumption limitations (Ziouzios etal. 2022).

Learn more about the mechanics of artiﬁcial intelli-

gence models. Because of the black box characteristics of

artiﬁcial intelligence models, it is diﬃcult for people to

understand the mechanism of artiﬁcial intelligence mod-

els (Guo etal. 2021). Now, there are some methods to

explain the inﬂuence of input variables on artiﬁcial intel-

ligence models, such as using visualization technology;

Selvaraju etal. (2020) use class activation mapping to

explain the mechanism of convolutional neural network.

However, only a few methods have been applied to explain

the mechanisms of artiﬁcial intelligence models. In the

future, researchers could develop more ways to interpret

artiﬁcial intelligence models for greater understanding

(Lin etal. 2022).

Combine artiﬁcial intelligence models with other tech-

nologies (Guo etal. 2021). For example, combining artiﬁcial

intelligence models and Internet of things (IoT) technology

allows artiﬁcial intelligence to be better applied to waste

management (Guo etal. 2021). In addition, it is also possible

to combine artiﬁcial intelligence models and data science.

This can provide more high-quality data for training artiﬁcial

intelligence models to improve the quality of the model (Lin

etal. 2022).

The combined use of multiple artificial intelligence

models is an inevitable trend. Artiﬁcial intelligence mod-

els include but are not limited to convolutional neural net-

works, residual network models, and gradient enhancement

regression models.Most existing research focuses on a

single artiﬁcial intelligence model for waste management.

Using a combination of multiple models for waste manage-

ment has better accuracy than a single model and eﬀectively

prevents overﬁtting (Guo etal. 2021). This can better solve

the service management problem.

In summary, the limitations of artiﬁcial intelligence in

waste management are primarily related to its diﬃculty in

practical operation compared to theory, the limited scope of

application, and lower eﬃciency in practical use. The poten-

tial for future progress lies incomprehensively understand-

ing the mechanisms behind artiﬁcial intelligence models,

combining artiﬁcial intelligence with other technologies,

and using multiple models, such asconvolutional neural

network, residual network model, and gradient enhancement

regression model .

Conclusion

Waste disposal is ineﬃcient, leading to severe environmental

pollution, high costs, and a lack of leadership in the dis-

posal process. Waste management is a challenge for both

developed and developing countries. However, artiﬁcial

intelligence can improve treatment eﬃciency, reduce envi-

ronmental damage, and provide computational solutions for

smarter waste management. This review is divided into nine

sections, including deﬁnitions and the application of arti-

ﬁcial intelligence in waste management. It highlights the

potential impact of artiﬁcial intelligence on waste manage-

ment, with practical applications such as smart bin systems,

waste-sorting robots, and predictive waste tracking models.

Artiﬁcial intelligence can also assist in managing hazardous

Fig. 6 Three challenges of artiﬁcial intelligence in waste manage-

ment. Implementing artiﬁcial intelligence in waste management can

be summarized as black box problems, a lack of data, and a shortage

of suitable models. Black boxes refer to the complexity of artiﬁcial

intelligence models, which makes it diﬃcult for researchers to under-

stand their mechanisms. Lack of data refers to the scarcity and unreli-

ability of data in the waste management industry, making it challeng-

ing to train artiﬁcial intelligence models. Finally, the lack of suitable

models means that most existing applications of artiﬁcial intelligence

in waste management rely on preexisting models rather than custom

models explicitly developed for waste management

Environmental Chemistry Letters

1 3

waste, reducing illegal dumping, and recovering valuable

resources from the waste stream. Additionally, artiﬁcial

intelligence can aid public health interventions, including

medical waste disposal and pandemic response.

The paper examines the impact of artiﬁcial intelligence

on waste logistics and transportation, including reducing

distance, cost, and collection time and improving collection

eﬃciency. Although some algorithms have limitations, artiﬁ-

cial intelligence can optimize waste treatment methods such

as recycling, composting, landﬁll, and incineration. Machine

learning, artiﬁcial intelligence, and deep learning techniques

can improve waste classiﬁcation, predict heavy metal levels

in compost, and model waste incineration processes. Envi-

ronmental variables such as temperature, humidity, and light

can aﬀect waste management artiﬁcial intelligence systems,

causing ﬂuctuations. Despite these challenges, artiﬁcial

intelligence can change how people deal with waste, lead-

ing to a more sustainable future with eﬃcient, economic,

ecological, and intelligent waste management systems.

Acknowledgements Dr. Ahmed I. Osman and Prof. David W. Rooney

wish to acknowledge the support of The Bryden Centre project (Project

ID VA5048), which was awarded by The European Union’s INTER-

REG VA Programme, managed by the Special EU Programmes Body

(SEUPB), with match funding provided by the Department for the

Economy in Northern Ireland and the Department of Business, Enter-

prise and Innovation in the Republic of Ireland.

Declarations

Conflict of interest The authors have not disclosed any competing in-

terests.

Open Access This article is licensed under a Creative Commons Attri-

bution 4.0 International License, which permits use, sharing, adapta-

tion, distribution and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s) and the source,

provide a link to the Creative Commons licence, and indicate if changes

were made. The images or other third party material in this article are

included in the article's Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not included in

the article's Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will

need to obtain permission directly from the copyright holder. To view a

copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.

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Mar 2025

Dillip Kumar Das

Many countries across the Global South strive to align their urban development with sustainability goals. Consequently, the notion of smart sustainable cities has emerged, integrating the ideas of smart cities and sustainability. The region faces diverse challenges, including rapid population growth and financial constraints. Infrastructural deficiencies, especially in digital infrastructure and AI adoption, add to these challenges. Therefore, exploring digital technologies and AI is essential for developing smart, sustainable cities in the Global South. This paper examined both the potential and barriers to digital technologies and AI. It also explored policy implications and proposes a framework for smart sustainable cities. A qualitative methodological approach is used, including a systematic literature review and case studies. The study demonstrates how various urban challenges can be addressed with digital technologies and AI, alongside the barriers to their adoption. The study proposes a conceptual framework with three key pillars: adopting digital technologies and AI as the pivotal element, overcoming barriers, and identifying application areas to transform cities into smart sustainable cities. Moreover, the paper discusses policy implications and suggests future directions for research.

Pioneering Data-Driven Environmental Governance Through Artificial Intelligence

Conference Paper

Full-text available

Jan 2025

Mohammad Hossein Mohammadi Ashnani

Environmental governance in the contemporary era is confronted with complex challenges such as climate change, environmental pollution, and the depletion of natural resources. To overcome these challenges, there is a need for innovative and efficient approaches that can process and analyze vast amounts of environmental data to facilitate evidence-based decision-making. Artificial intelligence (AI) has emerged as a powerful tool in this context, capable of revolutionizing environmental governance through predictive modeling, intelligent monitoring systems, and data-driven decision-making tools. This research investigates the transformative role of AI in environmental governance through data-driven approaches. Employing a mixed-methods approach that includes a systematic literature review, qualitative content analysis, and semi-structured interviews with experts, this study identifies and analyzes the key applications of AI in eight core areas of environmental governance. The findings reveal that AI has significant potential to improve climate change modeling, biodiversity monitoring, air and water quality management, natural resource optimization, waste management, disaster risk reduction, industrial risk assessment, and ecosystem restoration. However, important ethical challenges such as privacy concerns, exacerbating inequalities, and the need for transparency in decision-making have also been identified. This study emphasizes the need to develop robust governance frameworks to ensure the responsible and equitable use of AI in environmental governance.

Innovative Approaches to Recover Waste in the Textile Sector

Chapter

Feb 2025

Research on Innovative Strategies of Artificial Intelligence Technology in Smart City Management and Its Practical Effects

Article

Full-text available

Mar 2025

Zhisong Wu

Smart city management is the construction direction of the city in the new era, this paper improves the work on smart city management and establishes a smart city management evaluation system. Adopting the method of combining case and empirical research, City A is selected as the research object to construct a performance evaluation system with 17 performance evaluation indexes from five aspects: infrastructure, smart government, people-friendly services, security and development. The hierarchical analysis method and entropy weight method are used to derive the sovereign weight and objective weight respectively, and the two are combined to derive the comprehensive weight, and the Topsis model is used to evaluate the performance level of smart city management in City A. The final performance evaluation results are obtained after obtaining the closeness of each indicator in City A to the ideal solution and comparing the size of the closeness to the ideal solution of different cities. The safeguard index and beneficiary service index are the most and least weighted ones in the criterion layer, respectively. City A ranks fourth in the overall evaluation of smart management performance. There is still room for improvement in the smart government indicators and safeguard indicators. It has advantages in infrastructure indicators, people-friendly service indicators, and developmental indicators. In addition, the combined effect of multiple factors is much greater than the effect of single factors in enhancing the occurrence of spatial differentiation in the smart management performance of City A.

Research on the Integration and Innovation of Artificial Intelligence in Intangible Cultural Heritage Illustration Creation

Article

Mar 2025

Incorporation of AI into the developmental process of illustrations of ICH is not only a great advancement in the process of utilizing technology to put into practice ICH, but also shows a shift from the static use of traditional cultural factors in the representations of the ICH. In this research context, references shall be made to how information science and AI, particularly in connection with computer technologies, can be used for better visualization and sharing of intangible cultural heritage with generations to come. This paper discusses how the AI computational methods, especially the deep learning and generative models can mine and replicate the historical and cultural data to generate new, relevant, but culturally authentic illustrations of the heritage. This research will also establish how AI tools can recreate and reimagine traditional signifiers belonging to intangible cultural heritage by using image recognitions, natural language processing, and generative adversarial networks (GANs). Unlike traditional arts that have to be copied to conform to the current standards, these technologies not only replicate, but they also bring in new approaches by providing novel interpretations to traditional arts while at the same time conserving their originality as discussed below. This is important because it is only now that due to the advancement of AI, culturally relevant illustrations are created, which can be shared through digital platforms making heritage more accessible. The results will help to determine whether AI can be used as an instrument that can be effective in the sphere of conservation, as well as open up a possibility for further creation in the sphere of cultural heritage. This research will also provide a reference point for artists, historians and cultural organizations, who want to use AI in conserving and repurposing traditional or cultural asset in the modern socio-technological context.

CAHIERS MAGELLANES-NS SUSTAINABLE AI-POWERED WASTE MANAGEMENT: IMPROVING HEALTH AND SAFETY OUTCOMES FOR WASTE PICKERS IN JAMSHEDPUR CITY THROUGH INNOVATIVE TECHNOLOGICAL SOLUTIONS

Conference Paper

Full-text available

Mar 2025

CAHIERS MAGELLANES-NS SUSTAINABLE AI-POWERED WASTE MANAGEMENT: IMPROVING HEALTH AND SAFETY OUTCOMES FOR WASTE PICKERS IN JAMSHEDPUR CITY THROUGH INNOVATIVE TECHNOLOGICAL SOLUTIONS

Article

Full-text available

Dec 2024

Waste pickers play a vital role in any Indian city's urban waste management system. However, these workers typically face substantial health risks as a result of their exposure to hazardous chemicals and bad working conditions. This empirical study looks at how waste pickers in Jamshedpur City could have better health and safety outcomes through sustainable waste management systems driven by AI. This study attempts to enhance waste management's effectiveness and safety by integrating cutting-edge technological solutions with traditional approaches. Three primary uses of artificial intelligence (AI) are now being investigation: garbage collection; real-time monitoring of hazardous waste sites; and waste transportation and route tracking. The amount of time waste pickers spends collecting waste is reduced when artificial intelligence (AI) tools are used in the waste management process. Intelligent sorting systems lessen the risks that manual sorting poses to human health by automating the separation of recyclable items. For the waste management process to function more smoothly and efficiently, real-time monitoring of garbage collection is also beneficial. The goal of this study was to determine how waste pickers who collect waste from Jamshedpur's towns improve their health as a result of using artificial intelligence (AI). In addition, this study seeks to determine how garbage pickers, who all labour in hazardous conditions and demanding environments, might reduce their risk of illness by using artificial intelligence. To examine the impact of waste pickers' usage of AI technologies in waste management, primary data has been gathered from 117 respondents from Jamshedpur. The impact of AI technologies on waste pickers' health is being examined by the authors using an exploratory and descriptive methodology. The findings of this study will provide valuable insights to waste pickers, govt. and policy makers in designing appropriate strategies to enhance overall health of waster pickers in the society.

Mapping illegal waste dumping sites with neural-network classification of satellite imagery

Conference Paper

Full-text available

Oct 2021

Public health and habitat quality are crucial goals of urban planning. In recent years, the severe social and environmental impact of illegal waste dumping sites has made them one of the most serious problems faced by cities in the Global South, in a context of scarce information available for decision making. To help identify the location of dumping sites and track their evolution over time we adopt a data-driven model from the machine learning domain, analyzing satellite images. This allows us to take advantage of the increasing availability of geo-spatial open-data, high-resolution satellite imagery, and open source tools to train machine learning algorithms with a small set of known waste dumping sites in Buenos Aires, and then predict the location of other sites over vast areas at high speed and low cost. This case study shows the results of a collaboration between Dymaxion Labs and Fundación Bunge y Born to harness this technique in order to create a comprehensive map of potential locations of illegal waste dumping sites in the region.

Microplastic sources, formation, toxicity and remediation: a review

Article

Full-text available

Apr 2023
ENVIRON CHEM LETT

Microplastic pollution is becoming a major issue for human health due to the recent discovery of microplastics in most ecosystems. Here, we review the sources, formation, occurrence, toxicity and remediation methods of microplastics. We distinguish ocean-based and land-based sources of microplastics. Microplastics have been found in biological samples such as faeces, sputum, saliva, blood and placenta. Cancer, intestinal, pulmonary, cardiovascular, infectious and inflammatory diseases are induced or mediated by microplastics. Microplastic exposure during pregnancy and maternal period is also discussed. Remediation methods include coagulation, membrane bioreactors, sand filtration, adsorption, photocatalytic degradation, electrocoagulation and magnetic separation. Control strategies comprise reducing plastic usage, behavioural change, and using biodegradable plastics. Global plastic production has risen dramatically over the past 70 years to reach 359 million tonnes. China is the world's top producer, contributing 17.5% to global production, while Turkey generates the most plastic waste in the Mediterranean region, at 144 tonnes per day. Microplastics comprise 75% of marine waste, with land-based sources responsible for 80–90% of pollution, while ocean-based sources account for only 10–20%. Microplastics induce toxic effects on humans and animals, such as cytotoxicity, immune response, oxidative stress, barrier attributes, and genotoxicity, even at minimal dosages of 10 μg/mL. Ingestion of microplastics by marine animals results in alterations in gastrointestinal tract physiology, immune system depression, oxidative stress, cytotoxicity, differential gene expression, and growth inhibition. Furthermore, bioaccumulation of microplastics in the tissues of aquatic organisms can have adverse effects on the aquatic ecosystem, with potential transmission of microplastics to humans and birds. Changing individual behaviours and governmental actions, such as implementing bans, taxes, or pricing on plastic carrier bags, has significantly reduced plastic consumption to 8–85% in various countries worldwide. The microplastic minimisation approach follows an upside-down pyramid, starting with prevention, followed by reducing, reusing, recycling, recovering, and ending with disposal as the least preferable option.

Strategies to save energy in the context of the energy crisis: a review

Article

Full-text available

Mar 2023
ENVIRON CHEM LETT

New technologies, systems, societal organization and policies for energy saving are urgently needed in the context of accelerated climate change, the Ukraine conflict and the past coronavirus disease 2019 pandemic. For instance, concerns about market and policy responses that could lead to new lock-ins, such as investing in liquefied natural gas infrastructure and using all available fossil fuels to compensate for Russian gas supply cuts, may hinder decarbonization efforts. Here we review energy-saving solutions with a focus on the actual energy crisis, green alternatives to fossil fuel heating, energy saving in buildings and transportation, artificial intelligence for sustainable energy, and implications for the environment and society. Green alternatives include biomass boilers and stoves, hybrid heat pumps, geothermal heating, solar thermal systems, solar photovoltaics systems into electric boilers, compressed natural gas and hydrogen. We also detail case studies in Germany which is planning a 100% renewable energy switch by 2050 and developing the storage of compressed air in China, with emphasis on technical and economic aspects. The global energy consumption in 2020 was 30.01% for the industry, 26.18% for transport, and 22.08% for residential sectors. 10–40% of energy consumption can be reduced using renewable energy sources, passive design strategies, smart grid analytics, energy-efficient building systems, and intelligent energy monitoring. Electric vehicles offer the highest cost-per-kilometer reduction of 75% and the lowest energy loss of 33%, yet battery-related issues, cost, and weight are challenging. 5–30% of energy can be saved using automated and networked vehicles. Artificial intelligence shows a huge potential in energy saving by improving weather forecasting and machine maintenance and enabling connectivity across homes, workplaces, and transportation. For instance, 18.97–42.60% of energy consumption can be reduced in buildings through deep neural networking. In the electricity sector, artificial intelligence can automate power generation, distribution, and transmission operations, balance the grid without human intervention, enable lightning-speed trading and arbitrage decisions at scale, and eliminate the need for manual adjustments by end-users.

Litter on the streets - solid waste detection using VHR images

Article

Full-text available

Feb 2023

Failures in urban areas’ solid waste management lead to clandestine garbage dumping and pollution. This affects sanitation and public human hygiene, deteriorates quality of life, and contributes to deprivation. This study aimed to test a combination of machine learning, high-resolution earth observation and GIS data to detect diverse categories of residual waste on the streets, such as sacks and construction debris. We conceptualised five different classes of solid waste from image interpretation: “Sure”, “Half-sure”, “Not-sure”, “Dispersed”, and “Non-garbage”. We tested a combination of k-means-based segmentation and supervised random forest to investigate the capabilities of automatic classification of these waste classes. The model can detect the presence of solid waste on the streets and achieved an accuracy of up from 73.95%–95.76% for the class “Sure”. Moreover, a building extraction using an EfficientNet deep-learning-based semantic segmentation allowed masking the rooftops. This improved the accuracy of the classes “Sure” and “Non-garbage”. The systematic evaluation of all parameters considered in this model provides a robust and reliable method of solid waste detection for decision-makers. These results highlight areas where insufficient waste management affects the citizens of a given city.

Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis

Article

Full-text available

Feb 2023
Int J Environ Res Publ Health

Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA–k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA–k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m−2), 993.54 (kg·m−2) and 991.80 (kg·m−2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions.

Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction

Article

Full-text available

Feb 2023

The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.98, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.

GIS based MCDM for Waste disposal site selection in Dejen town, Ethiopia

Article

Full-text available

Jun 2023

Selecting a properly identified waste disposal site is the most complex and difficult task due to the social, environmental, technical, economic, and legal factors, and important point for the abruptly expanded industries and increased population. Waste management system is the challenging order and has posed infinite problems in environmental science and human health. The leftover materials that have been released from the industries and daily human life activities should be appropriately collected, transferred, transported, and finally dumped into the properly selected waste disposal site. Waste management greatly helps to keep the environment healthy and significantly reduces risks caused by improperly dumped waste materials. The present study was intended to investigate suitable waste disposal site in Dejen town applying GIS and AHP techniques of multicriteria decision making by considering the ten main influencing factors (distance from the settlement, the road network and the stream, land use, geology, geo�morphology, slope, wind speed and direction, lineament density and elevation). Based on the researchers’ expert and over-viewing of previous studies, the subjectively defined controlling parameters were hierarchically formulated in the matrix and changed into numerical values to obtain the relative impotency of each feature for the preparation of the suitability modeling map. 7.9, 46.4, 6.5, 22.2 and 17% of the study area is highly suitable, suitable, moderately suitable, less suitable and unsuitable for waste disposal site selection respectively. As a result, environmentally sound, feasible and acceptable waste disposal site map was generated using ArcGIS software based on the relative weights of the controlling factors.

Green construction for low-carbon cities: a review

Article

Full-text available

Jan 2023

The construction industry is a major user of non-renewable energy and contributor to emission of greenhouse gases, thus requiring to achieve net-zero carbon emissions by 2050. Indeed, construction activities account for 36% of global energy consumption and 39% of global carbon dioxide emissions. Reducing carbon emissions requires adapted government policies, carbon emission analysis and calculation models, and sustainable materials. Here, we review green construction with focus on history, carbon emissions, policies, models, life cycle assessment, and sustainable materials such as biochar, bioplastic, agricultural waste, animal wool, fly ash and self-healing concrete. Analysis of carbon emissions over the building life cycle shows that the construction phase accounts for 20–50% of total carbon emissions. The average ratio of construction phase annual emissions to operation phase emissions is 0.62. We present national policy frameworks and technology roadmaps from the United States of America, Japan, China, and the European Union, highlighting plans to achieve carbon neutrality in the building sector.

Identification and Sorting of Waste using Artificial Intelligence Based on Convolutional Neural Network

Conference Paper

Nov 2022

Predicting solid waste generation based on the ensemble artificial intelligence models under uncertainty analysis

Article

Feb 2023

Waste is a critical issue the modern world is facing, and its management involves many imperative parameters with distinct, negative impacts on the environment. From a fundamental perspective, the primary parameter in waste management is the very generation of waste. SWG standing for solid waste generation is a phenomenon influenced straightforwardly by many factors. This paper introduces an adaptive hybrid model based on the ensemble empirical mode decomposition (EEMD) and AI models to improve the prediction accuracy of monthly municipal SWG. First, to determine the optimum antecedent values of SWG, a partial autocorrelation function is used, and then all input/output variables are decomposed by EEMD to overcome the non-stationary and non-linearity of the time series in the SWG dataset. Data collected from Tehran between 1991 and 2013 helped evaluate the effectiveness of the suggested model. The proposed model was compared with several accredited single models to showcase its performance. Statistical metrics showed that suggested hybrid EEMD–MARS (R = 0.82, NSE = 0.67, RMSE = 9.656, MAE = 7.538) outperformed EEMD–LSSVM and standard LSSVM and MARS models for prediction of SWG. Additionally, the researchers confirmed the reliability of the results based on the uncertainty analysis using a Monte Carlo algorithm, concluding that the EEMD–MARS model had a higher certainty and displayed significant potential in simulating solid waste generation.

Artificial intelligence for waste management in smart cities: a review

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