Air Quality Monitoring and Management Using Sensors.

Format:

Book

Author/Creator:

Awasthi, Amit.

Series:

Developments in Weather and Climate Science Series

Developments in Weather and Climate Science Series ; v.Volume 9

Language:

English

Subjects (All):

Air quality.

Air quality monitoring stations.

Physical Description:

1 online resource (0 pages)

Edition:

1st ed.

Place of Publication:

Chantilly : Elsevier, 2025.

Summary:

Air Quality Monitoring and Management Using Sensors, Volume Nine offers a comprehensive overview of the latest advancements in air pollution monitoring technologies.With contributions from leading experts, the book discusses sensor innovations, data analysis, and practical applications.

Contents:

Front Cover

Air Quality Monitoring and Management Using Sensors

Copyright Page

Contents

List of contributors

About the editors

1 Introduction to air pollution monitoring and remediation: understanding the scope, impact, challenges, and needs

1.1 Introduction

1.1.1 Scope of air pollution monitoring

1.1.2 Types of IoT monitoring system components

1.1.2.1 Sensors

1.1.2.2 Enclosure

1.1.2.3 Power supply

1.1.2.4 Cloud server

1.1.2.5 Communication module

1.1.2.6 Miniature controllers

1.1.3 IoT-based air quality monitoring systems

1.1.3.1 Using the monitoring system

1.1.4 How does air pollution get less with IoT?

1.1.5 How to implement a system for monitoring air pollution

1.1.6 Popular air quality monitoring system based on IoT

1.1.6.1 Libelium smart environment PRO

1.1.6.2 Clarity node

1.2 Impact of air pollution

1.3 Air pollution's impact on particle contaminants

1.3.1 Carbon monoxide

1.3.2 Sulfur dioxide

1.3.3 Nitrogen oxide

1.3.4 Lead

1.3.5 Other air pollutants

1.4 Air pollution's consequences on health

1.4.1 Health effects

1.4.2 Impact on respiratory

1.4.3 Cardiovascular impact

1.4.4 Impact on neuropsychiatric

1.5 Challenges in air monitoring

1.5.1 Air pollution due to climate change

1.5.2 Issues with air pollution in hazardous waste

1.5.3 Air pollution challenges in ozone layer

1.5.4 Air pollution challenges management

1.6 Needs for effective remediation

1.6.1 Technologies for treating and eliminating environmental pollutants

1.6.2 Remediation technologies for efficient removal of environmental pollutants

1.6.3 Sensors

1.6.4 Biosensors

1.6.5 Sensors electrochemical

1.6.6 Sensors of mass

1.6.7 Optical measurement

1.6.8 Gas detectors

1.7 Conclusions

References.

2 Sensor-enabled insights into aerosol optical depth and climate variability: a scientometric analysis

2.1 Introduction

2.2 Methodology

2.2.1 Scientometric analysis

2.3 Findings and analysis

2.3.1 Geographical allocation of publication

2.3.2 Author's productivity and influence

2.3.2.1 Most productive and influential author

2.3.2.2 Most influential author

2.3.3 Core journals

2.3.4 Articles

2.3.5 Bibliographic coupling

2.3.6 Keyword analysis

2.4 Conclusion

References

3 Advancing air pollution sensor technologies: an overview of sensor systems, evolution, types, deployment, and implementation

3.1 Introduction

3.2 Evolution of sensors

3.3 Type of air quality sensors

3.3.1 Gas sensors

3.3.1.1 Electrochemical sensors

3.3.1.2 Catalytic sensor

3.3.1.3 Nondispersive infrared sensors

3.3.1.4 Solid-state sensors

3.3.1.5 Photo-ionization sensors

3.3.2 Particulate matter sensor

3.3.2.1 Optical sensors

3.3.2.2 Black-smoke method

3.3.2.3 Tapered element oscillating micro-balance sensors

3.4 Deployment of advanced sensor networks for air pollution

3.4.1 Traditional measuring stations

3.4.2 Modern air quality monitoring system using wireless sensor networks

3.4.3 Internet of Things based solutions

3.4.3.1 Key aspects of Internet of Things-based air pollution monitoring

3.4.3.1.1 Real-time data collection

3.4.3.1.2 Wide area coverage

3.4.3.1.3 Data integration and analytics

3.4.3.1.4 Alerts and notifications

3.4.3.1.5 Public awareness and engagement

3.4.3.1.6 Integration with other systems

3.5 Deployment and implementation

3.5.1 Sensor placement

3.5.2 Maintenance of sensor reliability and accuracy entails

3.5.3 Integration with Internet of Things and cloud computing.

3.6 Examples of Internet of Things-based air pollution monitoring solutions

3.6.1 Smart air quality monitoring networks

3.6.2 Wearable air quality monitors

3.6.3 Environmental monitoring platforms

3.6.4 Smart home air quality solutions

3.6.5 Drones and mobile sensors

3.7 Challenges and considerations

3.7.1 Data accuracy and calibration

3.7.2 Environmental influences

3.7.3 Data privacy and security

3.7.4 Infrastructure and connectivity

3.7.5 Cost and scalability

3.7.6 Maintenance and lifespan

3.7.7 Interoperability and standardization

3.7.8 Spatial and temporal resolution

3.7.9 Regulatory and compliance issues

3.7.10 User skill requirements

3.8 Conclusion

4 Present state of gas sensor technologies for monitoring air pollution

4.1 Introduction

4.2 A brief history of air pollution sensing technology

4.3 Present state of air pollution sensing, their challenges, and innovative solutions

4.4 Internet of Things

4.5 Artificial intelligence

4.6 In-depth review of relevant case studies

4.7 Closing remarks

5 Portable and wearable air pollution sensors for personal exposure monitoring

5.1 Introduction

5.1.1 Overview of air quality monitoring

5.1.2 Evolution of portable and wearable sensors

5.1.3 Application of sensors in personal exposure monitoring

5.2 Methodology

5.2.1 Establishment of inclusion and exclusion criteria

5.2.2 Procedures for study selection

5.3 Results

5.3.1 Challenges in personal exposure monitoring

5.3.2 Protocol for wearable and portable sensor-based personal exposure monitoring

5.4 Discussion

5.5 Conclusions

6 Sensor-based Approaches for Indoor Air Quality Monitoring and Control: PM2.5 and PM10

6.1 Introduction

6.2 Technological aspects of PM sensors

6.2.1 Electrochemical.

6.2.2 Optical sensor

6.2.3 Piezoresistive

6.2.4 Compare and contrast sensor performance parameters like sensitivity, selectivity, response time, and accuracy

6.3 Calibration procedures

6.3.1 Importance of sensor calibration for accurate measurements

6.3.1.1 Relative humidity adjustment

6.3.1.2 Temperature level adjustment

6.3.1.3 Calibration with reference monitors

6.3.2 Different calibration methods for PM sensors (e.g., gravimetric, reference instruments)

6.3.2.1 SidePak personal aerosol monitor AM510

6.3.2.2 Scanning mobility particle sizer

6.3.2.3 Air assure PM2.5 IAQ monitor

6.3.3 Calibration frequency and factor influencing calibration requirements for PM sensors

6.3.4 Calibration methodology

6.4 Real-world implementation: different approaches to air monitoring systems of indoor industrial, residential, and educational institutions

6.4.1 Deployment strategies for PM sensors in an indoor environment

6.4.2 Data acquisition and transmission methods

6.4.3 Challenges and considerations for long-term operation and maintenance

6.5 Practical applications in air quality monitoring

6.6 Data analysis and visualizations

6.6.1 Descriptive statistics

6.6.2 Time series analysis

6.6.3 Statistical methods

6.6.4 Spatial interpolation

6.6.5 Machine learning algorithms

6.6.6 The implication of sensor-based air quality monitoring

6.6.7 Challenges and opportunities of sensor-based air quality monitoring

6.7 Conclusion and future avenues of study

7 Optimizing indoor environments: harnessing sensor-based solutions for air quality management

Abbreviations

7.1 Introduction

7.2 Indoor air pollutants: sources and health impacts

7.3 Fundamentals of sensor technologies for IAQ monitoring

7.4 Sensor selection and deployment strategies for IAQ.

7.5 Real-time IAQ monitoring systems

7.6 Applications of sensor-based IAQ control

7.7 Challenges and future prospects to the implementation of sensor-based IAQ monitoring systems

7.8 Conclusions

Acknowledgment

Conflict of interest

Declaration of generative AI in scientific writing

8 Air quality prediction using sensor data and machine learning techniques

8.1 Introduction

8.2 Literature review

8.2.1 Sensor technologies for air quality monitoring

8.2.2 Role of machine learning in air pollution prediction and mitigation in urban areas

8.2.3 Integration of Internet of things and environmental monitoring

8.3 Proposed methodology

8.3.1 Data acquisition

8.3.2 Data exploration

8.3.3 Data preprocessing

8.3.4 Data reduction-Principal Component Analysis (PCA)

8.3.5 XGBoost-based air quality prediction

8.3.6 Random forest model

8.4 Results and discussion

8.5 Conclusion

9 Air quality monitoring using long range wide area network

9.1 Introduction

9.2 Air and its quality in urban cities

9.3 Air quality sensors

9.4 Air quality monitoring system: A brief overview

9.5 Internet of things and long-range wide area network

9.5.1 LoRa and LongRange wide area network

9.6 Literature review

9.6.1 Types of monitoring systems

9.6.2 Previous studies of Internet of things and its implementations in smart cities

9.6.3 Types of wireless technologies used to monitor environmental conditions

9.6.4 The role of air quality sensors in smart cities

9.6.5 Long range wide area network and its usages in smart cities

9.7 Methodology

9.7.1 Data collection through air quality sensors

9.7.2 Data transmission through LoRaWAN

9.7.3 Data analysis through the things network

9.7.4 Data visualization and notification.

9.7.4.1 Proposed way of air quality monitoring in smart cities using LoRaWAN.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

0-443-33974-0

9780443339745

OCLC:

1558597995