This volume offers expert contributions proposing new and recently set scientific standards for smart air quality (AQ) networks data processing, along with results obtained during field deployments of pervasive and mobile systems. The book is divided into 5 main sections; 1) future air quality networks, 2) general data processing techniques, 3) field deployments performances, 4) special applications, and 5) cooperative and regulatory efforts. The authors offer different sources of data for the production of trustworthy insights, including spatio-temporal predictive AQ maps meant to boost citizen awareness, and informed participation in remediation and prevention policies. Readers will learn about the best and most up-to-date practices for measuring and assessing air quality, while also learning about current regulatory statuses regarding air quality technology design and implementation. The book will be of interest to air quality regulatory agencies, citizen science groups, city authorities, and researchers and students working with air quality sensors and geostatistics.
Author(s): Saverio De Vito, Kostas Karatzas, Alena Bartonova, Grazia Fattoruso
Series: Environmental Informatics and Modeling
Publisher: Springer
Year: 2023
Language: English
Pages: 182
City: Cham
Preface
Contents
New Challenges in Air Quality Measurements
1 Introduction
2 Current Standard and Equivalence Measuring Methods
2.1 Problems Related to Current Air Quality Monitoring
3 Role of Low-Cost Sensors (LCS) in the Future Air Quality Networks
3.1 Low-Cost Sensor Technology
4 Data Elaboration of Low-Cost Sensors
4.1 Advantages of LCSs
4.2 Caveats of LCSs
5 Satellite Remote Sensors
5.1 Data Treatment
5.2 Advantages of Satellite Data
5.3 Caveats
5.4 Temporal and Spatial Resolution
5.5 Conclusion About Satellite Data
6 New Networks for Air Quality Monitoring
7 Conclusions
References
A Data Processing Architecture for Intelligent Hierarchical Air Quality Monitoring Networks in Urban Innovation and Citizen Science Applications
1 Introduction
2 General Architecture
2.1 AirHeritage IoT Inception and Storage Architecture
3 Data Processing Pipeline
3.1 Sensor Data Capture Stage: The MONICA Device
3.2 Calibration Stage
3.3 Sensor Fusion Stage
3.4 Personalized Feedback Stage
4 Conclusions
References
Using Continuous Integration Processes to Build Environments for Processing Air Quality Data from IoT Devices
1 Introduction
2 Software Engineering Processes in the Development of Complex Systems
3 Building an Environment for the Development of IoT Systems Using Good Software Engineering Practices
4 Verification of the IoT System Manufacturing Environment for the Construction of a Hybrid Information System on Air Quality for Gdańsk
5 Summary
References
AQ Mapping Through Low-Cost Sensor Networks
1 Introduction
2 Pollution Variables and Low-Cost Sensors
2.1 Common Pollutants in Urban Areas
2.2 Sensors
2.3 Low-Cost Commercial Sensors and Stations
3 AQ Data Collection Initiatives
3.1 Governmental and Private Efforts
3.2 Crowdsensing
4 Spatial Interpolation and Data Visualization
4.1 Deterministic Approach
4.2 Statistical Approach
4.3 Qualitative Comparison and Use Cases
5 Trends and Challenges
References
Odour Nuisance Monitoring
1 Odour Nuisance
1.1 Definition
1.2 Odour Measurement Techniques
2 Instrumental Odour Monitoring Systems
2.1 Description
2.2 Sensing Systems
2.3 Data Processing
3 The Use of IOMS for the Environmental Monitoring
3.1 General Aspects
3.2 Calibration
3.3 Validation
4 Examples of Applications
4.1 IOMS Monitoring at the Receptor
4.2 IOMS Monitoring at the Plant Fenceline
4.3 IOMS Monitoring at Emissions
5 Future Outlooks
References
Drone-Based Monitoring of Environmental Gases
1 Introduction
2 Integration of Chemical Sensing into Drones
2.1 Sensor Technologies
3 Air Pollution Tasks Requiring the Integration of Multiple Sensor Measurements
3.1 Algorithms for Chemical Mapping
3.2 Chemical Maps Based on Data Fusion and Model Integration
3.3 Software Tools for Spatial Interpolation and Mapping
4 Summary
References
Environmental Education for High School Students—Investigation of Air Quality with Low-Cost Sensors
1 Air Pollutants and Gas Sensors
1.1 Volatile Organic Compounds
1.2 CO2 and Indoor Air Quality
1.3 Particulate Matter
2 Learning Modules
2.1 Module 1: Function Principle of MOS Gas Sensors
2.2 Module 2: Calibration of MOS Gas Sensors
2.3 Module 3: Environmental Measurements
3 Development of Environmental Studies Using the “Internet of Things”
3.1 Example Environmental Study—Air Composition in Beehives
3.2 Example Environmental Study—Early Warning System for Forest Fires
4 Conclusion and Outlook
References
Analysis and Modelling of an Optical Particulate Matter Sensor Data Towards Its Performance Improvement
1 Introduction
2 Materials and Methods
2.1 The Pollutant of Interest: Particulate Matter
2.2 Low-Cost Sensors and Systems
2.3 The Data
2.4 Data Modeling Methods
2.5 Model Evaluation
3 Results and Discussion
3.1 Statistical Analysis
3.2 Modeling Results
4 Conclusions
5 Suggestions
References
