Novel Air Pollution Measurement System Based on Ethereum Blockchain

Abstract

The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

Impact of Air Pollution on Global Burden of Disease in 2019

“…Particularly, the morbidity and mortality due to AAP have escalated steadily over time, with an estimated 4.2 million annual deaths [3,8,9]. For this reason, it is important to monitor pollution data clearly and actuate optimized solutions in order to establish a new equilibrium among sustainability and human productivity and wellness [10]. Particulate matter (PM), nitrogen oxides (NO and NO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ) are the major established ambient air pollutants responsible for adverse health effects [2].…”

Communication Cost Reduction with Partial Structure in Federated Learning

“…In the most extreme case of data distribution difference (90, 10), FederatedPartial required 0.612 times less cost than the standard method, but it required 0.672 times less cost in the less localized data (10,90). This is because FederatedPartial has a greater effect when the data distribution difference between clients is large, which can be interpreted as the relationship between the data and model size of each client.…”

“…Third, communication with the server is not free. In general, as more devices and larger amounts of information are synchronized more frequently, the training difficulty of the server model decreases, but unrestricted communication with user devices can be dangerous [7,9,10]. For example, if it takes 100 rounds of communication to deploy and train a 100 MB server model, with an assumption that 100 communications are made from 1 million devices, the communication charges incurred from the communication traffic increase extraordinarily.…”

Comparison Process of Blood Heavy Metals Absorption Linked to Measured Air Quality Data in Areas with High and Low Environmental Impact

“…Recently, sensors for low-cost air quality monitoring based on IoT (Internet of Things) technology have been developed. This has allowed the implementation of air quality monitoring networks with high spatial and temporal resolution [23] that enable the detection of particulate matter of different sizes (PM10, PM2.5) and the measurement of the concentration of gases in the atmosphere (SO 2 , NO 2 , CO, O 3 , VOC), reliably protecting data integrity [24]. The new type of intelligent measuring device could be easily installed in many parts of the city following an optimization in the choice of places [25].…”