Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling

Abstract

Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3 to 5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations.

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

“…The monitoring as well as characterization of water quality was achieved but interoperability issues were reported in this work due to use of heterogenous sensors. Air quality evaluation using fixed as well as mobile nodes of sensors [75] was implemented, capable to check the air quality in stationary as well as mobile ways. In this latter case, the compatible sensors were deployed as mobile nodes which can work satisfactorily in a moving environment.…”

Dynamic Estimation of Individual Exposure Levels to Air Pollution Using Trajectories Reconstructed from Mobile Phone Data

“…To tackle this problem, researchers began to use survey data to record human movement behavior. The two most common techniques were manual sampling surveys [14,34] and Global Navigation Satellite System (GNSS)-enabled personal monitors [8,10,15,16]. For example, Yoo et al (2015) used GNSS-equipped monitor data of 43 participants to demonstrate how an individual’s mobility affects personal exposure estimates.…”

“…To address this problem, more detailed spatiotemporal human movement information is required. Although existing literatures have applied survey data [10,13,14,15,16], social media data [9,17,18,19,20] and mobile phone data [21,22,23,24,25] to record human movement behaviors, several limitations remain. As for the survey data-based methods, the cost of personal monitoring and sampling processes limits the number of samples, thereby reducing the persuasiveness of the estimation results [12,26].…”

An Actuarial Pricing Method for Air Quality Index Options

“…Air pollution has attracted increasing attention around the world. Several researchers have studied the comparability of air pollutant indexes and the assessment of air quality [9,10]. Suggestions and countermeasures have been proposed from the perspectives of pollution control and governance [11,12].…”