Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value

Gressent, Alicia; Malherbe, Laure; Colette, Augustin; Rollin, Hugo; Scimia, Romain

doi:10.1016/j.envint.2020.105965

Cited by 43 publications

(21 citation statements)

References 27 publications

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“…The correlation coefficient for the cross-validation test was 0.91 in Huang et al [28], which was less than this study (r-value 0.96). Gressent et al [29] analysed air quality data from low-cost sensors and integrated them with the estimation of ADMS-Urban in France. In contrast to this study, which uses annual NO 2 concentrations, Gressent et al [29] used hourly data and estimated PM 10 concentrations for 29 November 2018 from 7 a.m. to 7 p.m.…”

Section: Fusion Of No2-dtlcs With No2-airviro and No2-lurmentioning

confidence: 99%

“…Gressent et al [29] analysed air quality data from low-cost sensors and integrated them with the estimation of ADMS-Urban in France. In contrast to this study, which uses annual NO 2 concentrations, Gressent et al [29] used hourly data and estimated PM 10 concentrations for 29 November 2018 from 7 a.m. to 7 p.m. They used external drift kriging, which is similar to universal kriging, and reduced the bias from 8% to 2% when considering LCS observations instead of the model alone.…”

Section: Fusion Of No2-dtlcs With No2-airviro and No2-lurmentioning

confidence: 99%

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Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

2021

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Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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Section: Fusion Of No2-dtlcs With No2-airviro and No2-lurmentioning

confidence: 99%

Section: Fusion Of No2-dtlcs With No2-airviro and No2-lurmentioning

confidence: 99%

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

2021

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“…Gressent et al [ 25 ] conducted a large-scale project to view the distribution of air pollution in the entire city. Since the reference station network was sparse and expensive, many sensors (fixed low-cost sensors) were mounted on buildings and others (mobile low-cost sensor) mounted on moving vehicles to measure the concentration of particulate matter in the city center.…”

Section: Related Workmentioning

confidence: 99%

Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors

Cho

Baek

2021

Sensors

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Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

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“…Li et al [ 39 ] and Guan et al [ 48 ], on top of using several covariates in their geostatistical model, used a likelihood-based method making stricter assumptions about the underlying distribution of the data and increasing the computational resources, making it challenging to use in real-time applications. Gressent et al [ 43 ] used, as opposed to the likelihood method, a variogram-based method. They chose a purely spatial model that did not take into account the temporal correlation of the data.…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

Idir

Orfila

Judalet

et al. 2021

Sensors

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With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

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Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value

Cited by 43 publications

References 27 publications

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors

Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

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