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Air quality directly affects the health of humans. The health implications of poor air quality are recognized by professionals and the public alike and these concerns have driven both the proliferation of formal sensor networks, but also low-cost sensors which can be used in the home. The advancement of technology in recent years has also led to the rapid development of low-cost sensors. Given that citizens are concerned about the air quality of the environment in which they live, they are turning to the supply of low-cost sensors, as they are affordable. The question of the reliability of measurements from low-cost sensors remains an area of research. In this research work, the optimization of ozone (O3) and nitrogen dioxide (NO2) measurements of low-cost electrochemical air quality sensors is investigated by applying nonlinear regression, using a second-order polynomial equation as a correction factor. The proposed correction method is implementable in IoT devices, as it does not require high computational resources. The results show that the measurements are susceptible to correction, with the effect that the corrected values are close to the actual values obtained by the reference instruments of the Department of Environmental Pollution Control Project of Athens (PERPA), a service of the Greek Ministry of the Environment and Energy. [ABSTRACT FROM AUTHOR]

Air pollution, which is mainly caused by industrialization, intensive transportation, and the heating of buildings, is one of the most important problems in large cities because it seriously harms the health and the quality of life of their citizens. This is why air quality is monitored not only by governmental organizations and official research institutions through the use of sophisticated monitoring systems but also by citizens through the use of low-cost air quality measurement devices. However, the reliability of the measurements derived from low-cost sensors is questionable, so the measurement errors must be eliminated. This study experimentally investigated the impact of the use of a Kalman filter on the accuracy of the measurements of low-cost air quality sensors. Specifically, measurements of air pollutant gases were carried out in the field in real ambient air conditions. This study demonstrates not only the optimization of the measurements through the application of a Kalman filter but also the behavior of the filter coefficients and their impact on the predicted values. [ABSTRACT FROM AUTHOR]

Air quality directly affects the health of humans. The health implications of poor air quality are recognized by professionals and the public alike and these concerns have driven both the proliferation of formal sensor networks, but also low-cost sensors which can be used in the home. The advancement of technology in recent years has also led to the rapid development of low-cost sensors. Given that citizens are concerned about the air quality of the environment in which they live, they are turning to the supply of low-cost sensors, as they are affordable. The question of the reliability of measurements from low-cost sensors remains an area of research. In this research work, the optimization of ozone (O3) and nitrogen dioxide (NO2) measurements of low-cost electrochemical air quality sensors is investigated by applying nonlinear regression, using a second-order polynomial equation as a correction factor. The proposed correction method is implementable in IoT devices, as it does not require high computational resources. The results show that the measurements are susceptible to correction, with the effect that the corrected values are close to the actual values obtained by the reference instruments of the Department of Environmental Pollution Control Project of Athens (PERPA), a service of the Greek Ministry of the Environment and Energy.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO 2 on every street in Amsterdam ( n = 46.664) and Copenhagen ( n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers ( n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated r s (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated r s = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher r s = 0.65 with the deterministic model predictions compared to the data-only ( r s = 0.50) and LUR model ( r s = 0.61). In Copenhagen, mixed model estimates correlated r s = 0.51 with external model predictions compared to r s = 0.45 and r s = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations ( r s = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

Abstract: Slant column densities (SCD) of traffic-generated NO2 were derived during the “BAB II” motorway emission campaign in April/May 2001 using scattered sunlight and a CCD-based Multi-Axis- (MAX) DOAS setup with eight viewing directions being probed simultaneously. The setup allowed for the tracking of the diurnal variation of a motorway plume with 1min temporal resolution, an assessment of the vertical structure as well as the comparison with the plume outreach of Heidelberg. [Copyright &y& Elsevier]