Abstract
Deployment of an air quality low-cost sensor network (AQLCSN), with proper calibration of low-cost sensors (LCS), offers the potential to substantially increase the ability to monitor air pollution. However, to leverage this potential, several drawbacks must be ameliorated, thus the calibration of such sensors is becoming an essential component in their use. Commonly, calibration takes place in a laboratory environment using gasses of known composition to measure the response and a linear calibration is often reached. On site calibration is a promising complementary technique where an LCS and a reference instrument are collocated with the former being calibrated to match the measurements of the latter. In a scenario where an AQLCSN is already operational, both calibration approaches are resource and time demanding procedures to be implemented as frequently repeated actions. Furthermore, sensors are sensitive to the local meteorology and adaptation is a slow process making relocation a complex and expensive option. We concentrate our efforts in keeping the LCS positions fixed and propose to blend a genetic algorithm (GA) with a hybrid stacking (HS) ensemble into the GAHS framework. GAHS employs a combination of batch machine learning algorithms and regularly updated online machine learning calibration function(s) for the whole network when a small number of reference instruments are present. Furthermore, we introduce the concept of spatial online learning to achieve better spatial generalization. The frameworks are tested for the case of Thessaloniki where a total of 33 devices are installed. The AQLCSN is calibrated on the basis of on-site matching with high quality observations from three reference station measurements. The O3 LCS are successfully calibrated for 8–10 months and the PM10 LCS calibration is evaluated for 13–24 months showing a strong seasonal dependence on their ability to correctly capture the pollution levels.
Highlights
Modeling of the atmospheric chemical composition has advanced steadily over the past number of years [1,2] with the aim of producing actionable Air Quality (AQ) information
The purpose of this study is to: (a) introduce the spatial online learning (SOL) method for timely and long lasting calibration of environmental sensors (Section 2.3); (b) provide a general framework for calibrating any fixed Air Quality Low-Cost Sensor Networks (AQLCSN) given that a small number of the networks low-cost sensors (LCS) are collocated with reference instruments (Sections 2.4–2.6); (c) apply and evaluate the framework for particulate matter and ozone for the ideal scenario where there are no delays in reference measurements arrival, the realistic scenario referring to 5 h delays and the safe scenario referring to 12 h delays and assess the ability to keep the network active and accurate for longer periods (Section 2.7)
In concept #5, that lasts for the whole winter period of 2020–2021, the distribution returns to its original form with similar statistics
Summary
Modeling of the atmospheric chemical composition has advanced steadily over the past number of years [1,2] with the aim of producing actionable Air Quality (AQ) information. Data about AQ are routinely collected from official (ground-based) monitoring stations These data can be supplemented by Air Quality Low-Cost Sensor Networks (AQLCSN) operated by research institutes and companies and more recently by citizen science projects [3]. These can be complemented by remote sensing data from satellites such as Sentinel 2 products, land-use maps, shipping, traffic related and domestic emission profiles and other related databases. The accurate monitoring of the urban environment is becoming prominent for improved quantitative as well as qualitative information
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