Development of a baseline-temperature correction methodology for electrochemical sensors and its implications for long-term stability

Recent studies have shown that (three-electrode) electrochemical sensors can be utilised for air quality monitoring and exposure assessment. The long-term performance of these sensors is however, often limited by the effects of ambient meteorological parameters on the sensor baseline, in particular temperature. If electrochemical (EC) sensors are to be adopted for air quality measurement over extended periods (months), this effect must be accounted for. Recent long-term, ambient measurements of CO, NO and NO$_2$ using EC sensors have revealed that temperature (and relative humidity (RH)) had an effect on the baseline which was more pronounced in the case of NO sensors with coefficient of determination, $R^2$ of 0.9 when compared to CO and NO$_2$ with $R^2$ < 0.2. In this paper we present a correction methodology that quantifies this effect (referred to here as fitted baseline), implementing these correction on the EC measurements. We found that EC sensors corrected for baseline-temperature effect using the method describe in this paper show good agreement when compared with traditional reference instrument. The coefficient of determination $R^2$ of 0.7-0.8 and gradient of 0.9 was observed for baseline-temperature corrected NO compared to $R^2$ = 0.02 prior to baseline-temperature correction. Furthermore, the correction methodology was validated by comparing the temperature-baseline with proxy temperature compensating measurements obtained from the fourth electrode of a set of novel four-electrode electrochemical sensors. A good agreement (R$^2$ = 0.9, with gradients = 0.7-1.08 for NO and 0.5 < R$^2$ < 0.73 for CO) was observed between temperature fitted baselines and outputs from the fourth electrodes (also known non-sensing/auxiliary electrode). Meanwhile, the long-term stability (calibrated signal output) of temperature-corrected data was evaluated by comparing the change in sensor gain to meteorological parameters including temperature, relative humidity, wind speed and wind direction. The results showed that there was no statistically significant change in sensitivity (two-sided $t$-test, p = 0.34) of the temperature-corrected electrochemical sensor with respect to these parameters (over several months). This work demonstrates that using the baseline-temperature correction methodology described in this paper, electrochemical sensors can be used for long-term (months), quantitative measurements of air quality gases at the parts per billion volume (ppb) mixing ratio level typical of ambient conditions in the urban environment.The authors would like to thank Cambridge Commonwealth Trust & Cambridge Overseas Trust and Dorothy Hodgkin Studentship for the PhD studentship awarded to Olalekan Popoola. We will like to thank NERC for funding the SNAQ Heathrow project as well as DfT and EPSRC for funding the MESSAGE project

Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors

To carry out detailed source attribution for air quality assessment it is necessary to distinguish pollutant contributions that arise from local emissions from those attributable to non-local or regional emission sources. Frequently this requires the use of complex models and inversion methods, prior knowledge or assumptions regarding the pollution environment. In this paper we demonstrate how high spatial density and fast response measurements from low-cost sensor networks may facilitate this separation. A purely measurement-based approach to extract underlying pollution levels (baselines) from the measurements is presented exploiting the different relative frequencies of local and background pollution variations. This paper shows that if high spatial and temporal coverage of air quality measurements are available, the different contributions to the total pollution levels, namely the regional signal as well as near and far field local sources, can be quantified. The advantage of using high spatial resolution observations, as can be provided by low-cost sensor networks, lies in the fact that no prior assumptions about pollution levels at individual deployment sites are required. The methodology we present here, utilising measurements of carbon monoxide (CO), has wide applicability, including additional gas phase species and measurements obtained using reference networks. While similar studies have been performed, this is the first study using networks at this density, or using low cost sensor networks.The authors thank EPSRC (EP/E001912/1) for funding for the Message project. IH thanks the German National Academic Foundation for funding of MPhil degree.This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S1352231015300583#

The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks

Measurements at appropriate spatial and temporal scales are essential for understanding and monitoring spatially heterogeneous environments with complex and highly variable emission sources, such as in urban areas. However, the costs and complexity of conventional air quality measurement methods means that measurement networks are generally extremely sparse. In this paper we show that miniature, low-cost electrochemical gas sensors, traditionally used for sensing at parts-per-million (ppm) mixing ratios can, when suitably configured and operated, be used for parts-per-billion (ppb) level studies for gases relevant to urban air quality. Sensor nodes, in this case consisting of multiple individual electrochemical sensors, can be low-cost and highly portable, thus allowing the deployment of scalable high-density air quality sensor networks at fine spatial and temporal scales, and in both static and mobile configurations.This work was supported by EPSRC (grant number EP/E002102/1) and the Department for Transport

Research data supporting [The use of networks of low cost air quality sensors to quantify air quality in urban settings]

These data support the work on development and deployment of a network of low-cost sensors for improving our understanding of air quality. The project Sensor Network for Air Quality (SNAQ) was funded by NERC. The research was done at London Heathrow airport between 2012 and 2013. Data provided include raw sensor data and processed files used for the article. In addition, ADMS model data are provided for the comparison presented in the manuscript. The data are in csv, pdf and txt formats, accompanied with a ReadMe describing the content of the folders

Research data supporting [Use of networks of low cost air quality sensors to quantify air quality in urban settings]

These data support the work on development and deployment of a network of low-cost sensors for improving our understanding of air quality. The project Sensor Network for Air Quality (SNAQ) was funded by NERC. The research was done at London Heathrow airport between 2012 and 2013. Data provided include raw sensor data and processed files used for the article. In addition, ADMS model data are provided for the comparison presented in the manuscript. The data are in csv, pdf and txt formats, accompanied with a ReadMe describing the content of the folders

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Use of networks of low cost air quality sensors to quantify air quality in urban settings

Low cost sensors are becoming increasingly available for studying urban air quality. Here we show how such sensors, deployed as a network, provide unprecedented insights into the patterns of pollutant emissions, in this case at London Heathrow Airport (LHR). Measurements from the sensor network were used to unequivocally distinguish airport emissions from long range transport, and then to infer emission indices from the various airport activities. These were used to constrain an air quality model (ADMS-Airport), creating a powerful predictive tool for modelling pollutant concentrations. For nitrogen dioxide (NO2), the results show that the non-airport component is the dominant fraction (~75%) of annual NO2 around the airport and that despite a predicted increase in airport related NO2 with an additional runway, improvements in road traffic fleet emissions are likely to more than offset this increase. This work focusses on London Heathrow Airport, but the sensor network approach we demonstrate has general applicability for a wide range of environmental monitoring studies and air pollution interventions

Urban emission inventory optimisation using sensor data, an urban air quality model and inversion techniques

An optimisation scheme has been developed that applies a Bayesian inversion technique to a high resolution (street-level) atmospheric dispersion model to modify pollution emission rates based on sensor data. The scheme minimises a cost function using a non-negative least squares solver. For the required covariance matrices, assumptions are made regarding the magnitude of the uncertainties in source emissions and measurements and the correlation in uncertainties between different source emissions and different measurement sites. The scheme has been tested in an initial case study in Cambridge using monitored data from four reference monitors and 20 AQMesh sensor pods for the period 30 June 2016 to 30 September 2016. Hourly NOx concentrations from road sources modelled using ADMS-Urban and observed concentrations were processed using the optimisation scheme and the adjusted emissions were re-modelled. The optimisation scheme reduced average road emissions on average by 6.5% compared to the original estimates, changed the diurnal profile of emissions and improved model accuracy at four reference sites

Improving NO<inf>x</inf>emission estimates in Beijing using network observations and a perturbed emissions ensemble

Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOX emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOX