Amperometric Gas Sensors as a Low Cost Emerging Technology

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Amperometric Gas Sensors as a Low Cost Emerging Technology Platform for Air Quality Monitoring Applications: A Review. Ronan Baron, and John Saffell ACS Sens., Just Accepted Manuscript • DOI: 10.1021/acssensors.7b00620 • Publication Date (Web): 13 Oct 2017 Downloaded from http://pubs.acs.org on October 14, 2017

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Amperometric Gas Sensors as a Low Cost Emerging Technology Platform for Air Quality Monitoring Applications: A Review. Ronan Baron,* John Saffell* Alphasense Ltd, Sensor Technology House, 300 Avenue West, Great Notley, Essex, CM77 7AA, UK.

ABSTRACT: This review examines the use of amperometric electrochemical gas sensors for monitoring inorganic gases that affect urban air quality. First, we consider amperometric gas sensor technology including its development towards specifically designed air quality sensors. We then review recent academic and research organizations’ studies where this technology has been trialed for air quality monitoring applications: early studies showed the potential of electrochemical gas sensors when co-located with reference Air Quality Monitoring (AQM) stations. Spatially dense networks with fast temporal resolution provide information not available from sparse AQMs with longer recording intervals. We review how this technology is being offered as commercial urban air quality networks and consider the remaining challenges. Sensors must be sensitive, selective and stable; air quality monitors/ nodes must be electronically and mechanically well designed. Data correction is required and models with differing levels of sophistication are being designed. Data analysis and validation is possibly the biggest remaining hurdle needed to deliver reliable concentration readings. Finally, this review also considers the roles of companies, urban infrastructure requirements and public research in the development of this technology. KEYWORDS: Air quality monitoring, environmental sensors, electrochemical gas sensors, nitrogen oxides, ozone, sulfur oxides, carbon oxides, pollution, personal exposure, air quality networks. Air pollution is undeniably one of the major issues in our contemporary world. Air pollution was labelled as ‘the largest environmental hazard’ by the EU Environmental Protection Agency which quotes 467, 000 death related to air pollution in the EU in 2013. It is estimated that, in the UK alone, 40, 1 000 people die prematurely, as a result of poor air quality. Amongst the main pollutants are toxic gases such as nitrogen oxides, sulfur oxides, and aerosols/ particulates. A variety of 2,3 health effects have been reported. This prompted 4,5 governmental bodies to set legal pollution limits. We must be able to measure these pollutants and therefore air quality monitoring has generated significant interest in the last few years. Reliable and certified air quality monitoring instrumentation is available but the initial and maintenance costs limit their use to only a few specific locations. A low cost alternative for air quality monitoring would be highly desirable.

A number of low cost sensor technologies have been trialed for air quality monitoring. Amongst them, amperometric gas sensors are well established in the industry 6-9 for industrial safety since the 1970s. They are used in both portable and fixed site gas detectors and have proven to be affordable and robust. Almost all confined space workers globally rely on amperometric gas sensors for their personal safety. Amperometric gas sensors are also ubiquitous in domestic carbon monoxide detection monitors. Besides being cheap and reliable they generally have a linear output, require negligible power and can be designed to be selective to specific gases. Gases are typically detected in the range from 1 to 1000 ppm. However, in air quality monitoring applications the concentrations are two or three orders of magnitude less than for safety applications and, a priori, measuring such low concentrations with amperometric gas sensors represents a substantial challenge. Early studies showed that Indicative levels of pollutants can be detected using affordable, high density air quality

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networks. Since then, amperometric gas sensors have been identified as one of the most promising sensor technology platform for measuring inorganic gases in air quality monitoring. Both academic and industrial activity has increased dramatically in the last few years; commercial air quality monitoring networks using this technology to measure NO2, O3, CO, SO2, NO and H2S are now available. Air quality monitors comprising amperometric sensor technology are low weight and consume minimum power, making them suitable for deployment in a wide range of urban locations where space is limited and mains power is not easily accessible. They can be mounted on lamp posts at busy junctions, on walls at schools and office buildings, across open spaces and near transport hubs. The availability of low cost networks shifted the landscape to fine-grained air quality data, not available previously. A number of reviews dealing with low cost air quality monitoring instruments can be found, it includes the following references:

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sensors. First we consider the general principles of amperometic gas sensors and how they were adapted for low concentration measurements. We then review published studies corresponding to laboratory and field deployments. Challenges and capabilities, are addressed in regards of studies to date. Third, we discuss the role of governmental institutions and private companies.

UNDERSTANDING AMPEROMETRIC GAS SENSORS Standard amperometric sensors are 2-electrode or 3electrode cells based on fuel cell technology. Amperometric gas sensors are well established in the industrial gas safety industry. Amperometric gas sensors have the additional advantage of having a signal output current which is linearly proportional to the concentration of the detected gas.

A review by Snyder et al. from the US EPA (2013) addressed sensor technologies, citizen actions, compliance monitoring, personal exposure monitoring, and the challenges and roles 13 for government. A review by Kumar et al. (2015) from the University of Surrey reviewed the use of low cost sensing for managing air pollution in cities including the need for ubiquitous 14 sensing. A subsequent publication published in July 2017 gives guidance to the end-users to ensure reasonable data 15 quality. A review on modelling of smart sensors was published in 2015 by Reis et al. from the Natural Environment Research 16 Council. Focusing on smart and amperometric sensors, they looked at the handling of ‘big data’, measurement uncertainty and linking sensor data with toxicology and communicating with the citizen. General questions relative to networks can also be found a review by Yi et al. from the Chinese University of Hong 17 Kong published in 2015. Amperometric gas sensors are not the only low cost sensors available: other low cost air quality sensor technologies include non-disperse Infrared cells for CO2 and CH4 monitoring, photoionization detectors (PID) for VOC detection, metal oxide resistive sensors for broadband inorganic and VOC measurements and optical particle counters (OPCs) for counting particulates from 0.3 to 40 13-15,17 In particular, metal oxide resistive sensors can µm sometimes be used as an alternative sensor technology for measuring the gases accessible to amperometric gas sensors. Advantages and disadvantages of the different technologies is the object of scientific scrutiny and is discussed 13-15,17,18 elsewhere. The objective of this paper is to review the literature dealing with air quality monitoring using amperometric gas

Figure 1: Design for standard amperometric gas sensors. The most common target gases are O2, CO, NO2, NO, O3, H2S, SO2 NH3, HCN, HCl, HBr, CS2, Cl2, H2, HCl, HBr and HF. Some VOCs can also be detected. Amperometric gas sensors can also be used to detect vapors such as H2O2. An amperometric gas sensor requires three components (Figure 1). The gas chamber controls gas access and sensor sensitivity. The gas chamber can include a chemical filter to provide a better gas selectivity. The middle section is the electrochemical cell itself with the electrodes impregnated with an electrolyte solution. In some cases, the liquid electrolyte is part of a polymer matrix. The electrodes are generally made of precious metals or carbon in the form of nanoparticles or a very thin PVD or CVD deposited layer. The working electrode is generally supported on a gas porous membrane. The electrolyte solutions are typically mineral acids or organic solvents with an added salt. The lower section includes a reservoir which accommodates for changes in equilibrium electrolyte concentration as the relative humidity changes for mineral acids or for solvent evaporation when an organic electrolyte is used. The sensor’s pins provide direct electrical contact from the electrodes to the circuit board. Initially gases diffuse through the dust filter to the gas chamber, where chemical filters may improve gas selectivity. The target gas then diffuses through the gas porous membrane which supports the working electrode. The gas


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then contacts the three-phase interface where it meets the liquid electrolyte and the catalyst. The working electrode reaction takes place at this three phase interface (Figure 2).

Basically it is generally assumed that it is the analyte diffusion through the porous membrane supporting the working electrode which is the limiting step (Figure 3). Overall this configuration allows the sensor to respond rapidly to changes in the gas phase and typically the sensor response time (t90: the time necessary for the sensor output to reach 90% of the maximal sensor output) are in the order of a few seconds (Figure 4).

Figure 2: Representation of CO oxidation at the three-phase interface.

Figure 4: CO amperometric gas sensor response to air and 400 ppm of CO (Alphasense CO-AF sensor with a sensitivity of 70 nA/ppm and a t90 of 14 s).

Figure 3: Target gas amperometric gas sensor.

concentration

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an

The gas dissolves in a thin layer of electrolyte at a close proximity to the electrode material. A fixed potential is maintained between the working electrode and the reference electrode- this ensures complete reaction of the target gas and is another method to improve gas selectivity. Another electrochemical reaction takes place at the counter electrode to balance the production or consumption of electrons at the working electrode. The electrochemical reaction produces a flow of electrons in the form of a limited current between the working electrode pin and the counter electrode pin. This current is the measured sensor output. When the kinetics of reaction is sufficiently fast the electrode reaction mechanism is controlled by the mass transfer of the gas analyte to the working electrode. Diffusion to the sensor can be decomposed in the different elements that makes the gas pathway (Figure 3). The measured current is proportional to 6-9 the molar fraction of the gas analyte.

With Il the limited current, K the proportionality coefficient and c the molar fraction of the gas.

If we take the example of CO amperometric gas sensors, the CO oxidation takes place at the working electrode (eq. 1), while the reduction of oxygen happens at the counter electrode (eq. 2). The presence of oxygen is necessary to counterbalance the electrons produced at the working electrode and to form CO2 as a reaction product. Overall this reaction does not produce interfering species.

High sensor quality is ensured by testing every sensor. Typical parameters such as sensor zero current, sensitivity and response time are recorded for sensor traceability. The sensors are delivered to an instrument manufacturer where they are integrated into an instrument; the detector/instrument manufacturer will make a final instrument calibration before shipping to the end user.

AIR QUALITY AMPEROMETRIC GAS SENSORS Providing high quality environmental sensors is a challenging task. Standard amperometric sensors are typically used to detect gases at concentrations in the 110,000 ppm range, while air quality applications are in the low ppb range. It is well known, however, that the influence of environmental conditions such as the temperature and humidity become non-negligible when measuring gases in


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the ppb range. Also the requirements for gas cross-sensitivity in safety, combustion or process control applications are in general very different than for air quality monitoring. In the last few years, a range of air quality amperometric sensors have been developed to respond to the need for low cost air quality gas sensors. Several modifications have been brought to the standard sensor design in order to optimize their performance for this application. A range of 4-electrodes sensors which includes NO2, O3, CO, NO, H2S and SO2 have been designed for air quality applications. A second working electrode, or additional electrode which is often referred as an ‘auxiliary electrode’ (termed AE or WE2) (Figure 1) is designed so that it is not exposed to the target analyte gas. This provides a similar background current to the current that is observed at the first working electrode but without the faradaic current component which results from the exchange of electrons during the electrochemical reaction at the first working electrode. Figure 5 shows a typical experimental 4-electrode sensor response when it is exposed to gas, confirming that the target gas is not detected at the auxiliary electrode.

WE1 is the gas sensitive working electrode signal. WE2 is the offset correcting second working electrode signal and is a scaling coefficient taking into account the size difference of the working electrodes as well as the sign of the current. However, results show that the two electrode do not always follow each other. We argue that the mismatch is due to their different environments: the first working electrode is at the interface between the gas phase and the liquid face whereas the second working electrode is only in the liquid phase. The simple correction above works for some sensors but others require more elaborate correction algorithms. Sensitivity to more than one gas or VOCs can cause problems. In particular, both O3 and NO2 react on many sensors equally and this has been a major concern in the 12,19 past. They can now be separated by using two sensors, 20 one being filtered and the other unfiltered. The filtered sensor is specific only to NO2 while the unfiltered sensor senses both NO2 + O3. A simple subtraction yields the O3 concentration. It is also important to develop low electronic noise potentiostatic circuits. Typically, the current that is generated by the air quality amperometric gas sensors is in the order of tens of nA, so a circuit with noise of 1-2 nA is required. It is also important that the entire air quality unit shields the sensors from RFI/ EMI pickup.

Figure 5: 4-electrode CO amperometric gas sensor response to air and 50 ppb of CO with offset subtracted (Alphasense CO-B4 sensor with a sensitivity of 520 nA/ppm on a low noise potentiostatic board; the conversion from nA to mV is 0.7 mV/nA).

An example of experimental curve and corresponding calibration curve obtained with a CO sensor is depicted in Figure 6.

Correcting the sensor zero or background current is a major challenge. The sensor zero current must stabilize for a sufficient time after the instrument is switched on and it will also change when environmental conditions change. Experimental results identify temperature and humidity transients as one of the sources for the background currents. A simple approach for correcting the background current is to subtract the second working electrode signal from the first electrode signal:


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ACS Sensors and Newcastle University, while Imperial College London used mobile high performance ultraviolet differential spectroscopy instruments. A node unit used as a mobile instrument is shown in Figure 7.

Figure 6: 4-electrodes CO sensor response to various CO concentrations and corresponding linearity curve (Alphasense CO-B4 sensor with a sensitivity of 628 nA/ppm on ISB potentiostatic board, the conversion from nA to mV is 0.7 mV/nA). While the air quality amperometric sensors designed for air quality monitoring have been proven to work well in the laboratory, users must invest in efforts to prove that these low cost sensors can be used to provide useful information in a specific environment in the field. In the laboratory effectively all degrees of freedom are controlled, while in the field we either have no control over the environmental and gas variability, or very expensive air quality units with heating/ cooling and humidity control are available, but the price and power requirements exceed cities' budgets for air quality networks.

ENVIRONMENTAL MONITORING USING AMPEROMETRIC GAS SENSORS- FIRST STUDY The first use of amperometric gas sensors for air quality monitoring was made through the UK EPSRC and Department of Transport funded MESSAGE project (Mobile Environment Sensor System Across GRID Environments). The project ran from October 2006 to October 2009 and involved 5 universities and 19 non-research organizations from local government and overall coordination. The MESSAGE project included the use of small low cost sensors to increase the amount of data available for modelling pollution emissions and dispersion. Amperometric gas sensors were tested both by the University of Cambridge

Figure 7: University of Cambridge MESSAGE box including standard industrial safety CO, NO2 and NO amperometric gas sensors. Adapted with permission from [12]. Copyright 2013 Elsevier.

A first joint publication describes creating the MESSAGE infrastructure with the management of large quantities of 11 data. A second publication gives a general introduction regarding the University of Cambridge and Newcastle University field deployments and they produce a description 21 of several network deployments within various cities. While the authors indicate that the data would provide more insight into the nature of pollution and exposure, the data analysis was not provided in this article.

ACADEMIC DEPLOYMENT

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The Atmospheric Chemistry group led by Prof. R. Jones, University of Cambridge delivered a pioneering contribution into this scientific field. A research publication corresponding to studies done over six years was published 12 in 2013. Amperometric sensors were evaluated as a potential low cost sensor technology platform for building air quality networks. This work benefited from working in collaboration with Alphasense which provided sensors and support. The work focused on CO, NO2 and NO gases. Standard industrial safety sensors were slightly modified by Alphasense for the MESSAGE project to improve sensitivity. Sensor linearity, calibration, cross-sensitivity, response time and long term stability were studied in a laboratory environment leading to improvements for air quality monitoring. Tests showed that optimized


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amperometric sensors are adequate for measuring ppb levels of pollutant gas mixing ratios with an instrument detection limit of 2 to 10 ppb (ratio signal to noise of 3 ). While the first part of the study dealt with laboratory tests the second part of the study dealt with sensor node design and with the sensors deployment in the field both as mobile and static networks (node unit used as a mobile instrument in Figure 7). Optimized CO-AF, NO2-A1 and NO-A1 sensors were used in a portable instrument while modified CO-BF, NO2-B1 and NO-B1 were used for static networks. Some of the amperometric sensor nodes were collocated with a reference air quality monitoring station and measurements comparison show that the sensors respond to ppb concentrations of the analyte gases.

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curve d)) show that the morning (ca. 7:00 am) and afternoon (ca. 6:00 pm) rush hours are well detected. NO measurements as depicted in Figure 9 (A) produce an even more temperature sensitive sensor zero than CO. NO sensor measurements were found to be strongly correlated to temperature. The raw NO data presented in Figure 9 (A) shows spikes associated with individual pollution events superposed on a strong diurnal baseline variation. To deal with this problem some work was devoted to develop a temperature correction procedure using post data acquisition analysis on sets of NO measurements. The experimental curve was corrected by calculating a baseline using the minimum measurement, as a function of temperature, made in a given time interval of t t. The time internal was chosen to best fit with temperature response time.

Figure 8. CO measurements in the urban environment of Cambridge for different periods a) 2.5 months, b) one month, c) one week, and d) one day. All measurements are 30second averages. Adapted with permission from [12]. Copyright 2013 Elsevier. However, it was also found that the sensors output, or more precisely the baseline, follows a diurnal pattern. The diurnal feature corresponds to the diurnal temperature variation evidencing the strong influence of temperature on the sensor output. An example is given in Figure 8 which depicts CO measurements over various time periods. Measurements over a 3 months’ period show complex features with multiple pollution events (Figure 8 curve a). Shorter time series (Figure 8 curves b) and c)) show that short pollution events are overlaid onto a baseline which follows a diurnal pattern. The time series over a day (Figure 9


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ACS Sensors baseline correction is shown, together with reference instrument measurements, in Figure 9 (B). Figure 9 (C) shows that the corrected data correlates well with the reference instrument measurements. Overall, post acquisition data analysis led to satisfactory results and the authors concluded that: ‘low-cost miniature sensors previously considered at best indicative can, when suitably operated, be used for fully quantitative measurements of urban air quality’. Further published work by the University of Cambridge in 2016 focused on baseline temperature compensation in a 22 separate publication. The fitted baseline methodology was implemented on multiple datasets. The baseline correction used the minimum measurement value made in a given time interval of t t. t was optimized using the best regression 2 coefficients (R ) when correlating the calculated baseline with the exponential fit for NO and the linear fit for CO. Post acquisition data analysis used CO and NO measurements recorded during a 2010 sensor deployment in Cambridge. Specifically, data obtained from sensor node in a standard outdoor urban location was compared with data obtained from a second sensor node and a collocated reference instrument which where both were located a temperature controlled indoor station. The study calculated the hourly mean of 0.2 Hz measurements and the time internal was optimized for best fit with temperature. It showed that a good correlation with the collocated reference air quality reference instruments can be obtained, (i) directly for the sensors in a temperature controlled environment and (ii) after applying the fitted baseline correction for the sensors th deployed outdoor. Basic 4 electrode corrections were also tested using data collected at Heathrow Airport. It was found that the fitted baseline method described above tracks well th the 4 electrode for NO, but a slightly less good agreement was found for CO. Sensor sensitivity drift for the NO sensors was not statistically significant during the 22-month period, allowing us to conclude that long-term studies can operate with increased data reliability.

Figure 9: (A) Electrochemical sensor time series of NO measurements at 0.2 Hz. The graph includes raw NO data (red curve), the fitted baseline (blue curve) and the temperature profile (grey curve). (B) Electrochemical sensor time series of NO measurements corrected for temperature baseline effects at 0.2 Hz (green curve), hourly average (blue curve) and hourly mean NO measurements from an equivalent reference chemiluminescence instrument (red curve). (C) corresponding correlation plots for the 24h period shown in (A) and for the whole week period shown in (B). Adapted with permission from [12]. Copyright 2013 Elsevier. The temperature correction derived from this temperature-

Another study published by the University of Cambridge in 2015 shows that networks can provide 23 pollution source information by spatial scale separation. Pollution source attribution between regional, near field and far field can be estimated using spatially dense and high temporal CO measurements. Local pollution is the sum of regional baseline pollution and local contributions. Data obtained in urban locations was compared with data obtained in rural locations. The contribution of regional pollution in urban spaces was estimated as the average of the pollution measured at the nodes rural locations. The time scales of pollution levels accessible when using fast (less than 1 minute) sampling rates allows discrimination between


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these two cases as localized events occur over a short time (Figure 10).

Figure 10: Time series for CO illustrates the source contribution (area in green: near field, red: far field, blue: regional and grey: uncertainty to regional CO signal) for 0.1 Hz measurements in the urban environment of Cambridge. Adapted with permission from [23]. Copyright 2015 Elsevier. Apart from the points discussed above, these studies provided valuable practical experience in instrumentation and general knowledge of sensors performance specifications. For example, it was found that GPRS transmission generated high level of noise on signal output. It was also found that most amperometric sensors have adequate sensitivity stability over time. Other significant deployments include an air quality study at Heathrow Airport where about 50 Sensor nodes collected 24 data continuously for a period of 22 months. Each sensor node included CO, NO, NO2, O3 and SO2 Alphasense electrochemical gas sensors. 400 million data sets were recorded- one of the largest low cost AQ data set to date. Another study co-authored between researchers from ENEA and the University of Cambridge published in 2016 shows that a dynamic neural network approach improves the stochastic estimation of pollutant concentrations by chemical multisensory network devices in real world 25 deployments. The study also shows that environmental correction algorithms for electrochemical sensors can be further improved. Data used the Alphasense NO2-B4, O3-B4 and NO-B4 sensors from the Cambridge deployment.

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A paper by Pereira-Rodrigues et al. from Cairpol, written in collaboration with the Ecole des Mines d’Ales and Veolia Environement, France in 2010 is another early publication dealing with the deployment of amperometric 10 sensors in a network. Cairpol developed CairHaz, a low cost miniaturized H2S monitor which includes a filter for suppressing humidity transients and a fan rather than depending on diffusion. The monitor detected both H2S and CH3SH. Thirty units were deployed in a wastewater treatment plant in the South of France. The study demonstrated that it is possible to map odors over an extended area but results were not compared with a reference analyzer. A paper published in 2015 by Masson et al. from the University of Colorado at Boulder provides insight into 26 temperature correction. The authors developed a model starting from field data. NO-B4 electrochemical sensors (Alphasense Ltd.) and a collocated reference instrument were used. The sensitivity was first determined as a function of temperature from field data sets where we know the NO concentration by referencing to an analyzer. The sensor zero was subtracted from the sensor signal using the equation describing zero variations with temperature (see eq. 1 below, where T is the Kelvin temperature). When humidity was above 75% rh the data was ignored because they found that there were significant errors above this humidity. Second, sets of data when NO was absent were used to determine the relation between the sensor zero and temperature (see eq. 2 below).

With S the sensor sensitivity, T the temperature in °K, a1, a2 and a3 the equations coefficients for the fitted curve. I0 is the sensor zero current, a4, a5 are the equations coefficients for the corresponding fitted curve. The NO concentration was calculated from the sensor signal by first subtracting the calculated sensor zero and then dividing by the calculated temperature dependent sensitivity. Including the auxiliary electrode signal in the model resulted in an improved fit. Figure 11 shows the relation between working electrode and auxiliary electrode signals. The sensor zero was found to depend linearly on the auxiliary electrode signal.


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With I0 the sensor zero current, Iz is the auxiliary electrode signal, b1 and b2 the equations coefficients for the corresponding linear curve. The calculated sensor zero was derived from the auxiliary electrode output in this model. RMSE of ca. 12-14 ppb were obtained for both models but the second model, which uses the auxiliary electrode was found to be superior at high humidity.

Hong Kong. The study confirmed good sensor linearity and determined a limit of detection of 6 ppb for NO2 and 20 ppb for CO. The CO sensor showed no discernible performance changes in these temperature and humidity ranges. The NO2 sensor showed little impact of temperature but an increase in sensitivity with increasing humidity. The correction used for NO2 is formulated in eq. 4 below (C is the calculated concentration in ppb, V is the voltage, RH is the value for the relative humidity and the a, b, c, d are factors derived from laboratory and field data).

Figure 12 shows the field comparison results between the electrochemical sensors' processed data and the AQMS data. A very good agreement was observed with a regression 2 coefficient R of 0.97 and 0.90 and a mean error of 2 ppb and 14.1 ppb respectively for CO and NO2.

Figure 11: NO-B4 working electrode and auxiliary electrode signals including a linear fit of the observed zero current. Adapted with permission from [26]. Copyright 2015 MDPI. Interestingly, the model which does not include the auxiliary electrode showed that the parameters used in the month of January were applicable to December and February but the September parameters had a different offset at higher temperatures. The authors decided to determine the coefficients in their model using a larger set of experimental data (January + September) and as a result a good correlation could be attained for each month (RMSE of 12.1 for September and 14.9 for January). The authors concluded that for a specific region the sensors would need to be deployed for a sufficient period of time to determine the model parameters for that season. A paper published by Sun et al. from the University of Hong Kong in 2015 produces a correction algorithm using data collected before and during the 2015 Hong Kong 27 marathon. They used Alphasense NO2-B4 (with ozone filter) and CO-B4 sensors assembled on low noise circuit boards. Extensive laboratory characterization allowed determination of the effects of temperature and humidity. The temperature range was 15-21°C and the humidity range was 40-70%, corresponding to usual winter conditions in


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Figure 12: Comparison between the electrochemical and the AQMS measurements (A) for CO and (B) for NO2. Adapted with permission from [27]. Copyright 2016 MDPI. Data recorded during the 2015 Hong Kong marathon was broadcast in real time. The authors found that high quality data was obtained when following rigorous characterization and quality control procedures, insisting that uncorrected data is not adequate. The study gave an insight into the relevance of low cost sensing networks for personal exposure and their detailed analysis lead to several practical propositions. A study from the University of York published in 2015 by Lewis et al. specifically addresses the performance of low cost 28 chemical sensors for air pollution research. Amperometric gas sensors, photoionization detectors (PIDs) and particle matter sensors are included in the study. The study encompasses laboratory tests as well as tests in outdoor ambient air. A particular focus is given to cross-interferences and field data are compared with reference instruments. The authors point out that the unusual way in which this technology was developed using basic devices designed for other applications. They also note the lack of peer-reviewed literature concerning the technology and emphasize on the exceptionally challenging objective. The current state of the art is qualified it as a ‘work in progress, with promising performances for some chemicals and rather poor performances for others’. The authors also stress the need of more laboratory multi-parameter testing. Dealing more specifically with amperometric gas sensors, 2o sets of CO-B4, OX-B421, SO2-B4, NO-B4 and NO2-B4 sensors from Alphasense were tested. Field-testing gave a correlation 2 coefficient R of 0.9, 0.73 and 0.25 respectively for O3, NO and NO2 when compared with reference instrument measurements. It was found that the measurement dispersion between sensors was small and the lack of correlation in the case of NO2 was attributed to the sensor being sensing other parameters, with cross-sensitivities playing a large part. Some of the cross-sensitivities reported do not match with sensor manufacture specifications. Furthermore, it can be pointed out that this work does not mention a data correction methodology to subtract the effect of temperature variation. It could be then assumed that the data presented is uncorrected or that the authors rely on instrument manufacturer ‘inbuilt’ data processing and correction. Previous studies have shown that data correction is mandatory to produce good quality data. If no correction was applied, this study then confirms the need of temperature correction. If an inbuilt instrument correction was used, then we rely on a particular instrument manufacturer algorithm. Instrument manufacturer will adopt various methodologies

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for temperature correction and the instrument performance will be very dependent on the efforts that a particular instrument manufacturer has invested. It is very clear, given the effects of environmental effects, that data correction will play an important part in instrument accuracy. A second study from the University of York was 29 published in 2017 by Pang et al. The study focuses on the Alphasense ozone sensor OX-B421 with a low noise potentiostat. The authors used a baseline drift correction by subtracting the auxiliary electrode signal. Laboratory tests 2 showed a very high degree of linearity (R =0.995) and a limit of detection of 5 ppb. The sensors were then deployed to measure O3 uptake by sea water measurements and to measure air quality for 18 days. Cross-interferences to NO2 and NO were corrected. The data obtained was compared to a reference instrument. The authors concluded that amperometric O3 sensors are adequate for these applications, but they also highlighted the problems of rapid humidity changes and interfering species. The authors also state that these sensors are also relevant for specialized laboratory studies where reference instruments cannot be used such as measuring gas concentrations in small volumes. A third study from the University of York was published 18 in 2017 by Smith et al. The study focuses on the Alphasense carbon monoxide sensor CO-B4 with a low noise potentiostat as well as on a metal oxide VOC sensor. The authors advocate the advantage of using sensor clusters, making then possible to minimize errors due to individual sensor drift. A paper by Hasenfratz et al. from ETH, in collaboration 30 with EMPA and FHNW was published in 2017. The study deals with measurements made over more than two years using mobile sensor nodes installed on top of public transport vehicles in Zurich. They collected more than 50 million measurements. The Alphasense CO-B4 and NO2-B4 sensors, along with metal oxide O3 sensors and particle counters were used. The authors focused on creating pollution maps; their model was then tested by comparing with reference particle counters. An article published in 2015 by Arfire et al. from EPFL 31 addresses the modelling for CO 4-electrode sensors. using City Technology carbon monoxide sensors. Complex models taking into account temperature and temporal drifts were tested to provide sensor field calibration. The performance of the algorithms was tested with data obtained using a mobile sensor network deployment in Lausanne. An article from the University Politehnica of Bucharest by Firculescu et al. published in 2015 studied mobile low cost


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air quality monit0ring applications. A very compact node using the miniature Alphasense CO-D4 sensor was used but they found that the sensor was sensitive to temperature and wind. Data was collected with the node located in a car; the temperature range was 20-25°C while following the same route five times in Bucharest. An article published in 2014 by Pokric et al. from DunavNET and the University of Belgrade presented the 33 real time monitoring of air quality. Alphasense O3-B4, NOB4, NO2-B4 and CO-B4 sensors were used within the European CITI-SENSE project. The sensors gain and offset were determined from field data. Cross-correlation between sensors is proposed as a way to check sensor status. A calibration curve re-adjusted at regular interval was proposed. A study by Brynda et al. from the Czech Technical University in Prague focused on sensor module design and building a network using Alphasense CO-B4, NO-B4, NO234 B4 and SO2-B4 sensors. Only CO preliminary results are discussed. The authors found good agreement with the reference instrument for CO but they stressed the need for calibration of sensitivity, offset and temperature. A paper from the Pontifical Catholic University of Peru by Chavez et al. published in 2015 shows the relevance of low cost amperometric sensors networks in developing 35 countries. The nodes included amperometric sensors, NDIR CO2, galvanic O2 sensors, particle monitors, temperature and humidity probes. Alphasense CO-B4, NO2-B4, SO2-B4, O3B4 sensors were used with low noise potentiostats. Data was displayed in near-real-time on a website. Despite some differences the authors found that the nodes data were consistent with the reference data and concluded that the results are useful enough for air quality assessment purposes. A study from Queensland University of Technology by Villa et al. published in 2016 deals with the development and validation of an unmanned aerial vehicle 36 (UAV) based system for air pollution measurements. Alphasense CO-B4, NO2–B4 and NO-B4 were used in this study. In some cases, UAV systems can provide the possibility to get close to pollution sources which are not accessible otherwise. The paper gives guidelines on how to develop UAV systems to measure point source emissions. A study from MIT by Hagan et al. made available on the web in August 2017 deals with SO2 measurement on the 37 Island of Hawaii. The Island of Hawaii offers the remarkable characteristics of presenting a wide SO2 dynamic range while other pollutants concentrations are very low.

Nine SO2 probes were collocated with two reference instruments for a five months-period. The sensors sensitivities were found to be very stable and calibration using nonparametric algorithms lead to excellent 2 performance with RMSE < 7 ppb and R > 0.997 when considering the 0 to 2 ppm concentration range. It was also found that the error was less than 4 ppb when considering only the 0 to 50 ppb concentration range.

REAL TIME MEASUREMENTS IN THE FIELD BY RESEARCH INSTITUTES AND ENVIRONMENTAL AGENCIES We review in this section projects and studies funded by the European Union and the USA through their research grants and environmental agencies. The European Union plays an active role in encouraging air quality monitoring using low cost sensors. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 (ambient air quality and cleaner air for Europe) sets clear objectives for air quality in general and 4,5,38 also refers to indicative measurements. The use of low cost sensors is encouraged by the European Union. The European Union is active in financing collaborative research projects. CITI-SENSE is an example: a FP7 project which set up low cost sensor networks in nine European cities. Other air quality projects were funded by the FP6 and FP7 programs. The European Union COST (European Cooperation in Science and Technology) action TD1105 (EuNetAir) focused on European Networks and New Sensing Technologies for 39 Air-Pollution Control and Environmental Sustainability. This network of scientists looks at air quality sensors, validation and standards, and coordinated networks. The objectives are to coordinate and manage interactions between specialists to address environmental issues and to establish European leadership in the green economy and competitiveness of the European industry. The EuNetAir network includes experts encompassing all aspects of air quality from nanomaterials and low cost sensor research, to power management, wireless communications, modelling, standard and protocols. 80 institutions from 35 countries are represented in the network. In particular, CEN 264 WG 42 is currently drafting a standard for validation of low cost gas and particulate monitoring networks.

Another aspect is providing independent evaluation of the technology by the European Commission Joint Research Centre (JRC) laboratories. Dealing with low


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cost sensor technology in real world environments poses the problem of identifying the best technologies available and the best protocols for performance validation. Significant work was published by Spinelle, Gerboles et al. from the JRC, in collaboration with Aleixandre from CSIC, the Spanish National Research Council13,40-45 Examples of performance evaluation include the evaluation of amperometric sensors from several companies as well as the Cairpol sensor module for the monitoring of O3 and NO2.13 It was shown that in laboratory the limit of detection is under 10 ppb and that unfiltered NO2 and O3 sensors detected both NO2 and O3 gases without discrimination. Another example is the detailed performance evaluation of the Alphasense O3-B4 sensor.40 The JRC also conduced sensor performance evaluation in the field in collaboration with ENEA, as well as CSIC, the Spanish National Research Council and Phoenix Sistemi & Automazione.42-45 Calibrations against reference instruments over several weeks assessed if the sensors could reach the Data Quality Objective (DQO) of a maximum of 25% uncertainty set by the European Air Quality Directive for indicative methods. Different low cost sensors were tested. Several sensor calibration methods were studied, including linear regression, multivariate linear regression and artificial neural networks. The study highlighted the difficulty of getting adequate correlations between sensor and reference measurements.

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A study coordinated by the Institute of Environment and Development (IDAD) and performed in collaboration with European partners compared field performance of several sensor modules46 This is a significant study which deployed sensors nodes for two weeks in Aveiro. Various research centers and companies from 12 countries from the European network EuNetAir were involved in the campaign. Particle monitors, metal oxide sensors and amperometric sensors were studied, all simultaneously compared with reference analyzers. Suppliers included Cambridge SNAQ boxes, ECN Airbox nodes, Envira NanoEnvi boxes, Environmental Sensors AQMesh boxes, ENEA/Air-Sensor boxes and VITO/EveryAware SensorBox nodes. Amongst other technologies, most were equipped with NO, NO2, O3 and CO amperometric sensors from Alphasense B4 series and an NO2 City Tech 3E50 in the ECN box which includes a filter for cross-interference and humidity. Temperature correction was not defined. The AQMesh unit achieved the highest correlation coefficient and the lowest mean absolute errors: R2 was 0.70 for O3, 0.89 for NO2, 0.86 for CO and 0.80 for NO. Other sensor boxes were unable to provide the same degree of correlation, possibly because of the lack of data correction for temperature variations. The correlation graphs for CO are presented in Figure 13, showing variable results. The authors concluded that low cost sensors have potential for air quality monitoring if supported by proper post processing and modelling tools.


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Figure 13. October 2014 Aveiro, Portugal, trial of senor modules during an EuNetAir joint exercise. Sensor modules and reference instruments correlation plots for CO with (A) ENEA/Air-Sensor boxes, (B) Envira NanoEnvi boxes, (C) Environmental Sensors AQMesh boxes and (D) Cambridge SNAQ boxes. Adapted with permission from [46]. Copyright 2016 Elsevier.

A study published in 2017 by Castell et al. from the Norvegian Institute for Air Research (NILU) collaborating with Technion, evaluated 24 identical AQ Mesh units against CEN reference 47 analyzers. This work was partially funded through the CITISENSE project. The AQMesh nodes were fitted with NO, CO, NO2 and O3 Alphasense B4 sensors and field data was collected for six months. Laboratory correlations were R higher than 0.9, but field correlations were significantly lower. The European Air Quality Directive for indicative methods were not met for CO, NO2 and O3. The authors concluded, however, that low cost air quality monitoring is promising as it can provide coarse indications about the level of pollution. While European Union actions have been detailed here, the US EPA conducted parallel testing in the USA. Snyder et al. from the US EPA wrote that the EPA objective is to ‘facilitate, communicate and promote the responsible use of 13 air pollution sensor data’. Actions include conducting workshops, developing a draft roadmap, making a link

between various organizations and producing independent studies. Cairpol instruments were tested in a study by Duvall et al. 48 from the US EPA in 2016. CairClip NO2 and CairClip O3/NO2 miniature nodes equipped respectively with NO2 and O3 + NO2 amperometric sensors were deployed in Houston and Denver. The O3 concentration was derived from the output of a filtered sensor (NO2 sensor) subtracted from the output of an unfiltered NO2 sensor. They were used in a network operated by community members, mostly teachers and students: citizen scientists. Agreement between the CairClip measurements and the reference measurements for O3 was found, but not for NO2. Lessons learned included management of a ‘citizen scientist’ network, including communications format, training for data collection and communication with the community. The authors focused


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on low cost sensors' performance evaluation, stressing that studies must have extended periods. However, data correction methodologies were not discussed in the paper and the lack of correlation observed for NO2 seems to indicate that temperature data correction was required. Another study with Cairpol instruments included NO2 and O3 sensors as well as AQMesh instruments with NO2, O3, NO, CO and SO2 sensors was published by Jiao et al. 49 from the US EPA in 2016. The Community Air Sensor Network (CAIRSENSE) project aimed at understanding the capability of emerging air sensor technology and the deployment of sensors in cities. The study shows variable agreement between low cost sensors measurements and reference monitors for ten months in 2014-2015. The two AQMesh units showed weak correlation for O3, very variable correlation for NO2, good correlation for NO (r=0.88-0.93), good correlation for CO (r=0.79-0.82) and no correlation for SO2. The two Cairclip sensors showed good correlation for O3 (r=0.82-0.94) and good correlation for NO2. They recommended collocation with a reference monitor when designing field studies. Very recently (2017) a publication by Fishbain et al. from the Technion, reviews the mathematical tools available for assessing the performance of air quality micro-sensing units 50 in varied applications and environments. This is in fact a collective publication co-authored between academic groups involved in the European project CITI-SENSE. The idea being that laboratory assessments will not always reflect the sensor performance in the field. The authors apply a list of criteria and mathematical techniques to sensors data collected in eight European cities. A comprehensive ‘sensor evaluation toolbox’ is proposed to assess various performance aspects such as the capability to locate pollution sources, to measure background pollution or to see short pollution events for example. The ‘sensor evaluation toolbox’ includes eight assessment criteria: Root Mean Squared Error, Pearson, Kandel and Spearman correlations as well as four new performance measures. An Integrated Performance Index (IPI) is defined using the different assessment criteria measurements to give an overall sense of sensor performance.

THE ROLE OF PRIVATE COMPANIES Private companies’ commitment has played a major role in the scientific development of this technology. We list below the main companies. Alphasense Ltd. is a gas sensor manufacturer. Alphasense was an industrial contractor in the MESSAGE project and has funded both external and internal research into air quality sensor development since 2007, introducing a new family of amperometric sensors for air quality. Alphasense also

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provides PID and metal oxide VOC sensors, and particulate monitors. Environmental Instruments Ltd. was the first provider of integrated air quality networks incorporating specifically designed 4-electrode air quality amperometric sensors through the AQMesh. The first AQMesh was field trialed in 2011 though it became available commercially only a few years later. Raw sensor output, not data, is transmitted to the company server for correction (and then distribution). Correction algorithms use their proprietary software. Numerous comparison field trials data are published regularly on the company website. Cairpol. was created at the end of 2006 in the technology incubator of the Ecole des Mines d’Ales (EMA). Cairpol’s first objective was to create very sensitive instruments to provide air quality instruments for asthmatics, then increasing it's scope in 2011 deploying a network to monitor H2S using amperometric gas sensors. Envirowatch Ltd was created in 2010 and started commercializing the E-Mote the same year. The company started with a transfer of technology from Newcastle University, which was a major partner in the MESSAGE project. The first E-Mote delivered real-time measurements data and was equipped with a solar panel. New entries into the market include Kunak, Ateknea, Aclima, South Coast Science, Vaisala and Bosch-Intel. Other major companies are entering this market. It is important that air quality network companies work with the city, providing the hardware, communications, cloud data transfer plus effective deployment and maintenance and finally data analysis: a challenging supply chain. Working with US EPA, UNEP, WMO, CEN will become a more important requirement for network acceptance. The demands are moving from networks in European and North American cities to African, south Asian, south American and Chinese cities. In general, companies offer various options regarding hardware and services. Companies have very different approaches and they have an essential role in delivering good quality data. In particular, investment in product development, data analysis procedures and field validation is essential to produce end-user high quality data. Commercial instruments need to be able to provide air quality data in real time. A first possibility is to get the data analyzed by a program loaded onto the instrument. Another possibility is to get the raw data sent to a data analysis center where the analysis will be operated (Figure 14). This second option offers great flexibility to alter programs parameters and allows a ‘skilled’ operator to detect sensor failures. In


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real-world applications instruments are subjected to a large range of conditions depending in geographical conditions, local parameters and final positioning. Server-centric calibration will have an advantage over on-device calibration when considering large scale market-viable models. Furthermore, the introduction of dynamic methods which integrate peripheral data in the centralized data analysis model will bring new possibilities in terms of calibration/data management.

Figure 14: Real-time data transmission to analysis center and analysis before redistribution. Adapted with permission from Environmental Instruments Ltd. Careful analysis of the data and baseline correction is also needed. Recent examples are given in Figure 15 for NO, Figure 16 for NO2 and Figure 17 for CO.


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Figure 15: AQMesh monitor real-time NO corrected field data collected from Marylebone road, London, UK. The data used to produce these graphs were provided by Environmental Instruments Ltd. (A) Time trace for a two-months period, (B) time trace for a two-weeks period and (C) time trace for a two-day period. (D) Correlation plot for the two-months period. Sampled data were averaged in one hour periods. Adapted with permission from Environmental Instruments Ltd.


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Figure 16. Summer 2016 NO2 E-Mote real-time corrected field data from Milton Keynes, UK, provided by Envirowatch Ltd. Sampled data were averaged in one hour periods. Adapted with permission from Envirowatch Ltd.

temporal density required for real time urban air quality mapping and urban infrastructure improvement. The studies referenced here show various performances from poor to good- this variance can be explained as due to: 1- sensors have improved since 2006 2- testing methodologies and electronics/housings are not yet standardized 3- data analysis is critical and is only now being better understood. Combining laboratory and field testing, and moving networks to more extreme environments will provide the data sets required to further improve sensor performance and artificial intelligence (AI) correcting algorithms.

AUTHOR INFORMATION Corresponding Authors * John Saffell, Technical Director, Alphasense Ltd., email: [email protected] * Ronan Baron, Senior Scientist, Alphasense Ltd., email: [email protected]

Author Contributions Figure 17: Winter 2016 Kunak monitor real-time CO corrected field data from Pamplona, Spain, provided by Kunak Technologies S.L. Sampled data were averaged in one hour periods. Adapted with permission from Kunak Technologies S.L.

The manuscript was written by both authors. All authors have given approval to the final version of the manuscript.

Funding Sources The authors declare no competing financial interest.

ACKNOWLEDGMENTS CONCLUSIONS Amperometric gas sensors have been trialed for air quality monitoring applications and are one of the most promising technology for indicative toxic inorganic gas measurements. As this new technology has been introduced into the new air quality network market, lessons have been learned. Low cost air quality sensors are not replacements for reference analyzers, but they can provide the high spatial and

The authors thank Peter Neasham and James Neasham from Envirowatch Ltd., Javier Fernandez Huerta from Kunak, Amanda Billingsley and Steve Earp from Environmental Instruments Ltd. for sharing field data. The authors also acknowledge Professor Roderic Jones at the University of Cambridge Department of Chemistry for continued support and partnership.

VOCABULARY


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Air quality sensors. Air quality sensors refer to sensors for measuring both ambient indoor and outdoor pollution due to toxic gases and aerosols. It is normally expected that measurements will be below short term immediate threat to human life even though long term effects are known to be dramatic; Safety sensors. At the opposite, safety sensors are sensors designed to save the life of people potentially exposed to immediately dangerous high levels of toxic gases. They will be used, in general, in confined spaces where there are safety concerns due to possible toxic gas concentration build up; Spatial scale separation. Spatial scaling here refers to the distinction between regional, near field and far field pollution sources. Localized pollution events happen over shorter time scales than the total contribution of all the regional pollution. So when considering near field pollution high frequency measurements capture local pollution whereas regional pollution constitutes the signal baseline; Near field, far field and regional pollution sources. The definition of the area covered by each of the near field, far field and regional pollution sources in a pollution model can be adapted to a particular site where we can distinguish between different types of contributions. This can depend for example on the geographic location, the topography or the population density of the site.

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84x47mm (300 x 300 DPI)


Amperometric Gas Sensors as a Low Cost Emerging Technology

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