Indoor air quality monitoring

Indoor air quality monitoring

CHAPTER 11 Indoor air quality monitoring € tze, Tilman Sauerwald Andreas Schu Lab for Measurement Technology, Saarland University, Saarbr€ ucken, Ger...

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CHAPTER 11

Indoor air quality monitoring € tze, Tilman Sauerwald Andreas Schu Lab for Measurement Technology, Saarland University, Saarbr€ ucken, Germany

11.1 Introduction People spend most of their time indoors, either in their home, in the office, in public spaces like restaurants or museums or in transport, both private cars or public buses, trains, and airplanes. Recent studies estimate that more than half a million premature deaths in Europe are caused by air pollution [1] and the EU project Healthvent stated that outdoor and indoor air contribute similarly to the overall burden of disease [2]. Air pollution leads to serious health effects, especially cardiovascular (CV) diseases, asthma, allergies, and lung cancer, but also other types of cancer and even diabetes. In the context of this chapter, we will address only gaseous pollutants, which can be detected by low-cost gas sensors, but one should be aware that particles also play a very important role for air quality, both in- and outdoors. Indoor air quality (IAQ) is a complex issue being influenced by a wide range of factors. Today, the most common approach for indoor air monitoring is based on determination of the CO2 concentration, which goes back to studies by Pettenkofer in the 19th century. For over 150 years, the guideline value of 1000 ppm CO2 as recommended by Pettenkofer has been used for determining indoor air quality [3]. Over time, most users have forgotten, however, that Pettenkofer introduced this value as an indicator only, not as the cause of poor air quality. In fact, the Pettenkofer limit is suitable if deterioration of the air quality is primarily caused by the presence of humans, which increases not only the concentration of CO2 but also many other gases by respiration. Specifically volatile organic compounds (VOCs) also associated with human respiration and perspiration lead to poor indoor air quality, also associated with the sick building syndrome especially in modern energy-efficient buildings. As an alternative indicator, determination of the total VOC concentration (also named TVOC value) is proposed, which allows the use of lowcost gas sensors instead of the more costly NDIR sensors required for CO2 monitoring. Both methods are however severely limited if the air quality is affected by other pollutant sources, for example carbon monoxide (CO) from stoves or heaters, formaldehyde from furniture or building materials, or benzene from outdoor sources. Indoor air monitoring therefore can address various applications, with the main fields being safety and ventilation control. Safety primarily addresses monitoring of CO to

Advanced Nanomaterials for Inexpensive Gas Microsensors https://doi.org/10.1016/B978-0-12-814827-3.00011-6

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prevent acute poisoning and of combustible gases, primarily methane, to prevent explosions and fires. Note that the latter application was the original motivation for the development of metal oxide gas sensors (“Taguchi sensors”) by Naoyoshi Taguchi in the 1960s, which led to the establishment of Figaro Engineering in 1969 [4] and marked the kick off for the now 50 years of intensive research in materials, gas sensors, and sensor systems. Note also that the drive toward miniaturization and large production volumes achieved with micromachined gas sensors starting approx. 20 years ago was again caused by an IAQ application, that is, cabin air quality (CAQ) in cars, leading to the development of low-cost sensor systems for automotive applications to control the air inlet in cars [5]. Due to the strong impact of heating, ventilation, and air conditioning (HVAC) on the energy consumption in buildings, ventilation control has been one main driver in recent years. HVAC systems are responsible for approx. half of the primary energy consumption in industrialized countries and estimates state that this can be reduced considerably with improved insulation and reduced air exchange rates. However, this focus on energy saving overlooks the severe impact on indoor air quality caused by increasing concentrations of pollutants due to reduced ventilation rates. Therefore, demand-controlled ventilation (DCV) is today considered as indispensable for achieving a good compromise between air quality/health and energy efficiency in buildings. However, due to the lack of suitable sensors and sensor systems, the compromise between IAQ and energy in buildings is currently greatly unbalanced favoring reduced energy consumption: Fig. 11.1. Note that DCV is also heavily connected with the comfort of people inside buildings, which is a complex interplay of air temperature, humidity, air speed, and air quality. Furthermore, not only health effects have to be considered but also odor. Again, human presence accounts for some of the odors encountered indoors but also paints, building materials and decoration like plants and candles as well as various activities like cooking, bathing, etc. contribute to the complex smells indoors. The challenge for sensor systems

Fig. 11.1 Today, the compromise between IAQ and energy in buildings is greatly unbalanced favoring reduced energy consumption because suitable IAQ sensors and sensor systems are lacking [6].

Indoor air quality monitoring

in this context is that there is no absolute good or bad because the scented air freshener might be desired by one person but seem insufferable to another as odor is highly subjective. This chapter will primarily focus on gas sensors and sensor systems for ventilation control as this application is currently mostly meant when discussing IAQ. Sensor systems for safety applications face quite different challenges due to strict legal regulations and the severe consequences of missed alarms, while air quality—both in terms of health effects and comfort—is an open and rapidly evolving field with many new players and, as of today, fairly blurry requirements and performance indicators.

11.2 Target gases and interferents For a systematic discussion of sensors for IAQ monitoring, the various target gases and their concentrations, as well as ambient conditions and the background matrix with a wide range of possible interferents have to be defined. Furthermore, test and calibration methods along with reference measurements are discussed as these play an important role for the development of low-cost sensors and sensor systems and for their later acceptance. A wide range of factors influence indoor air quality. First, unless active measures like air filtration or cleaning are used, indoor air will always reflect the outside air with typical pollutants like CO, NOx, and SO2 from exhaust gas emissions, ozone from photoinduced reactions between oxygen and other pollutants like VOCs and NO2 and many other industrial or agricultural pollutants and odors like benzene or ammonia. Typically, the concentration of pollutants from outside sources is lower inside due to consumption and filtering effects. However, indoor sources add to these outside sources, which can lead to significantly higher pollutant concentrations. As mentioned earlier, human presence is a very important factor for IAQ. Everyone knows the effect that a meeting room may seem okay to the people inside, but for someone entering from the outside, the air is stuffy and simply “bad.” People inside are not aware of this poor air quality as their senses, especially the human nose, continuously adapt to the ambient, thus blanking out slow changes and responding primarily to sudden changes, even if they are small. This “bad” air—a very complex mixture of VOCs resulting mainly from human respiration and in part also from perspiration—leads to drowsiness and headaches after some time, which can also severely limit mental capabilities. In fact, this leads to reduced productivity for not only office workers but also teachers and students in classrooms, which increases the social cost of poor IAQ dramatically beyond the direct health costs associated with diseases caused by air pollution [7]. For this reason, many IAQ solutions available today are actually only systems to detect human presence and estimate its effect on air quality. It is implicitly assumed that outside air is always clean so that air quality is improved by (increased) ventilation. However, other sources, both indoor and outdoor, are not taken into account, which would be required for a

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comprehensive ventilation strategy. Note that this also means that DCV should ideally also take the outside air quality into account to achieve the best ventilation strategy, for example, to avoid replacing moderately poor indoor air by heavily polluted outdoor air during rush hour. Indoor sources beyond human presence and, in extension, human activities like cooking are manifold and include VOC emissions from building materials like pressed wood, paints, and synthetic materials as well as inorganic gases like ozone from laser printers.

11.2.1 CO2 and H2: Indicator gases for human presence To indicate “bad” air caused by human presence, CO2 sensors are long established. This goes back to Pettenkofer who already in 1858 identified CO2 as a good indicator for human presence leading to the now widely accepted limit of 1000 ppm for good indoor air [8]. The indoor CO2 level basically reflects the amount of air exchange relative to indoor occupant density and metabolic activity. Direct health effects for CO2 occur at higher concentrations with typical limits of 5000 ppm for prolonged periods and 35,000 ppm for 15 min in the workplace [9]. This is also supported by the effective limit values for CO2 on the international space station ISS, where operational limits are 5000 ppm depending on the duration of the mission [10, 11] and only recent studies suggest lower concentrations of approx. 3000 ppm to already induce first symptoms like headaches [12]. Most CO2 sensors are based on nondispersive infrared (NDIR) absorption, a physical sensor principle that achieves highly accurate and long-term stable sensors and is therefore also used in many reference instruments [13]. On the other hand, while allowing miniaturization down to a few cm3, the cost reduction potential is limited by the necessity for various components like IR source, cuvette with an optical path length of approx. 1 cm, a sensitive IR detector, accurate assembly and calibration. NDIR sensors are either based on a dual wavelength setup allowing to compensate aging of components and contamination of optical components to achieve a stable baseline or make use of automatic baseline correction (ABC) [14] algorithms, which takes the minimum value recorded over a certain period and uses it as a one-point calibration value for the natural CO2 background concentration of 400 ppm. There have been many attempts to develop low-cost sensing materials and sensors for CO2 monitoring, for example on the basis of metal oxides [15], solid electrolytes [16, 17], or nanotubes. However, due to the low chemical interaction of the highly inert CO2, it is very challenging to achieve the required level of selectivity in a complex background of reactive gases, and therefore, no low-cost/low-power chemical sensors are available for CO2 detection in IAQ applications today. Similarly to CO2, hydrogen (H2) can also be used as an indicator for human presence in indoor scenarios as there are no significant other sources for H2 in indoor air and the

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indoor concentration of H2 therefore reflects human occupancy similar to CO2, although at much lower concentrations. The composition of human breath has been investigated extensively with analytical methods especially aiming at the understanding of the metabolome for the detection of diseases [18] and is in principle well known, although it is a very complex system strongly influenced by individual differences. These investigations normally focus on organic compounds, but studies show a breath H2 level in the order of 10 ppm [19, 20]. As the atmospheric background concentration of H2 is also much lower (approx. 500–1000 ppb) [21, 22], sensitive hydrogen sensors allow detection and quantification of human presence [23]. The advantage when using H2 as indicator gas is the possibility to use a wide range of different sensor principles [24]. Especially metal oxide semiconductor (MOS) resistive-type sensors offer high sensitivity to H2 in the low ppm and sub-ppm range [23] combined with very low cost (see also Section 11.5). The limited selectivity of MOS sensors can be improved at the sensor level by optimized materials and operating conditions [25–27] or, specifically for H2, suitable treatment of the sensor, for example with HMDSO, to increase the selectivity by reducing the sensitivity to other gases [28]. On the sensor system level, further improvement is possible with suitable sensor arrays [29], dynamic operating modes [30], and gas-specific filters [31]. While sensors for hydrogen detection are readily available, reference measurements are very complicated. The standard technique for analytical gas sensing, that is, gas chromatography (GC) with mass spectroscopy (MS) for selective detection, cannot be applied as often GC uses hydrogen as carrier gas and the MS does not detect very low masses, that is, is blind for H2. Similarly, H2 cannot be detected using optical methods as the symmetrical molecule is IR inactive at normal ambient pressure. A possible reference instrument is therefore a reducing compound photometer that uses a chemical reaction of hydrogen with mercury oxide for selective detection [22, 32]. An alternative method is mass spectroscopy mostly used for isotope selective detection of hydrogen [33]. Both methods use a GC column to separate H2 and carbon monoxide (CO), which also leads to a detector signal. Due to this complexity of achieving good reference data, many studies in IAQ completely ignore hydrogen, and little is therefore known about characteristic concentrations in typical scenarios. Note that the human nose is neither sensitive to CO2 nor to H2; thus, the human perception of “bad” air is not influenced by these gases emphasizing their suitability as an indicator or proxy for air quality only and not as a primary target gas.

11.2.2 TVOC and specific VOC While monitoring total VOC (TVOC) concentrations is state of the art [34], this parameter alone is not sufficient for the estimation of health effects since it also includes benign substances and cannot be attributed to symptoms like the sick building syndrome [35, 36]. However, if the TVOC value is basically caused by the presence of humans in a room, then

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it also accounts for typical symptoms of bad indoor air like drowsiness, the lack of concentration, and headaches. For this reason, CO2 (and now also H2; see earlier) is often monitored as proxies because their concentration is reasonably proportional to the TVOC resulting from human presence, that is, not only contained in human breath but also associated with sweat and other human excretions. Note that this VOC mixture is complex, and little is known about specific effects by individual VOCs, the effect of the VOC mixture and its sum concentration. However, the source for this pollutant is indoors; thus, increased ventilation is always assumed to lead to improved air quality. This, of course, holds also for other indoor sources. On the other hand, specific hazardous VOCs might actually come from the outside. One highly relevant example is benzene, which often results from traffic emissions or nearby gas stations [37]. In this case, the simple solution to increase ventilation does not work and actually leads to even worse indoor air quality. The same holds for other typical outdoor pollutants like CO, NOx, and ozone. Today, identification and measurement of individual VOCs is expensive and timeconsuming requiring elaborate sampling techniques and gas chromatography coupled with mass spectrometry (GC-MS) [38]. Even then, the total level is underestimated as VOCs, at very low concentrations, are very difficult to detect. Furthermore, very volatile organic compounds (VVOCs) are not collected by standard sampling methods while semivolatile organic compounds (SVOCs) are often not completely released from standard absorbent materials. Thus, both VVOCs and SVOCs are often not monitored at all with analytical methods [39, 40]. Several investigations have been performed to determine the actual occurrence of hazardous VOCs in indoor air, for example, by Bernstein et al. [41] or in European projects like the Airmex study [42] and the INDEX project [43]. From these, the VOCs occurring most often are formaldehyde, benzene, naphthalene, toluene, and limonene. Due to their carcinogenic nature, the first three are of great health concern, and therefore, very low guideline threshold values (in indoor air) have been set at 0.1 mg/m3 (81 ppb) for formaldehyde and 0.01 mg/m3 (1.9 ppb) for naphthalene according to the World Health Organization (WHO) [44] and at 5 μg/m3 (1.6 ppb) for benzene according to EU guidelines [45], which poses a great challenge for online monitoring. The last two have much higher threshold limit values (toluene 50 ppm [46] and limonene 20 ppm [47]), well above the general recommended limit for VOCs in indoor air: WHO suggest a limit for the total VOC concentration of 1 ppm, and already at 0.5 ppm, increased ventilation is recommended. Thus, VOC monitoring for indoor air quality and demand-controlled ventilation actually has two targets: first, limiting the total concentration of VOCs (TVOC) in indoor air to 1 ppm, and second, limiting specific VOCs with very severe health impact to their specific threshold limit concentration, which is up to three orders of magnitude lower than the TVOC limit. Today, many sensor systems mainly based on metal oxide semiconductor (MOS) gas sensors are offered for TVOC monitoring for private customers due to their low cost and proven

Indoor air quality monitoring

long lifetime. In professional instruments, on the other hand, mainly photoionization detectors (PID) are used to monitor the total VOC concentration. These instruments with their physical detection principle allow more stable quantification, but they are not able to differentiate between benign and hazardous VOCs. Studies by the Joint Research Center (JRC) in Ispra, Italy, for sensor systems allowing monitoring of benzene at relevant concentrations have also shown that most PIDs, often despite the manufacturers claim, do not reach a detection limit of single digit ppb concentrations, while experimental gas sensor systems based on MOS sensors combined with temperaturecycled operation show promising detection limits and good selectivity [48]. Further details are given in Section 11.3. Another area of increasing interest is the detection of mold based on microbial volatile organic compounds (MVOC) [49]. Mold formation is an increasing concern in modern buildings due to reduced air exchange and increased air tightness leading to the accumulation of humidity, for example, from exhaled breath, and then to the formation of mold, often in hidden spots. Sensor systems for background or acute monitoring and for discovery of mold spots are highly sought after [50], but the wide spectrum of microbial mold with varying emissions and the low concentrations pose a huge challenge for sensitivity and selectivity.

11.2.3 Odor monitoring In addition to health effects, odor monitoring is another target application for gas sensors in indoor situations, primarily in the kitchen or the bathroom but also in other areas of the house, for example due to sweaty sports clothing or shoes. Similarly to VOC monitoring, the typical application would be to reduce malodor by increased ventilation. Many odors are caused by—often complex—VOC mixtures, but there are also inorganic odorants like hydrogen sulfide (H2S) and ammonia (NH3). The target concentration to be detected can be as low as 20 ppt [51] depending on the human odor threshold, which is, however, not as well defined as TLV values for hazardous gases as human perception can easily vary over more than one order of magnitude [52]. Furthermore, many humans actually lack the receptors to detect certain smells at all, for example, boar taint caused by androstenone [53], thus increasing the variability of the odor perception and the ambiguity for odor monitoring. On the other hand, odor monitoring is basically a comfort application and not directly health relevant; thus, a false negative response by the gas sensor system is more acceptable. The downside is that people will notice these missed alarms, which can reduce their trust in the gas sensor system. Odor monitoring can also address the opposite, that is, monitoring of an odorant concentration that is actively introduced to improve odor in general or induce a certain smell indoors. There have been applications in the past, where a gas sensor would trigger the odorant release to ensure a pleasant level at all times. This is also used in stores to create a

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pleasant climate inducing people to spend time and money. In both cases, a sensor system to monitor the absolute odorant level is preferable to human control as the human nose will gradually adapt to the odor level, which can easily result in too high dosing, which quickly becomes unpleasant for people entering the room from the outside. For a store, this would actually have the exact opposite result of the intended effect.

11.2.4 Background and interferents One of the biggest challenges for gas sensor systems is the discrimination of a target gas or gases against a complex background of other gases that do not play a role for the target application but can cause a sensor response. This is also true for indoor air quality, especially when trying to use VOC concentrations to determine human presence as is suggested by several sensor manufacturers (see Section 11.5). In addition to human breath, there are many indoor sources of VOCs such as building materials, carpets, and furniture as background sources as well as cleaning agents, odorants, cooking, eating, and drinking as acute sources. The challenge for IAQ sensor systems is therefore to allow monitoring of target compounds against this complex background. This explains why CO2 monitoring based on the Pettenkofer value has been highly successful in the past: CO2 can be easily monitored even in a very complex VOC background using NDIR sensors, both due to the stability and selectivity of the sensors and the comparatively high concentration. Low-cost hydrogen sensors should be able to compete with CO2 monitors if sufficient selectivity and long-term stability can be achieved. Note that this requires a considerable poisoning resistance of the sensors, especially versus silicon-containing compounds like hexamethyldisiloxane (HMDSO). While these compounds, originating from plasticizers (i.e., wire insulation) and printing products typically occur only in low concentrations [54], they lead to deposition of SiO2, that is, glass, on the reactive sensor surfaces reducing the sensitivity drastically [55]. This effect can also be used to increase the selectivity of metal oxide sensors for hydrogen [27, 28], but in the long run, the sensors are poisoned completely requiring replacement. Not only the lifetime can be improved with optimized materials, but also the operating mode can help to extend the sensor lifetime. In addition, poisoning can be recognized from the sensor response pattern [56, 57], which would at least allow timely replacement to ensure the correct function. For VOC and odor monitoring, the necessary selectivity can be achieved by taking the background into account during calibration [58, 59]. However, as the background changes considerably between different buildings according to the local ambient (e.g., city vs country) and the construction (old vs new) as well as between different rooms depending on furnishings and use (living room, bathroom, kitchen, and bedroom), it might be necessary to adapt sensor systems to the local background. This would mean extending the calibration by additional on-site measurements, that is, measuring the target gas(es) in the required concentration against the typical local background, or even replacing lab by on-site calibration completely.

Indoor air quality monitoring

11.2.5 Testing of sensors Today, there are no suitable standards for testing of indoor air quality sensors for ventilation control. The ISO 16000 standard addresses several sampling methods for the determination of specific contaminants like VOCs and also covers test methods for material emissions. While Part 29 of this standard does address “test methods for VOC detectors,” these are not universally suitable as only very specific sensor models are covered by this standard [60]. For addressing IAQ and ventilation control, a suitable test should encompass relevant target gases indicating human presence, that is, H2 or CO2 as indicator gases or a VOC mixture representing normal breath components and a second VOC mixture representing typical emissions from building materials, furniture, and other household sources like cleaning and cooking. Concerning relevant concentrations, these are in the range up to a few ppm as the WHO recommends increased ventilation at a TVOC level of 0.5 ppm. Note that for analytical measurements in indoor air toluene is often used as the reference compound, that is, sensitivity coefficients of analytical instruments are referenced to toluene to allow the determination of an approximate total VOC concentration. However, this does not mean that sensor testing only of toluene would be sufficient due to the wide variation of the sensitivity for various chemical groups observed for low-cost sensors. Therefore, VOC mixtures should contain representative components from different chemical groups, that is, alkanes, aldehydes, alcohols, terpenes, and aromatic compounds, and the sensitivity of the sensor should not vary too much for different compounds to achieve an acceptable performance of the monitoring. If specific hazardous VOCs are addressed, then much lower target gas concentrations are required as outlined earlier. Test equipment allowing reproducible measurements in the ppb range is highly complex as reference gases typically contain contaminations in the ppm range. In the last years, only few systems dedicated to the test of gas sensor systems at ppb level have been reported [61–65]. Gerboles and Spinelle [61] demonstrate an approach using circulating air in a toroidal glass duct. Target gas concentrations can be closed-loop controlled by reference measurement methods. This approach is capable of testing larger devices such as complete sensor systems, and it therefore also addresses the influence of environmental parameters like air flow. The closed-loop approach allows using larger gas flow rates and continuous reference measurements. However, a drawback of this approach is the requirement for real-time reference analytics leading to high costs and considerable efforts for adaptation of the test gas profile. A different approach is the use of continuous flow mixing systems [66] with dynamic dilution that use mixtures of various mass flows. These systems can be highly adaptable to various gas profiles; however, the gas flow rate is often limited. For ppb concentration levels, this approach has two additional challenges: the generation of very small continuous mass flows for very low concentrations [63] and the minimization of side effects, for example, sorption on the tubing or dead volumes [65].

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In all cases, poisoning especially by organosilicon compounds like HMDSO needs to be addressed also during testing to allow an estimation of the long-term performance. Typical standards such as DIN EN 50194-1, one of several standards that describe test methods for detectors of combustible gases in domestic premises, require such detectors to still operate properly after being exposed to 10  3 ppm HMDSO over a time period of 40 min, corresponding to a dose of 400  120 ppmmin [67]. However, in some environments organosilicon concentrations can be as high as 300 μg/m3 (few 10 ppb) [54], thus leading to a dose of 400 ppmmin within just few days. Therefore, much higher doses are suggested for testing, for example, exposure of the sensor to 250 ppm of decamethylcyclopentasiloxane (D5) for up to 200 h corresponding to a dose of 3*106 ppmmin or approx. 10 years of operation in a background containing 40 ppb of siloxanes [68]. Similarly, sensor systems for fire detection are required to be tested with SO2 at a concentration of 25 ppm for 21 days or 8*105 ppmmin [67]. While this was originally designed to test the corrosion resistance of electronic components and connectors, for gas sensors, this is also relevant for the sensor performance due to the poisoning effect of sulfur compounds on the catalysts typically incorporated in the sensing layer. Ideally, the sensor should retain its sensitivity during this time, or it should be able to indicate that it has lost its sensitivity, for example with a suitable sensor self-test [56, 57].

11.2.6 Reference methods Most IAQ studies depend on the analysis of VOCs using standardized methods as defined in ISO 16000 part 6 for VOCs [69]. However, this test is severely limited as it defines determination of VOCs in indoor (and test chamber) air by active sampling on Tenax TA sorbent, thermal desorption, and gas chromatography using mass spectrometry (GC-MS). This limits the approach to VOCs that are adsorbed on Tenax and are released again by thermal desorption. This does neither cover very volatile organic compounds (VVOC) nor semivolatile VOC (SVOC). VVOCs will simply pass through the sampling material, while SVOCs are not released completely due to their very high-boiling point. One prominent example for this shortcoming is formaldehyde for which a specific separate sampling and quantification protocol is required [70]. Another example is the SVOC benzo[a]pyrene, a polycyclic aromatic hydrocarbon and the result of incomplete combustion of organic matter, which has been identified by the European Environmental Agency as one of the few critical hazardous compounds, which is increasing in Europe due to the increased use of wood stoves [71]. In addition to VVOCs and SVOCs, the standard method has limitations with even more organic compounds. Due to the GC approach, the volatility is often correlated with (inverse) retention time; a common definition of VOCs is organic compounds having a retention time between hexane and

Indoor air quality monitoring

hexadecane. This definition, however, only works for substances with similar polarity as the typically nonpolar GC stationary phase. Polar organic compounds like oxygencontaining VOC (OVOCs) have a much shorter retention time in a nonpolar column compared with nonpolar substances of the same volatility and are therefore also not measured by many analytical methods. This means that standard measurement techniques are actually blind to a fairly wide range of compounds and that our understanding of environmental pollution and health effects is somewhat limited due to missing data on these compounds. In addition, sampling only allows determining time-weighted average (TWA) values either for the long term (1–24 h) or the short term (5–60 min), thus possibly missing relevant short concentration peaks. Sensor-based monitoring, on the other hand, would allow both an improved temporal resolution and a wider detection spectrum, as sensors will respond to practically all VOCs including VVOCs, SVOCs, and OVOCs as well as radicals, which might be formed, for example, during a fire. The lack of suitable practical reference methods for indoor air is a limiting factor for the development and testing of lowcost sensor IAQ monitoring systems.

11.3 MOS sensors for IAQ monitoring Sensors for IAQ monitoring based on VOCs and hydrogen have to combine high sensitivity down to the ppb level with robustness and low price. For a detailed assessment of indoor air quality, additionally an option for selective detection is needed. Metal oxide semiconductor (MOS) sensors are the most promising choice for this combination. Dating back to the first description of metal oxide sensors by T. Seiyama et al. over 50 years ago, high sensitivity has been known, which at that time exceeded the sensitivity of conventional detectors by three or more orders of magnitude [72]. Despite the development of other highly sensitive detectors like photon ionization detectors (PID), MOS sensors still achieve very good sensitivity to a very broad range of gases and can be combined with various multisignal methods. Commonly, MOS sensors operate with the change in resistance/conductance being recorded as sensor signal [73] but also other transducer principles, for example, field effect transistors [74] or thermoelectric readout [75], have been reported. The resistance of granular films of an n-type semiconductor can change dramatically even for low concentrations of reducing gases especially when it is dominated by so-called grain boundaries. A grain boundary can be modeled by a point contact of two small crystallites (grains) of the semiconductor. In the case of tin dioxide (SnO2), the material with by far the most references and the widest use in commercial devices, the surface of the semiconductor is covered in air with acceptor states, which are attributed to oxygen species [73, 76]. The negative surface charge causes a depletion layer in the oxide

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bulk corresponding to a band bending with the energy EB. The electrostatic repelling of charge carriers by the surface charge then strongly reduces the conductance G at the boundary: G ¼ Go ∗e

EB

=kB T

(11.1)

Reducing gases will react with the adsorbed oxygen, which reduces the surface charge and the corresponding band bending. In steady state, the change in conductance typically follows a power law with respect to the gas concentration [76, 77]. Commercial sensors often specify detection limits of a few ppm or few 100 ppb for VOCs like benzene [48], but these limitations are often mainly due to uncompensated sensor drift, the limitation of readout circuits or the lack of suitable reference measurements.

11.3.1 Commercial sensors Commercial MOS sensors have been available for five decades, and the automation and integration of sensor manufacture technologies has seen four major steps: • Manual processing • Automated processing in planar technology • MEMS technology • Integration with ASIC As in other technological developments, a progress in automation means higher one-time cost for the process equipment combined with reduced costs per item or item functionality. Size of market and market perspectives are the most important factors for selection of the suitable technology. Sensors for safety applications, for example pellistors, and sensors in niche markets are in many cases still based on manual or semiautomated processing techniques. The ventilation control of cars [78] as the first mass market has strongly promoted the development of MEMS gas sensors [79–82] for decades. Processing of those sensors starts from silicon wafers, in which membranes are processed by various etching techniques to achieve thermal insulation [80]. The first so-called μ-hotplates have been processed by isotropic backside etching [79, 83]; in more recent developments, front side etching was employed yielding even smaller hotplates [81]. State-of-the-art μ-hotplates are based on anisotropic etching to reduce the chip size and cost. The integration of ASICs requires the combination of CMOS and MEMS processing steps [68, 84]. Due to the limited CMOS compatibility of gas sensor materials, especially the electrodes, which are often manufactured from gold or platinum, MEMS processing has to be applied after CMOS processing, which limits the technological spectrum. Alternative combinations of ASICs and sensors with multichip packages and stacking using through silicon vias (TSV) are also investigated [85].

Indoor air quality monitoring

11.3.2 Novel sensor materials and processes The continuous strive for low-energy consumption is demanding smaller and thinner hotplates and sensor layers. In classic preparation techniques, for example, using screen printing, the film thickness is in the micrometer range, contributing strongly to the thermal mass and even the thermal conductance of the hotplate. On the other hand, the functionality of gas sensors requires a nanogranular structure of the films with almost (but not quite) stoichiometric metal oxide. Most thin-film techniques, for example, sputtering, are not suitable for the deposition of those films, as they are aiming preferentially at dense films and tend to yield strongly nonstoichiometric oxides. An earlier approach to overcome these obstacles was the so-called rheotaxial growth and thermal oxidation (RGTO) approach. The preparation starts with a metallic tin layer. A thermal treatment leads to a formation of metallic droplets (dewetting). A subsequent oxidation process leads to the growth of these droplets forming small contact point and thus a very sensitive sensor [9]. However, RGTO films were never adapted for mass production, probably because their fragile quasi-two-dimensional grain network is susceptible to deterioration. Alternatively, methods with direct preparation of micro- and nanooxide grains like flame spray pyrolysis and pulsed laser deposition have been investigated. Both methods utilize the nucleation of oxide nanoparticles in the plasma phase. For flame spay pyrolysis (FSP), a metallic precursor and oxygen are injected into a hydrogen flame. This method has been investigated for various oxides [86] including tin oxide, indium oxide, and their mixed oxides [87, 88] as well as tungsten oxide [89]. Pulsed laser deposition (PLD) uses ablation of an oxide target with a high-power laser pulse. In contrast to sputtering, the plasma can be generated in low vacuum, allowing the formation of particles already in the plasma plume before hitting the target. Depending on the pressure in the deposition chamber, PLD is capable of depositing either dense or granular oxide films [90, 91]. At low pressures, no or only incomplete plasma nucleation occurs leading to dense films comparable with sputtered layers. With increasing pressure, particle nucleation is complete in the plasma, and even generation of particle clusters can be observed (Fig. 11.2). The formation of thin granular films for gas sensing using various oxides has been demonstrated [92–95] enabling a new generation of fast low-power gas sensors. 11.3.2.1 Multisignal generation and dynamic operation Most metal oxide gas sensor are not selective to certain reducing gases, which on one hand allows the detection of various gases, but on the other does not give sufficient information to discriminate, specify, and quantify gases easily. For this propose multiple, uncorrelated signals have to be recorded by the sensor system. Dynamic operation techniques have been originally developed to generate multisignals with a single-sensor device with the goal of gas discrimination. Dynamic operation and multisignal generation are often used as synonyms, but in our understanding, dynamic operation is always

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(A) WO3

(B) WO3

p(O2)=0.07 mbar

p(O2)=0.05 mbar

1 mm

SiO2

(C) WO3

p(O2)=0.2 mbar

SiO2

1 mm

SiO2

(F) SnO2

(E) SnO2

p(O2)=0.05 mbar

p(O2)=0.1 mbar

200 nm

SiO2

(D) WO3

p(O2)=0.1 mbar

1 mm

1 mm

SiO2

200 nm SiO2

Fig. 11.2 Scanning electron microscopy cross-sectional micrographs of the postannealed metal oxide layers produced by ps-PLD: WO3 layers deposited at p(O2) ¼ (A) 0.05, (B) 0.07, (C) 0.1, and (D) 0.2 mbar; SnO2 layers deposited at p(O2) ¼ (E) 0.1 and (F) 0.2 mbar [95].

implying a real change of the sensor state (e.g., the temperature or gas atmosphere). On the other hand, multisignal generation can also be achieved without dynamic operation at steady sensor state, for example, by using impedance spectroscopy or a static temperature gradient. By far, the most important dynamic operation mode is the modulation of temperature, often referred to as temperature-cycled operation (TCO). The first example of TCO was described and patented by Eicker already in 1973, using it to discriminate between methane and carbon monoxide to improve the safety monitoring of coal mines. The work was followed by many other groups [96–100] for many different applications. The relevance of TCO has further increased strongly with the introduction of micromachined sensors, which require less costly electronic circuits for the heater and enable faster temperature changes due to their low thermal time constant [101, 102]. Especially for those sensors, the TCO can be used to improve other sensor properties like stability [103, 104] and sensitivity [59, 105] as well. The reason for this is that within the TCO, the sensor is in transitory, nonequilibrium states that cannot be obtained at any

Indoor air quality monitoring

steady temperature. The equilibrium band-bending energy caused by surface coverage with oxygen is increasing with temperature; a rapid decrease in temperature can therefore cause a surface with strong excess of surface oxygen, which is obviously highly sensitive to reaction with reducing gases. A strong increase of the sensor response as high as a factor of 1000 has been reported [106]. With this method, the detection of benzene in the concentration range from 0.5 to 5 ppb has been reported in clean synthetic air with a quantification uncertainty of less than 100 ppt. However, background gases such as the ubiquitous hydrogen and carbon monoxide increase the uncertainty significantly [107]. For the robust detection and quantification of benzene at the very low threshold level of 1.5 ppb in the highly complex indoor air background, further improvement at the sensor and/or at the system level, for example, by integrating preconcentration devices, is needed.

11.4 Integrated sensor system with preconcentration Preconcentration by sampling on sorbents is an established method in gas detection, for example, in the IAQ analytical reference measurements. As already mentioned in Chapter 11.2.6, sampling is only adequate for a certain volatility range of VOCs, which can, by a specific sorbent, easily be adsorbed (at room temperature) and desorbed (at elevated temperature). This means that while sampling reduces the universality of the method on the one hand, on the other hand, it can provide better information on a specific group of target substances. Preconcentration is in general aiming at extracting the substance from a large volume (long sampling time) during the sampling phase and then injecting it into a small volume (short desorption time). The efficiency can be described by a preconcentration factor PF defined as the concentration at desorption divided by the concentration at adsorption. While the kinetic of the sampling is typically very specific for each experimental setup, the thermodynamic is much simpler and allows in many cases a good prediction of the described system. The thermodynamic behavior of diluted gases can be normally described by a Henry constant determining the proportionality between airborne and ad- or absorbed molecules. For a volume-based comparison of molecular concentration, this constant is often referred to as the partition coefficient pc: csorbent pc ¼ (11.2) cgas This means that in equilibrium, the concentration in the sorbent exceeds the concentration in air by the factor pc. Desorption can utilize the fact that the partition coefficient is strongly temperature dependant. The change of the coefficient can be described by the van’t Hoff equation [108], which is also used for the vapor pressure of liquids: Esub

pc ¼ p0c ∗e

=kT

(11.3)

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Obviously, the underlying model for the van’t Hoff equation makes a strong simplification for the adsorption/desorption equilibrium assuming adsorption sites with constant sublimation energy Esub and no limitation of the number of adsorption site. Despite this simplification, the equation provides a good estimate for the temperature-dependant adsorption of diluted VOCs [109, 110] and an estimation of the preconcentration factor PF, which can be obtained by a single preconcentration step:   4T EA ∗ k T T pc ðT1 Þ B 1∗ 2 (11.4) PF  ¼e pc ðT2 Þ with T1 and T2 being the adsorption and desorption temperature and 4T ¼ (T1  T2) the temperature difference. Obviously, high sorption energies are a prerequisite for high preconcentration factors. Novel materials, for example, metal organic frameworks (MOF), providing sites with high sorption energies even for higher volatile compounds [111] are a key for the improvement of preconcentration devices that can outperform classic materials like TENAX TA by orders of magnitude. It is important to note that this thermodynamic view only determines the upper limit of PF and that more complex analytical [112] or numerical [111] modeling should be used for the comprehensive design of preconcentration systems.

11.4.1 The SENSIndoor solution A low-cost solution for an integrated sensor and preconcentrator system in which the gas transport within the system is completely achieved by diffusion and therefore no active parts like valves or pumps are needed has recently been demonstrated. The drawback of diffusion-driven gas transport is that it only follows concentration gradients and that it is limited by the diffusivity of the gas. For VOCs in air diffusion constants are typically in the range of 0.05–0.1 cm2/s, correspondingly this approach is limited to devices with dimensions of up to a few millimeter. Thus, the integrated sensor preconcentrator system is based on micromachined hotplates for the preconcentrator and the sensor(s) as shown in Fig. 11.3. The total size of the system in Fig. 11.3 is 5 * 7 mm with a height of 1 mm. FEM simulations and measurements show a strong increase in the observed sensor signals for the detection of benzene and toluene [111] as well as representative TVOC mixtures [113] and a better suppression of permanent background gases like hydrogen [114].

11.5 IoT sensor solutions for IAQ In 2017 and 2018, several new integrated miniaturized gas sensor systems have been introduced by leading sensor manufacturers addressing applications of gas sensors in mobile phones, wearables, and Internet of Things (IoT) devices. In all cases, one of the primary applications addressed is monitoring of indoor air quality, with further

Indoor air quality monitoring

Fig. 11.3 Sensor preconcentrator microsystem demonstrator manufactured by SGX. The larger hotplate on the left is coated with a preconcentrator material (MIL-53, a MOF), the two smaller hotplates on the right are gas sensors with different sensitive layers. The system is pictured without lid. (Modified after A. Sch€ utze, T. Baur, M. Leidinger, W. Reimringer, R. Jung, T. Conrad, T. Sauerwald, Highly Sensitive and Selective VOC Sensor Systems Based on Semiconductor Gas Sensors: How to?, Environments 4 (2017) 20. https://doi.org/10.3390/environments4010020.)

applications also being named like personalized weather stations, kitchen range hood control, air cleaners, and breath alcohol monitoring, but also context awareness, for example, room change detection, fitness monitoring/well-being. Furthermore, these sensors would obviously also allow hazard warning, especially for high CO concentrations or gas leaks, but this is not claimed by the manufacturers due to legal ramifications if people are injured or killed because the sensors have missed an alarm. These sensors are sometimes dubbed “digital” gas sensors, because they include electronics for sensor operation, readout, and data evaluation in addition to a digital communication interface (I2C) even though they are smaller than most conventional gas sensors. The gas sensors are all resistive-type sensors based on metal oxides deposited on MEMS microhotplates, due to the unique combination of low cost, high sensitivity, and long lifetime that this sensor principle offers and that is proven in large volume applications for automotive cabin air quality [5]. Typical package sizes are 3*3*1 mm3, predominantly to address the high volume markets for mobile phones and wearables, with average power consumption in the range of approx. 10 mW. Some sensor systems include multiple gas sensors and other additional environmental sensor functions. For improved performance and reduced power consumption, all of these sensors make use of pulsed operation or temperature modulation. This is complemented by manufacturer specific algorithms for determining air quality values, often expressed as an equivalent CO2 concentration to be compatible with existing IAQ sensor solutions. These algorithms often include an automatic baseline correction (ABC) as also used in low-cost IR sensors to compensate sensor drift by setting the minimum output value over a certain period, for example, 1 week, to 400 ppm CO2, that is, to the current ambient background value.

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11.5.1 Bosch Sensortec BME680 This integrated sensor system, introduced in 2017, comprises temperature (T), relative humidity (RH), and ambient pressure (p) sensors in addition to the MEMS gas sensor [115]. Designated as an integrated environmental sensor, this microsystem extends the 3-in-1 sensor platform combining T, RH, and p sensors often found in wearables today with a gas sensor to allow air quality monitoring. The gas sensor not only provides an IAQ index value as output but can also be user programmed with individual temperature profiles and data evaluation. The sensor is today integrated in IoT devices like the Bosch Twinguard, combining a smoke detector with IAQ monitoring, Bosch air purifiers, and in the iBlades sleeve for selected smartphones. In addition, several reference designs and application boards are available for integration of the sensor in IoT devices. One example for this use is a suite of training modules for high school students to introduce them to chemical sensors and to pave the way for environmental studies performed by the students themselves [116].

11.5.2 Sensirion multipixel gas sensor SGP30 In 2018, Sensirion, well known for their digital temperature and humidity sensors, introduced the Multipixel technology. As the name implies, these sensors include four gas sensing layers integrated on a single sensor platform as shown in Fig. 11.4, which is unique. Furthermore, this sensor platform is based on CMOS technology, that is, sensor and electronics are monolithically integrated on a single chip. For IAQ monitoring, which is again the primary application, the sensor provides two output values, one TVOC value with a range of 0–60 ppm and an equivalent CO2 value with a range of 0%–6% [117]. For the CO2 equivalent value, the SGP30 makes use of a sensor with increased selectivity versus hydrogen to allow a differentiation of air quality based on human presence from other VOC sources, which are included in the TVOC output signal [68]. The readout electronics achieve a high dynamic range of eight orders of magnitude [68], which is

Fig. 11.4 Membrane heater of the Sensirion Multipixel gas sensor. The borders of the four sensor segments have been highlighted by dashed colored lines [68].

Indoor air quality monitoring

required for making full use of the potential offered by temperature-cycled operation [59, 118]. The manufacturer also claims a very high robustness against contamination/poisoning by siloxanes based on exposure tests in 250 ppm decamethylcyclopentasiloxane (D5) for up to 200 h resulting in long-term stability for typical indoor air applications [68].

11.5.3 AMS CCS811 The AMS CCS811 gas sensor was also introduced in 2018 [119]. Previously, Austria Microsystems (ams) had acquired both Cambridge CMOS Sensors and Applied Sensors Germany to be able to integrate various technologies including CMOS microhotplates and different gas sensing layers. Similar to the SGP30, the sensor provides two output values, equivalent CO2 and eTVOC, which are both calculated from a single MOS sensor using proprietary algorithms. Different operating modes allow control over the average power consumption with readout values provided every second down to every minute. An evaluation kit is available from ams [120] and several application boards for building IoT devices.

11.5.4 IDT ZMOD4410 Integrated device technologies (IDT) acquired the American sensor manufacturer Synkera Technologies Inc. in 2016 and introduced the ZMOD4410 TVOC and indoor air quality sensor platform in 2018 [121]. This platform includes a tiny MOS sensor chip (approx. 300*600 μm2) with an ASIC for dynamic sensor operation and readout [122]. In addition to an eCO2 value supported by automatic baseline correction, the sensor also provides an AQ level output based on recommendations of the German Umweltbundesamt (UBA) [34]. To assess the response of the gas sensor module to the levels described in the UBA study, the module has been tested using a TO-15 66 multicomponent TVOC standard defined by the EPA. Based on an alternative firmware, the sensor can also be used to detect gases indicating the presence of odors in kitchens and bathrooms to allow control of automatic exhaust fans and other air changing systems.

11.6 Conclusion and outlook The detection of indoor air quality is a very complex objective, which has not yet been fully solved neither with conventional analytical methods nor with sensor systems. Analytical methods are limited due to the high effort, which only allows few samples at selected buildings. Moreover, each analytical method only covers a selection of substances often excluding VVOC, SVOC, and OVOC. A complete picture of the personal exposure to hazardous compounds in indoor air is therefore not given, especially for substances or sources that are currently not in the focus of investigations. Gas sensors

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therefore have a huge potential to complement analytical measurements. They are inexpensive and allow continuous measurements in each building and even in each room, not only in a selected subset. Nanostructured MOS sensors with TCO achieve sensitivity down to the ppb range with selectivity for specific VOCs, for example, benzene, and even sub-ppb in clean air background. Numerous new commercial sensor types have been launched in recent years by various manufacturers, and new technologies for the improvement of the sensing layers, for example, the deposition of very thin granular films using FSP or PLD for faster and more sensitive sensors, have been studied intensively with great progress. While those sensors can already provide a good estimate for total VOC exposure, a robust measurement of the concentration of individual VOCs is still an open question due to the very low guideline values (e.g., 1.5 ppb for benzene) and the very complex matrix. Gas sensor systems with additional components, for example, for preconcentration, are a way to overcome current limitations. New nanomaterials for preconcentration like MOFs and innovative system approaches allow significant miniaturization and cost reduction for these devices. This technological progress opens the path for comprehensive monitoring of indoor air quality, which will enable an effective reduction of exposure to hazardous compounds and an improved understanding of their health effects.

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