Low-cost sensors for outdoor air quality monitoring

CHAPTER 12

Low-cost sensors for outdoor air quality monitoring Michele Penza Department for Sustainability, Division of Sustainable Materials, Laboratory of Functional Materials and Technologies for Sustainable Applications, ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Brindisi, Italy

12.1 Introduction Air pollution is a major problem in our modern world. Today, about 92% of the world’s population lives in regions where air pollutant levels are higher than WHO-specified limits [1]. Air pollution was labeled as the largest environmental risk by European Environment Agency (EEA), which estimates 467,000 deaths related to air pollution in the Europe in 2013 [2]. Almost one third of the Europe’s citizens are exposed to excessive concentrations of the airborne particulate matter. Exposure to PM2.5, NO2, and O3 lead to, respectively, 431,000, 75,000, and 17,000 premature deaths in Europe (2012 concentrations). The main pollutants are toxic gases such as nitrogen oxides, sulfur oxides, ozone, and aerosol/particulates. A variety of health effects have been reported [3]. Thus, the EU governmental bodies set legal pollution limits to mitigate environmental risks by the Ambient Air EU Directive 2008/50/EC [4]. In addition, air pollution is also responsible for global climate change and environmental problems such as acid rains, ozone depletion, and damage to crop [5, 6]. Usually, air quality is monitored by measuring concentrations of various pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), volatile organic compounds (VOCs), and particulate matter (PM) at stationary sites using accurate and expensive analyzers [7–9]. Monitoring sites in the Europe are setup by Ambient Air Directive 2008/50/EC, which defines the minimum number of fixed air quality monitoring stations (AQMS) for each pollutant depending on air-pollution level, population density, and coverage area. Usually, one official AQMS covers in average about 100,000 people in the cities of the developed countries. Sometimes, this distribution rate is insufficient to achieve accurate information on the spatial distribution of air pollution or identify pollution sources. However, air-pollution dispersion models can be used to tackle this issue, but their accuracy is rather limited [10, 11]. Reliable and certified air quality monitoring instrumentation is available for reference measurements, but the initial and maintenance costs limit their use to a few

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specific locations only. A low-cost alternative for air quality monitoring would be highly desirable. Recent advancements in sensor technologies have realized to the emergence of a new paradigm for air-pollution monitoring [12–14]. Electrochemical sensors have been identified as one of the most promising sensor technologies to measure inorganic gases in air quality monitoring at ppb levels that are 2 or 3 orders of magnitude lower than for safety applications at ppm levels. Also, optical counters have been used as promising low-cost sensors for particulate matter in real scenario. A review by Lewis et al. [15] from World Meteorological Organization (2018) reported on low-cost sensors for the measurement of the atmospheric composition giving an overview of future applications by outlining advantages and current limits in air quality monitoring. Gerboles team reported on low-cost commercial sensors for indicative monitoring of ambient gases [16] and field calibration of a cluster of low-cost O3 and NO2 [17] sensors and NO, CO, and CO2 [18] sensors for air quality monitoring. Borrego et al. [19, 20] focused on assessment of air quality microsensors versus reference methods by a realworld joint exercise using a hundred of low-cost sensors operated side to side to reference analyzers, installed in a mobile air quality laboratory, and working for a two-week campaign at Aveiro city in Portugal. The COST Action TD1105 (EuNetAir) [21] focused on European Network devoted to new sensing technologies for air-pollution control and environmental sustainability [22–25]. This concerted action looks at a new detection paradigm based on low-cost sensing technologies for air quality control (AQC) and set up an interdisciplinary top-level coordinated network to define innovative approaches in sensor nanomaterials, gas sensors and devices, wireless sensor systems, distributed computing, methods, models, standards, and protocols for environmental sustainability within the European Research Area (ERA). The EuNetAir network includes >200 experts working in at least 120 research teams from 38 countries. The objective of the action is to create a cooperative network to explore new sensing technologies for low-cost air-pollution control through field studies and laboratory experiments to transfer the results into preventive real-time control practices and global sustainability for monitoring climate changes and outdoor/ indoor energy efficiency. The ambition is to establish European leadership in the green economy and competitiveness of the European industry. Outstanding studies showed that indicative levels of air pollutants can be detected using reliable and high-density sensor networks for air quality monitoring in various European cities such as Cambridge (the United Kingdom) [26] and around Heathrow airport [27] in London, Oslo (Norway) [28, 29], Zurich (Switzerland) [30–32], Antwerp (Belgium) [33–35], and Bari (Italy) [36, 37]. In the United States, the Environmental Protection Agency (EPA) conducted parallel studies on air quality monitoring using low-cost sensor networks and their related performance assessment such as the CAIRSENSE project [38] for suburban environment monitoring including NO2, O3, CO, SO2, and PM2.5. Furthermore, the BErkeley Atmospheric CO2 Observation Network (BEACO2N) is an ongoing greenhouse gas

Low-cost sensors for outdoor air quality monitoring

and air quality monitoring campaign, managed by R.C. Cohen team, operating in the San Francisco Bay Area since late 2012. The current network is composed of about 50 nodes stationed on top of schools and museums at approximate 2-km intervals consisting of NO2, NO, O3, CO, SO2, CO2 PM10, and PM2.5 sensors [39–41]. In Asia, Ning and coworkers [42] developed a next-generation air sensor network tested during the Hong Kong Green Marathon 2015 including NO2, O3, CO, and PM2.5 monitoring. The field evaluation in an urban roadside environment in comparison with designated monitors showed good agreement with measurement error within 5% of the pollutant concentration. Real-time air-pollution concentration data were wirelessly transmitted, and the Air Quality Health Index (AQHI) for the Green Marathon was calculated and then broadcast to the public on an hourly basis. Also, Gao et al. [43] deployed a distributed network of low-cost sensors to measure spatiotemporal variations of PM2.5 in Xi’an (China). Correlation between sensors and reference monitors was high (R2 ¼ 0.86–0.89). The objective of this chapter is to review the literature dealing with air quality monitoring using networked low-cost sensors deployed for real applications. A brief survey of the active materials used for gas sensors will be given with emphasis to the metal oxides, carbon materials, and hybrid materials exhibiting promising sensing properties for air quality monitoring.

12.1.1 Status of the low-cost air sensor technologies Air pollution sensors are designed for the measurements of the atmospheric composition at ambient concentrations of the different types of analytes: • Toxic gases including NOx (NO2, NO), O3, CO, and SO2. • Volatile organic compounds (VOCs) including benzene, toluene, ethylbenzene, and xylene. • Greenhouse gases (GHG) including CO2, CH4, and N2O. • Airborne particulate matter (PM) in various classes such as PM10, PM2.5, PM1.0, and ultrafine particle (UFP). Air sensors can be classified into two main categories: (1) sensors measuring concentration of gas and vapors and (2) sensors measuring mass concentration of particulate matter. Usually, the sensor systems consist of a few basic elements including some functionalities: (a) the sensor element consisting of active material absorbing gaseous species dispersed in the ambient air, (b) the transducer that converts the sensor response into the output electrical signal, (c) a data logger with capability linked to a communication device (transmitter or cell phone), and (d) a power source (battery or energy harvester). Most commercially available gas sensors are based on two main principles of transduction: • Electrochemical cells and metal oxides: chemical interactions between sensing material (metal oxides, catalysts, and conducting polymers) and gases such as nitrogen dioxide

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(NO2), ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), and individual and/or total VOCs. • Optical sensors: absorption of visible light (e.g., O3) or infrared wavelengths (e.g., CO2) or chemiluminescence (NO2). Furthermore, particulate matter (PM) mass can be measured by frequency changes of an oscillating device or by light scattering related to size and mass concentration of the particles to be measured. Air quality control (AQC) is currently realized by continuous and discontinuous methods to be carried out with automated, semiautomated, and/or manual devices (e.g., chemical monitors, sampling, and analyzers) to check calibration procedures and standards. Such analysis equipment is very expensive, and therefore, no dense network of air monitoring nodes could be used if reliable devices at low cost are not employed. To do this, new sensing technologies such as cost-effective microsensors based on gas-sensitive nanomaterials could be used for monitoring of ambient air, rural or remote sites, and traffic on road network in smart cities. They offer the opportunities for real-time mapping of air pollution by connecting several sensors through wireless networks or GSM. This is critical for validation of dispersion models of air pollutants and evaluation of exposure of the population. A Roadmap [44] on Next-Generation Air Monitoring (NGAM) by US EPA was drafted on low-cost sensor technologies that may be used in various applications (Fig. 12.1) in the air quality management with different required data quality Required data quality Higher Ambient air monitoring network

Legal compliance

Community based monitoring

Supplement air monitoring network

Screening for hot spots Science education Qualitative personal monitoring

Lower

Fig. 12.1 Roadmap for Next-Generation Air Monitoring (NGAM) by US EPA with different required data quality depending on specific application. (Draft Roadmap for Next Generation Air Monitoring (NGAM) edited by US EPA, 8 March 2013. https://www.epa.gov/sites/production/files/2014-09/documents/ roadmap-20130308.pdf. Courtesy by US EPA.)

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including supplementing routine ambient air monitoring networks, expanding the participating sensing, monitoring personal exposure, and enhancing source compliance monitoring.

12.1.2 Ambient Air EU Directive The European Directive 2008/50/EC [4] on Ambient Air Quality and Cleaner Air for Europe was setup to define consolidated limits of the air pollutants to be monitored to protect human health and mitigate the negative effects on ecosystems. Current EU legislation requires informing the public on air quality (AQ), assessing air pollutant concentrations throughout the whole territory of the member states, and indicating exceedances of the limit and target values, forecasting potential exceedances and assessing possible emergency measures to abate exceedances. For this regulatory purpose, effective modeling tools can be used in parallel with accurate measurements of the air pollution. Fixed measurements and indicative measurements have been defined by EU Air Quality Directive 2008/50/EC [4] to address quality assurance (QA) and quality control (QC) with different data quality objectives (DQO). Fixed measurements means measurements taken at fixed sites to determine the levels in accordance with the relevant data quality objectives (DQO). The fixed measurements are mandatory in zones and agglomerations where the upper assessment thresholds are exceeded. On the contrary, the indicative measurements means measurements that meet data quality objectives that are less strict than those required for fixed measurements. The high-accuracy DQO for all air pollutants of the fixed measurements can be fulfilled by high-cost standard chemical analyzers only, while the less strict DQO for some air pollutants of the indicative measurements could be fulfilled by accurate and calibrated low-cost sensors. The use of appropriate accurate low-cost sensors for the indicative measurements should allow for a reduction of at least 50% of the required minimum number of high-cost fixed air-sampling points. This is a benefit of the new challenging sensors distributed in outdoor areas for air quality control. The DQO is a measure of the acceptable uncertainty for fixed and indicative measurements. The allowed uncertainties by EU Directive are reported in the Table 12.1.

12.1.3 Air pollution limits The air pollution monitored by means of solid-state chemical sensors at low cost and good reliability is surely a primary need for the public health and the preservation of the environment and ecosystems. The main air pollutants such as NOx, (NO and NO2), O3, CO, SO2, NH3, volatile organic compounds (VOCs) (especially benzene, toluene, ethylbenzene, xylene [BTEX]), and greenhouse gases (CO2, CH4, and N2O)

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Table 12.1 Allowed uncertainties by EU Directive 2008/50/EC for the fixed measurements and indicative measurements related to air pollutants to be monitored Max uncertainty requested by EU Directive NO2/NO/ NOx, SO2, CO

Benzene

O3

PM10/ PM2.5

Fixed measurements (high accuracy)

15%

25%

15%

30%

Indicative measurements (low accuracy)

25%

30%

30%

50%

Devices for measurement

Analyzers: fluorescence, chemiluminescence, gas chromotography, UV photometry, optical absorption, gravimetry Low-cost sensors (diffusive samplers)

are classified as toxic and/or hazardous and are very dangerous, especially above the values of threshold, for man and all living beings. The threshold values declared by laws on ambient air quality (EU Directive 2008/50/EC) and indoor air quality at workplaces (American Conference of Governmental Industrial Hygienists (ACGIH)), including the technical characteristics, mainly the concentration range, requested to the solid-state chemical sensors are reported in the Table 12.2 [4, 45, 46].

12.2 Materials for air quality sensors Chemical sensors can be divided into various categories according to the type of active material used: First, a broad range of inorganic materials or polycrystalline materials have been used in gas sensors. They include semiconducting metal oxides, carbon nanomaterials, zeolites, perovskites, and metallic catalysts. Generally, these materials operate at elevated temperatures (200–400°C) to adsorb gaseous molecules. This class of sensing materials includes the nanostructured materials. Second, organic materials and polymers have been largely studied in gas sensor applications, mainly working at room temperature. Third, there are composite materials including filler-matrix structures, hybrid materials based on organic/inorganic components, mixed materials, and heterostructures working at or close to room temperature as reversible materials. Finally, there are biologically derived materials such as proteins, enzymes, and antibodies. They are used for selective biosensing, but their stability is still open issue. A challenging demand for air sensor materials is the limit of detection down to ppb or sub-ppm level. Here, a brief on key features of the most investigated materials for chemical sensing and air-pollution monitoring is reviewed.

Table 12.2 Threshold limit values (TLVs) of the ambient air pollution (EU Directive 2008/50/EC) and indoor pollution in the workplaces (ACGIH) to be measured by solid-state chemical sensors SO2

CO

CO2

O3

BTXa

Technical requests of the solid-state sensors

Air pollution (0.01–0.3 ppm)

Air pollution (0–2 ppm)

Air pollution (0.1–10 ppm)

Air pollution (200–400 ppm)

Air pollution (0–0.5 ppm)

Ambient standard of atmospheric pollution

0.04–0.06 ppm (daily average)

<0.04 ppm (daily average)

Level of attentionb

NO2: 200 μg/m3 (NO2: 100 ppb) NO2: 400 μg/m3 (NO2: 200 ppb) NO2: 3 ppm (NO2: 5.6 mg/m3) NO: 25 ppm (NO: 31 mg/m3)

SO2: 125 μg/ m3 SO2: 250 μg/ m3 SO2: 2 ppm (SO2: 5.2 mg/m3)

Air pollution B: 0.1–5 ppb T: 0.1–5 ppm X: 0.1–10 ppm B: < 2 ppb T: < 1 ppm X: < 1 ppm (daily average)

Level of alarmc TLVd

<0.06 ppm (hour average) CO: 15 μg/m3

O3: 180 μg/m3

CO: 30 μg/m3

O3: 360 μg/m3

CO: 25 ppm (CO: 29 mg/m3)

CO2: 5000 ppm (CO2: 9000 mg/m3)

O3: 0.2 ppm (O3: 0.4 mg/m3)

B: 0.5 ppm (B: 1.6 mg/m3) T: 50 ppm (T: 188 mg/m3) X: 100 ppm (X: 434 mg/m3)

The concentration is reported in part per million (ppm) or part per billion (ppb) and/or equivalently in mg/m3 or μg/m3. a BTX (B, benzene; T, toluene; X, xylene). b Level of attention. For level of attention, it is considered as the concentration of an atmospheric pollutant agent that determines the state of attention. c Level of alarm. For level of alarm, it is considered as the concentration of an atmospheric pollutant agent that determines the state of alarm. d Threshold limit value (TLV). TLV is defined as the average concentration-time weighted, over a working day conventional of 8 h and over 40 h weekly working, to that it is programmed that all workers can be repeatedly exposed, day by day, without negative effects. Definition of TLV by American Conference of Governmental Industrial Hygienists (ACGIH).

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NOx

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12.2.1 Metal oxides Gas sensors based on semiconducting metal oxides have been engineered >50 years ago [47]. These sensors normally consist of ceramics [48], pellets [49], thick films [50], thin films [51], and nanostructures [52]. A great variety of materials has been studied with the n-type semiconducting tin dioxide (SnO2) as the most important material largely used in commercial gas sensors manufactured by Figaro Engineering Inc. (Japan) and FIS Inc. (Japan). SnO2 has been modified by small amounts of catalytic metal additives such as platinum, palladium, gold, or other noble metals for enhanced sensitivity. Other important n-type semiconducting metal oxides investigated in gas sensors are zinc oxide (ZnO), titanium oxide (TiO2), indium oxide (In2O3), tungsten trioxide (WO3), gallium oxide (Ga2O3), iron oxide (Fe2O3), vanadium oxide (V2O5), etc. Moreover, p-type semiconducting metal oxides have been studied such as copper oxide (CuO), nickel oxide (NiO), cobalt oxide (Co3O4), chromium oxide (Cr2O3), manganese oxide (Mn3O4), and strontium titanate (SrTi1 xFexO3). A lot of research and development has been done to improve the sensor performance in terms of sensitivity, selectivity, and stability. The most toxic, combustible, and hazardous gases such as CO, CO2, NO2, SO2, NH3, H2S, and C2H5OH have been considered as target gases. Moseley [53] reported on the progress in the development of semiconducting metal oxide gas sensors focusing on the exploration of the response mechanisms, the selection of the most appropriate oxide compositions, the fabrication of two-phase heterostructures, the addition of metallic catalyst particles and the optimization of the manner in which the materials are presented to the target gas, the role of the structure in the active materials, and the importance of the nanostructure of the sensing elements. A comparison of the gas-sensing properties has been drawn considering the type of metal oxide, catalyst, structure, phase, composition, and nanostructure. Emerging applications of the metal oxide gas sensors have been explored including indoor air quality, street-level air quality, and exhaled breath sensing. Barsan et al. [54, 55] reported on the fundamental in the design of the nanostructured tin oxide for gas detection providing a model of the electrical conduction for the sensing mechanisms. Dey [56] reported a detailed study of semiconductor metal oxide gas sensors providing a comparison on the sensor performance. Different parameters (mainly sensitivity, selectivity, and stability) of the metal oxide gas sensors have been discussed. This paper gives an insight about the dopant- or/and impurity-induced variations in the active materials used for gas sensing. It is concluded that dopants enhance the properties of the metal oxides for gas-sensing applications by changing their microstructure and morphology, activation energy, electronic structure, or band gap. In some cases, dopants create defects in metal oxides by generating oxygen vacancies or by forming solid solutions. These defects enhance the gas-sensing properties. Different nanostructures (e.g., nanowires, nanotubes, heterojunctions, and nanopowders) have also been reviewed.

Low-cost sensors for outdoor air quality monitoring

Comini et al. [57] reported on metal oxide heterostructures for gas sensors. The combination of different metal oxides to form heterostructures (e.g., SnO2-ZnO, SnO2-CuO, WO3-SnO2, ZnO-CuO, and SnO2-CeO2) further improves the selectivity and/or other important sensing parameters. A very large number of different morphologies and structures (e.g., mixed compounds, bilayers, core-shells, branch-like heterostructures, decorated heterostructures, and longitudinal heterostructures) have been proposed, each one exhibiting peculiar sensing properties toward specific chemical compounds (e.g., NO2, NH3, H2S, acetone, methanol, ethanol, toluene, formaldehyde, CO2, H2, and LPG). An emerging strategy to lower the sensor temperature in the metal oxides is the self-heating approach, without the need of external heaters. Prades et al. [58–60] reported on single metal oxide nanowire sensors integrated into rigid and/or flexible substrates. Due to the Joule effect induced by current applied in conductometric measurements, nanowires heat up to elevated temperatures (100–400°C) and can eventually melt. This self-heating of the nanowires can be used to achieve desired sensor performance without external heaters and very low power consumption in the microwatt range. A proof of concept has been demonstrated to sense NO2 in the range 1–10 ppm. Kim et al. [61] and Kock et al. [62] reported on gas sensors prepared using p-type metal oxide semiconductors such as NiO, CuO, Cr2O3, Co3O4, and Mn3O4. These gas sensors were used to detect C2H5OH (ethanol), HCHO (formaldehyde), CO, NH3, H2, H2S, (CH3)3N (trimethylamine), C6H4(CH3)2 (xylene), and C6H5(CH3) (toluene), whose concentrations were in the range 1–1000 ppm. The p-type metal oxide semiconductors with distinctive surface reactivity and oxygen adsorption are advantageous for enhancing gas selectivity, decreasing the humidity dependence of sensor signals to negligible levels, improving recovery speed, and usage in sensor arrays. Binions et al. [63] reported on metal oxide semiconductor gas sensors for environmental monitoring. They have been used extensively to measure and monitor trace amounts of environmentally important gases such as carbon monoxide (CO) and nitrogen dioxide (NO2). An overview of important contributions are discussed for the use of metal oxide sensors for the detection of a variety of gases such as CO, NOx, NH3, and the challenging case of CO2. Finally, a description of recent advances is presented including the use of selective zeolites layers and new perovskite-type materials. Also titania [64] powders with Ta and Nb catalysts have been proposed for enhanced CO gas sensing, and palladium-doped tungsten trioxide [65] for hydrogen sensors has been studied. Fig. 12.2 shows some examples of metal oxides for gas sensing.

12.2.2 Carbon nanomaterials Engineered materials at nanoscale level including both one-dimensional nanostructures (nanotubes, nanowires, nanobelts, nanorods, and nanoobjects) and two-dimensional

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Fig. 12.2 Field-emission scanning electron microscopy images of (A) TiO2-SnO2 core-shell nanofibers (NFs), (B) SnO2 NFs, (C) TiO2 core-shell NFs, (D) CuO nanorods, (E) CuO-MnO2 nanocomposite intercalated sheets, (F) hollow hierarchical SnO2-ZnO composite, (G) 3% In2O3-SnO2 nanostructures, (H) flowerlike ZnO decorated with NiO, (I,J) nanobelts of ZnO decorated with In2O3, (K) ZnO nanorods decorated by PdO. (M) Dynamic C2H5OH-sensing transient for (L) NiO-functionalized SnO2 hollow spheres measured at 450°C, according to Ref. [61]. ((A–K) Reprinted with permission from D. Zappa, et al., Metal oxide -based heterostructures for gas sensors: a review, Anal. Chim. Acta 1039 (2018) 1–23.)

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monoatomic nanomaterials (e.g., graphene) have strongly emerged for gas-sensing applications. They have a great potential for a promising development of gas sensors, but their use in commercial devices is still a challenge. Gas sensors based on active nanomaterials exhibit high performance at laboratory level due to a mix of excellent properties such as greater gas adsorption capacity caused by a high surface-to-volume ratio, high structural and thermal stability, high modulation of electrical charge upon gas exposure, tuning of the sensing properties by surface modifications, and tailoring of composition and size of the building blocks with the advantages in terms of low power consumption and miniaturization. Unfortunately, the real-world application of chemical sensors integrating active nanomaterials is still a challenge, due to low performance in terms of selectivity, long-term stability, and cross sensitivities to many other interferences (temperature, relative humidity, and counterpart gases). Recent development [66–68] revealed that carbon nanomaterials such as nanocarbons, carbon nanotubes (CNTs), graphene nanosheets, carbon nanofibers, and carbon black exhibit strong high-performance gas sensing. The electrical properties of the carbon nanotubes (CNTs) are extremely sensitive to charge transfer and chemical doping effects by various gas molecules. When electronwithdrawing molecules (e.g., NO2, O3, and O2) or electron-donating molecules (e.g., CO, H2S, NH3, SO2, H2, CO2, and C2H5OH) interact with the p-type semiconducting CNTs, they will change the density of the major charge carriers (i.e., holes) in the nanotube, changing the electrical conductance. This behavior is the basis for sensing applications of CNTs as gas sensors [69–75]. Graphene is a two-dimensional monolayer of sp2-hybridized carbon atoms densely packed in a honeycomb crystal lattice. This material has already shown promise as ultrasensitive gas detector at ppb detection level. Single-layer graphene sensors have every atom at the surface and demonstrate sensitivity down to single molecular level [76]. Gas sensors operating in the part-per-billion (ppb) range are required for environmental monitoring of nitrogen dioxide (NO2). Currently, there is a lack of cheap sensors, which can operate in this concentration range of environmental interest. Several outstanding studies have been proposed using graphene and related modified material for the detection of nitrogen dioxide (NO2) [77], ammonia (NH3) and NO2 [78], volatile organic compounds (VOCs) [79], and relative humidity [80], using flexible substrates [81] and traditional transducers such as chemoresistors, field-effect transistors, and surface acoustic waves. A detailed study on gas sensing using graphene and related materials has been realized by S.S. Varghese and coworkers [82]. Fig. 12.3 shows some examples of carbon nanomaterials (e.g., CNTs and graphene) for gas sensing.

12.2.3 Conducting polymers The conducting polymers (CP)-based gas sensors exhibit responses to a broad range of organic vapors and gases (NH3 and NO2) changing the magnitude depending on target

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Fig. 12.3 Scanning electron microscopy images of (A) single-walled carbon nanotube (SWCNT) networked films; (B) horizontally aligned CNTs films; (C,D) vertically aligned CNTs films at different magnification. (E) I-V curves of the CNT-SnO2 gas sensor; (F) response of the CNT-SnO2 devices; (G) response of the devices to different concentrations of NO2 gas, at 150°C; (H) response-versus-concentration. (I) Scheme of a graphene-based NO2 gas sensor; (J) response of a graphene-based sensor upon exposure of 100 ppm NO2, at room temperature. (K) Graphene sensor chip on a holder, (L) prototype of a portable device based on graphene sensor chip for NO2 monitoring, (M) NO2 concentrations measured by graphene sensor versus true concentration measured by a gas analyzer. Black triangles correspond to the result of the first measurement cycle, white triangles correspond to the second measurement cycle, and the solid lines are linear approximation. (Reprinted with permission from (A–H) Z. Xiao, et al., Recent development in nanocarbon materials for gas sensor applications, Sensors Actuators B, 274 (2018) 235–267. (I, J) S.S. Varghese, et al., Recent advances in graphene-based gas sensors, Sensors Actuators B, 218 (2015) 160–183. (K–M) S. Novikov, et al., Graphene based sensor for environmental monitoring of NO2, Sensors Actuators B 236 (2016) 1054–1060.)

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molecules under test. They operate at or close to room temperature with low power consumption. After 40 years of research in the field, the fundamental nature of the charge propagation is generally understood; in other terms, the transport of electrons can be assumed to occur via an electron exchange reaction (electron hopping) between neighboring redox sites in redox polymers and by the movement of delocalized electrons through conjugated systems in the case of so-called intrinsically conducting polymers (e.g., polyaniline, polypyrrole, polythiophene, polyphenylene, and PEDOT). Detailed reviews [83, 84] on CP properties give an overview of the gas-sensing applications. Polymers for gas and odor sensing have been reviewed by Persaud [85]. The doping generates charge carriers in the polymeric chain through chemical modification of the structure and involves charge exchange between the polymer and the dopant species. Park et al. [86] reported on chemoelectrical gas sensors based on hybrid conducting polymers for sensing of ammonia at room temperature. Tang et al. [87] reported on a conductive polymer nanowire gas sensor fabricated by nanoscale soft lithography for sensing of ammonia and nitrogen dioxide. The nanowire chemiresistive gas sensor is demonstrated for NH3 and NO2 room-temperature detection and shows a limit of detection at ppb level, which is compatible with nanoscale PEDOT:PSS (poly(3,4-ethylene-dioxythiophene)-poly(styrene-sulfonate)) gas sensors fabricated with the conventional lithography technique. Ly et al. [88] reported on a highly sensitive ammonia sensor for diagnostic purpose using reduced graphene oxide (rGO) and conductive polymer. This sensor exhibits high reproducibility, high linearity of concentration dependency, and a very low detection limit (0.2 ppm) of ammonia both in N2 and exhaled air environments, at room temperature.

12.2.4 Hybrid materials The hybrid materials based on mixed metal oxides, composites of different materials, p-n heterostructures, and other complex structures have been largely studied for gas sensing. Heterostructures may improve the sensor performance by catalytic activity, increased adsorption, and creating a charge carrier depletion layer that produces a larger modulation in the electrical resistance [89]. Yan et al. [90] reported on sensing performance of the α-Fe2O3/SnO2 nanofiber heterostructures for conductometric ethanol (C2H5OH) detection. A remarkable improvement of the sensing performance can be attributed to the synergetic effects of the component phases. Miller et al. [91] reported on nanoscale metal oxide-based heterojunctions for gas sensing. Various studies have shown that incorporating two or more metal oxides to form a heterojunction interface can have beneficial effects on the gas sensor performance, especially for the selectivity. Mechanisms explored include p-n and n-p potential barrier manipulation, n-p-n response type inversions, spillover effects, synergistic catalytic behavior, and microstructure enhancement. Lee [92] reported on hierarchical and hollow oxide nanostructures as very promising

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gas sensor materials due to their high surface area and well-aligned nanoporous structures with agglomerated configurations. Furthermore, the employment of the n-p heterojunctions is among the most popular strategies to increase the performance of the gas sensors. In fact, a systematic study of (n)xSnO2-(p) (1x) Co3O4 composite nanofibers (NFs) for gas-sensing applications has been proposed by Kim et al. [93]. Shao et al. [94] reported on heterostructured p-CuO (nanoparticle)/n-SnO2 (nanowire) devices for selective H2S detection. Penza et al. reported on Au and Pd nanoparticles deposited on multiwalled carbon nanotubes for NO2 and other gaseous pollutants sensing [95], and also Pd-modified ZnO nanorods were proposed as active sensing layers in chemiresistive gas sensors for hydrocarbon gas detection (e.g., CH4, C3H8, and C4H10) [96].

12.2.5 Comparison of material gas-sensing properties The key performance of the sensor materials studied for chemical sensing and air quality monitoring has been depicted in the Table 12.3. Advantages and disadvantages of the different active materials (e.g., metal oxides, carbon nanomaterials, conducting polymers, and hybrid materials) used for gas sensors are reported in terms of detection limit, operating temperature, concentration range, response, and recovery time. The target gases for air quality monitoring are CO, NO2, NO, O3, SO2, H2S, and volatile organic compounds (VOCs) including benzene, toluene, ethylbenzene, xylene, and further greenhouse gases (CO2, CH4, and N2O). The detection limit of environmental interest is in the range of a few ppm for CO (< 10 ppm), a few hundreds of ppm for CO2 (< 500 ppm), and a few hundreds of ppb for other toxic gases (NO2, NO, O3, SO2, and H2S) below 100–200 ppb depending on target gas to be detected and regulatory items. These limits in the useful concentration range should be addressed with low power consumption at low operating temperature, ideally at room temperature. Sometimes, this has been achieved by carbon nanomaterials (nanotubes and graphene) and conducting polymers but not by metal oxides working at elevated temperatures. Other disadvantages for metal oxides are the dependence on ambient interferences (e.g., temperature, humidity, and counterpart gases), low selectivity, and poor stability. To improve selectivity, heterostructures have been studied with success at laboratory level but poor verification under field test. Moreover, the carbon nanomaterials are not used in the air quality sensors yet due to low reliable performance in the real world for long-term operation. At the current stage, the most promising active materials used for air quality sensors are electrochemical solid-state materials for gas detection in real scenario and optical particle counters for particulate matter detection.

12.3 Air quality sensor parameters The air quality monitoring by low-cost sensors is based on different building blocks integrated with increasing complexity from sensor element, sensor module, multiple sensor

Low-cost sensors for outdoor air quality monitoring

Table 12.3 Comparison of the gas-sensing properties by metal oxides, carbon nanomaterials, and hybrid composites of interest for air quality monitoring and chemical sensing Sensor material

Transducer

Target gas

TiO2:Au

Chemoresistor

CO

(Hollow nanosheets) SnO2:Au

Chemoresistor

CO

WO3:Ag

Chemoresistor

NO2

(Hollow nanotubes) WO3

Chemoresistor

NO

SnO2/rGO:Ag

Chemoresistor

NO2

(Sol-gel nanoparticles) TiO2

Chemoresistor

NO2

(Sol-gel nanoparticles) In2O3

Chemoresistor

NO2

(Sol-gel nanoparticles) SnO2: Pt (Core-shell nanorods) CuO:W

Chemoresistor

CO

Chemoresistor

NO2

(Core-shell nanowires) TiO2:W

Chemoresistor

NO2

Zeolite-modified WO3

Chemoresistor

NO2

MWCNTs:Pt or MWCNTs:Pd

Chemoresistor

NO2

Key performance

References

DL: < 10 ppm CR: 1–10 ppm OT: 325°C DL: 1 ppm CR: 0–5 ppm OT: 220°C DL: 0.1 ppm CR: 0–1 ppm OT: 75°C DL: 0.1 ppm CR: 0–1 ppm OT: 350°C DL: 1 ppm CR: 0–5 ppm OT: room temperature DL: 0.5 ppm CR: 0.5–4 ppm OT: 400–500°C RH ¼ 30% DL: 0.5 ppm CR: 0–1 ppm OT: 130°C DL: <10 ppm CR: 8–50 ppm OT: 350°C DL: 1 ppm CR: 0–10 ppm OT: 150°C DL: 5 ppm CR: 0–10 ppm OT: room temperature DL: < 100 ppb CR: 0–500 ppb OT: 350°C DL: < 100 ppb CR: 0–1 ppm RST: < 5 min RCT: < 10 min OT: 200°C

Review Moseley [53] Review Moseley [53] Review Moseley [53] Review Moseley [53] Review Moseley [53]

Review Dey [56]

Review Dey [56] Review Dey [56] Review Zappa et al. [57] Review Zappa et al. [57]

Review Fine et al. [63] Penza et al. [71]

Continued

249

Table 12.3 Comparison of the gas-sensing properties by metal oxides, carbon nanomaterials, and hybrid composites of interest for air quality monitoring and chemical sensing—cont’d Sensor material

Transducer

Target gas

MWCNTs:Au

Chemoresistor

NO2

Plasma-treated Rh, Pd, Au, Ni-decorated MWCNTs

Chemoresistor

Benzene

Graphene

Chemoresistor

NO2

Graphene

Chemoresistor

NO2

Graphene-modified V2 O 5

Chemoresistor

NH3

Iron oxide nanoparticlemodified graphene

Chemoresistor

VOCs (benzene)

Conducting polymers

Chemoresistor

NH3

Key performance

References

DL: < 100 ppb CR: 0–1 ppm RST: < 4 min RCT: < 5 min OT: 20–250°C DL: 50 ppb CR: 50–500 ppb OT: room temperature DL: < 100 ppb CR: 100–1000 ppb RST: < 30 s RCT: <1 min (UV light) OT: room temperature DL: < 5 ppb CR: 1–50 ppb RST: < 1 min RCT: < 2 min OT: room temperature DL: 1 ppm CR: 1–100 ppm RST: < 1 min RCT: < 10 min (UV) OT: room temperature DL: < 10 ppb CR: 1 ppb–5 ppm RST: < 1 min OT: 150°C DL: 1 ppm CR: 1–100 ppm RST: < 1 min RCT: < 10 min OT: room temperature

Penza et al. [72]

Leghrib et al. [73]

Review Singh et al. [67]

Novikov et al. [76]

Kodu et al. [78]

Rodner et al. [79]

Review Inzelt et al. [84]

Low-cost sensors for outdoor air quality monitoring

Table 12.3 Comparison of the gas-sensing properties by metal oxides, carbon nanomaterials, and hybrid composites of interest for air quality monitoring and chemical sensing—cont’d Target gas

Sensor material

Transducer

Conducting polymer nanowire (PEDOT: PSS)

Chemoresistor

NO2, NH3

Metal oxide heterojunctions

Chemoresistor

NO2, CO, H2S, NH3, ethanol

Key performance

References

DL: NH3 (< 0.8 ppm) DL: NO2 (< 25 ppb) CR: NH3 (0.8–4 ppm) CR: NO2 (20–200 ppb) RST: < 1 ppm RCT: < 5 min OT: room temperature DL: a few ppm CR: 1–100 ppm RST: < 5 min RCT: < 10 min OT: 20–250°C

Tang et al. [87]

Review Miller et al. [91]

DL, detection limit; S, sensitivity; CR, concentration range; RST, response time; RCT, recovery time; OT, operating temperature.

node, and finally networked sensor nodes managed by a virtual private network deployed in urban hotspots. A typical scheme is reported in Fig. 12.4. Here, we report on definition of the sensor parameters, the key performance indicators for the sensor assessment, and finally the metrics used for comparing the sensor data and reference measurements in terms of sensor-versus-analyzer to evaluate data quality and related uncertainty.

12.3.1 Sensor parameters for chemical sensing The solid-state sensors are essentially constituted by a chemically sensitive interface (sensitive material) and a transducer able to convert a chemical input (gas concentration and ion concentration) and/or physical input (temperature, pressure, acceleration, etc.) into an output, generally an electrical signal, by means of a conditioning and/or signal processing electronics. The input magnitudes or measurands include chemical and/or biological magnitudes such as concentration and identity unknown species in gaseous or liquid phase, other than physical general magnitudes such as the temperature, pressure, velocity, acceleration, and force. A transduction process is realized by converting the input event or measurand into an output electrical signal (analog voltage or current, digital voltage) correlated to the measurand that generates it. The output electrical signal is properly conditioned, processed, and stored for data analysis.

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Fig. 12.4 Scheme of a wireless sensor network with increasing complexity steps from a sensor element, to sensor module, to sensor node, to a wireless sensor network used for air quality monitoring.

The chemical solid-state sensors are characterized by the main following parameters [97, 98]: • The sensitivity is a measure of the magnitude of the output signal produced by a response to a given input magnitude, in other terms the ratio between two nonhomogeneous magnitudes such as output signal/input measurand. • The response time and recovery time indicate the time that the sensor signal spends to pass from 10% (90%) to 90% (10%) of its excursion to reach a new steady state, respectively, during the response and recovery dynamics. • The resolution is the measure of the minimal variation of the input magnitude to which the sensor is able to response for a given signal-to-noise ratio. • The limit of detection is the lowest concentration measured by the sensor for a given signal-to-noise ratio. • The selectivity characterizes the capability of the sensor to distinguish a given input magnitude from another measurand belonging to different class. The selectivity of a sensor is a characteristic very difficult to achieve. • The drift of the sensor output signal is due to intrinsic reasons without correlation to an input measurand under test. • The stability is the attitude of the sensor to keep constant in time its metrological characteristics, in other terms, to achieve its baseline signal as constant in the time independently by external factors. • The repeatability is the attitude of the sensor output signal toward a given input measurand in different repeated measurements.

Low-cost sensors for outdoor air quality monitoring

The several categories of the solid-state chemical sensors are differentiated by the physical principle of the signal transduction by distinguishing the following transducers: conductometric, optical, electrochemical, mechanical/acoustic or ultrasonic, thermal, and fieldeffect transistor (FET). A detailed classification of the solid-state chemical sensors is reported in Table 12.4 describing the principle of operation, the methods of fabrication, and technical comments. Table 12.4 Types of chemical solid-state sensors and their principle of operation Transducer

Conductometric

Principle of operation

Methods of fabrication

Electrical conductivity

PVD Microfabrication MEMS technology Screen printing

Δc ! Δσ ! Δi ! ΔV

Dip coating MEMS technology Microfabrication

Δc ! Δn ! ΔI ! Δi ! ΔV

Screen printing Dip coating MEMS technology Microfabrication PVD Microfabrication

Δc ! Δσ ! Δi ! ΔV

PVD Microfabrication PVD Screen printing Microfabrication MEMS technology

Δc ! ΔΦ ! Δi ! ΔV

• Conducting polymers

• Metal oxides • Carbon materials • Hybrid materials Optical

Electrochemical

Absorption Emission Fluorescence Chemiluminescence Evanescent wave Fiber optics Ionic conductivity

• Amperometric • Potentiometric • Voltammetric Thermal

Flow of thermal energy

• • • •

Field-effect transistor (FET) Ultrasonic Mechanical Acoustic

Catalytic Pyroelectric Calorimetric Thermoelectric Charge capacitive coupling Piezoelectricity

• QCM • SAW • TFBAR

Input/output comments

Δc ! ΔT ! Δi ! ΔV

Δc ! Δm ! Δf Δc ! Δm ! Δf, Δφ

Δc ¼ variation of concentration; Δσ ¼ variation of electrical conductivity; Δi ¼ variation of current; ΔV ¼ variation of voltage; Δn ¼ variation of refractive index; ΔI ¼ variation of light intensity; ΔT ¼ variation of temperature; ΔΦ ¼ variation of work function; Δm ¼ variation of mass; Δf ¼ variation of frequency; Δφ ¼ variation of acoustic wave phase.

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12.3.2 Key indicators for air sensor performance assessment A program of air sensor evaluation should include a list of parameters that might constitute one example of a testing protocol. The key indicators to assess air sensor performance should include accuracy, precision, bias, linearity, determination coefficient (R2), rootmean-square error (RMSE), sensitivity, limit of detection (LOD), interferences or cross sensitivities such as NO2 and O3, or PM and relative humidity [99]. Usually, an approach based on a tiered performance system should address the performance targets suitable for a given use case. A breakdown of the performance tiers is classified by possible descriptors as follows: 0 1 2 3 4

Just don’t use it Qualitative Semiquantitative Reasonably quantitative Almost regulatory quality

0.00 < R2 < 0.25 0.25 < R2 < 0.50 0.50 < R2 < 0.75 0.75 < R2 < 0.90 0.90 < R2 < 1.0

To assess the performance of each calibration method at individual air pollutant levels, the measurement uncertainty should be calculated using the orthogonal regression of the estimated outputs against reference data. This uncertainty is compared with the data quality objectives (DQO) for the indicative method that corresponds to a less stringent uncertainty such as a value of 30% for O3 and 25% for NO2 at the limit value set by the European Directive. The estimation method of the uncertainty, which corresponds to the relative expanded uncertainty Ur, was carried out using Eq. (12.1), where b0 and b1 are the slope and intercept of the orthogonal regression and RSS the sum of square of residuals is calculated using Eq. (12.2). The details of calculation of the orthogonal regression can be found in the guide for the demonstration of equivalence [100, 101]: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi RSS 2  u2 ðxi Þ + ½b0 + ðb1  1Þxi 2 ðn  2Þ Ur ðyi Þ ¼ (12.1) yi X ðyi  b0  b1 xi Þ2 (12.2) RSS ¼ According to the EU Ambient Air Directive, the allowed uncertainties are 50% for PM10 and PM2.5; 30% for O3; and 25% for CO, NOx, NO2, and SO2.

12.3.3 Metrics for comparison between air sensors and reference analyzers The metrics [19, 28] are usually used for comparing sensor data (Mi) with observations from reference measurements (RMi), where n represents the total number of observations

Low-cost sensors for outdoor air quality monitoring

and μM and μRM represent the respective standard deviations for sensor observations and reference measurements. The metrics used are reported in Table 12.5. Usually, a joint exercise sensors-versus-analyzers is based on the colocation of the air sensors near reference instrumentation for any air pollutant under test. Table 12.5 Metrics used for comparing sensor data Comparison metrics

Short name

Mathematical formulas

Characteristics

Mean bias error

MBE

MBE ¼ M  RM

Correlation coefficient

r

Centered root-meansquare error

CRMSE

Rootmeansquare error

RMSE

Estimation of the magnitude of differences between sensor estimation and reference values averaged over the whole sampling period Measures the strength and the direction of a linear relationship between two variables and receives a value between 1 and 1; is independent of the difference in the variance (var) of M and RM, thus if r ¼ 1 and var.(M) < var.(RM), then variance correction may be required Used for the quadratic decomposition of RMSE as the sum of mean bias error and centered root-mean error; is an indicator of the sensor random error. Can be normalized with the standard deviation of the observations from the reference instrument Indicates the magnitude of the error and retains the variable’s unit; is sensitive to extreme values and to outliers; tends to vary as a function of the standard deviation of the RM

n 1X ðMi  M ÞðMi  RM Þ n i¼1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n X 1X 21 2 ðMi  M Þ ðRM i  RM Þ n i¼1 n i¼1

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N P 2 1 ½ðMi  M Þ  ðRM i  RM Þ n 1

ffi rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n P 2 1 ð M  RM Þ i i n i¼1

Continued

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Table 12.5 Metrics used for comparing sensor data—cont’d Comparison metrics

Normalized meansquare error

Short name

Mathematical formulas

NMSE

n P

Characteristics

ðMi  RM i Þ2

i¼1 n P

μ2 ¼ MRM 2 μ2Μ  MÞ

ðMi

i¼1

Fractional bias

FB

Factor of exceedance

FOEX

Mean absolute error

MAE

where μ indicates the mean value μ2M  μ2RM  1 2 μ + μ2RM 2 M

 100 

N ðMi > RM i Þ 1  Ntotal 2

MAE ¼

n 1X jMi  RM i j n i¼1



Estimator of the overall deviations between reference and sensor measurements; sensitive to extreme values Represents a measure of the agreement between the mean measured concentrations against the reference measurements. A perfect agreement would imply that FB ¼ NMSE ¼ 0 Measures the over or under estimation of studied measurements against reference data. The best condition would be that FOEX ¼ 0; unless, ideally, all measurements are equal to reference measurements; thus FOEX would be equal to 50. The same FOEX value (50) results from the complete underestimation of measurements, while FOEX ¼ 50 in the case of a complete overestimation Indicates the average of the magnitude of the errors; it does not indicate the direction of the error, but only its magnitude and its sensitive to outliers. Can be normalized with the standard deviation of the observations from the reference instrument

Mi indicates a value measured by one of the sensors participating in the experiment, and RMi indicates the reference measurement [19].

Low-cost sensors for outdoor air quality monitoring

12.4 Transducers and their principles of operation A transducer converts the chemical information into an electrical output by providing important functionalities to the sensor for air-pollution detection and chemical sensing. An overview of the types of transducers used for air quality monitoring is reported in the Fig. 12.5. Electrochemical gas sensors are the most promising low-cost sensors available for air quality including NO2, NO, O3, CO, SO2, H2S, and NH3 monitoring. Other low-cost air quality sensor technologies include nondispersive infrared (NDIR) cells for greenhouse gases monitoring such as CO2 and CH4 and specific volatile organic compounds (VOCs); the photoionization detectors (PID) for VOCs detection, mainly total VOCs; the metal oxide resistive sensors for broadband inorganic gases (NO2, NO, O3, CO, SO2, H2S, and NH3) and VOC measurements; and the pellistors as detectors for combustible gases (CH4, H2, and CO) and hydrocarbons. Finally, optical particle counters (OPCs) for counting particulates with size from 0.3 to 40 μm. Advantages and disadvantages of the different technologies are discussed elsewhere [102, 103].

12.4.1 Transducers for chemical sensors The low-cost commercial sensors represent a big opportunity to operate sensor networks deployed for monitoring the ambient gases within large urban areas and hot spots. All chemical sensors comprise an appropriate chemically sensitive material interfaced to a transducer. Interaction of the air pollutant with the active material generates some physical changes sensed by the transducer and converted into an output signal. Table 12.6 shows the main features of the transducers used for air quality monitoring. Here, a brief [104] of the principles of operation for transducers used in air quality monitoring is given. • Electrochemical (EC): Standard amperometric sensors are two-electrode, threeelectrode and four-electrode cells based on fuel cell technology. Amperometric gas sensors have the advantage of having a signal output current that is linearly proportional to the concentration of the detected gas. Typically, the current commercially available devices are around 1–3 cm in diameter making these systems quite bulky Type of transducer

Air pollutant

Electrochemical

NO2, NO, O3, CO, SO2, H2S

Spectroscopic and NDIR

CO2, CH4, specific VOCs

Photo-ionisation detector

total VOCs

Optical particulate counter

PM10, PM2.5, PM1.0, BC

Metal oxides

NO2/NO, Q3, CO, SO2, H2S, VOCs

Pellistors

CH4, Hydrocarbons

Device

Fig. 12.5 Overview of the transducers used for air quality sensors and gas sensing.

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Table 12.6 Measurement parameters for air sensor performance Transducer

Sensitivity

Selectivity

Stability

Limit of detection

Electrochemical

High

Variable

Improved

ppb

Spectroscopic and NDIR

High

Variable

Low

ppm

Photoionization detector

High

Low

Improved

ppm

Optical particulate counter Metal oxides

High

Improved

Improved

μg/m3

High

Variable

Low

sub-ppm

Pellistors

High

Low

Improved

ppm

Open questions

Interference, calibration, signal processing Interference, calibration, signal processing Interference, calibration, signal processing Interference, calibration, signal processing Interference, calibration, signal processing Interference, calibration, signal processing

and robust. They operate at room temperature and low power consumption, working for around 2 years. • Spectroscopic and NDIR: this technology is based on optical transducing mechanisms such as infrared (IR) gas absorption. In these sensors, an IR light illuminates the gas to be measured. The gaseous molecules adsorb the radiation at determined narrow bands of adsorption, which is characteristic of each molecule. The intensity of this adsorption follows the Beer-Lambert equation. These sensors have a size of a few millimeters and a power consumption of a few hundred mWatt. The detection range of these sensors goes from 5% volume to 500 ppm for hydrocarbon gases and 300 to 10,000 ppm for CO2. • Photoionization detector (PID): In the photoionization process, the molecules of the target gas are illuminated by UV light. The absorbed energy can break the molecule and generate electrically charged ions. These ions are exposed to an external electrical field by generating a current. This electrical current is proportional to the gas concentration. The usual size of these sensors is around 20 mm  16 mm, with a weight of 8 g and a power consumption of 110 mWatt. This sensor is able to detect VOCs with ionization potentials below 10.6 eV. • Optical particulate counter (OPC): Particle measurements using optical instruments are based on the fact that when a particle passes through a beam of light, some of the light

Low-cost sensors for outdoor air quality monitoring

is scattered. Detection of this scattered light is the basis of all such instruments. Particle number can be determined simply by counting the pulses of scattered light reaching the detector. However, it is possible to obtain much more information using optical scattering techniques than just number. The intensity of scattered light is related to the size of the scattered particle, and this relationship can be used to make measurements of particle size. Further, the spatial scattering pattern is dependent on particle shape, so this is another parameter that can be measured with optical instruments. • Metal oxides (MOX): The metal oxides change their resistance, or conductivity, when exposed to different ambient gases. Tin oxide (SnO2) is the most used metal oxide reacting with a large number of gases. The widely accepted model is that tin oxide forms grains—thus, the grain boundaries dominate the sensing mechanisms in the electrical conductivity. In the presence of an oxidizing gas (NO2 and O3), the gas molecules react with the tin oxide (n-type) trapping electrons of the surface. This accumulation of electrons creates a negative charge space acting as a barrier for the electrons by increasing the electrical conductivity. Opposite electrical behavior occurs for reducing gases (CO, SO2, and NH3) reacting with n-type metal oxides. The temperature and humidity interfere in the sensor response and need to be controlled or measured with precision to assess their influence. Stability is an open issue. Recalibration is necessary after a certain time of operation. • Pellistors: They are detectors for combustible (flammable) gases. They operate by detecting the heat liberated when the combustible gas reacts with atmospheric oxygen on the surface of a catalytic bead. The typical pellistor consists of a fine (typically 25–50-μm diameter) platinum wire spiral supported within a bead of porous alumina. They are not specific in their response to combustible gases and respond reasonably rapidly (around 20 s) to gas concentration down to 500 ppm. They operate at high temperatures and have high power consumption, typically 350 mWatt. Other transducers developed for chemical sensing are depicted as well: • Conducting polymers (CP): they have attracted much interest as sensor materials for room-temperature operation to detect inorganic gases (NH3, NO2, etc.) and volatile organic compounds (alcohols, ketones, esters, aromatics, etc.). The most commonly applied polymers for gas-sensing applications are based on pyrrole, aniline, thiophene, and their derivatives with extended conjugation. Partial oxidation of the polymer chains leads to electrical conductivity, mainly based on p-type carriers (holes). They suffer of partial recovery of baseline signal and need to be restored with external factors (light, heat, and current). Another disadvantage is the drift occurring in the time. They are cross sensitive to humidity and many other interfering vapors. • Acoustic wave sensors (QCM, SAW, and TFBAR): they operate by detecting the effect of sorbed molecules on the propagation of acoustic waves. There are three types of acoustic sensors such as quartz crystal microbalance (QCM) based on bulk acoustic wave devices working at 10–20 MHz, the surface acoustic wave (SAW) devices

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working at higher frequencies in the range of 100–1000 MHz, and thin-film bulk acoustic resonators (TFBAR) working at highest frequencies in the range of 1–10 GHz. They consist of a piezoelectric substrate (quartz, lithium niobate, zinc oxide, and aluminum nitride) coated with a suitable sorbent layer (polymers, macromolecules, and phthalocyanines). Sorption of vapor molecules into sorbent layer is detected by changes in the acoustic wave velocity, hence changes in frequency and amplitude of the oscillating sensor. Typically, they have been studied for vapor detection at room temperature. Selectivity and stability are open questions. • Field-effect transistor (FET): field-effect gas sensors are based on metal-insulatorsemiconductor structures in which metal gate is a catalyst (platinum, palladium, and iridium) for gas sensing. Any gas that changes the surface potential, either changing the work function of gate metal or in any other way, will be sensed by these devices. A typical example is the palladium gate for hydrogen sensor in which H atoms are generated by the dehydrogenation of molecules on the palladium surface. Changing the gate metal with different sensitivity to gas under test can be provided. They operate at room temperature with reduced power consumption. They suffer as other similar devices of the loss of selectivity and stability including interference of humidity and temperature. • Fiber optics: they rely on the light guided in the fiber and reacting with a sensitive sorbent material. The most common configuration is that the optical sensing material is placed at the distal end of the fiber, but other geometries are possible. The optical properties that can be measured include changes in optical path length due to swelling of sensitive material inducing a refractive index change. In addition, other sensing mechanisms involved can be luminescence, absorption, fluorescence, and reflectance. Interferometric equipment for optical sensing is rather large and expensive; therefore, it does not compare positively with low-cost solid-state gas sensors. A substantial development of gas sensors configured as array of low-selective devices has been carried and reviewed in literature [105].

12.4.2 Air sensors versus reference analyzers The evaluation of the status of air quality (AQ) is based on ambient air measurements based on reference analyzers at high accuracy addressing the data quality objectives as required by Ambient Air EU Directive. The increasing availability of the low-cost sensors using various operation principles and refined calibration algorithms gives the opportunity to estimate the overall performance of a large number of collocated sensors [19, 20, 106]. Calibration methods have been based on sensor intercomparison with reference analyzers by collocation approach to enhance the data quality, so that a sensor network may generate reliable AQ data. Colocation method consists of sensors compared with a reference instrument closely located (within 10 m) to determine if sensors

Low-cost sensors for outdoor air quality monitoring

Table 12.7 Qualitative comparison of selected parameters between low-cost sensors and reference analyzers Parameters

Low-cost sensors

Reference analyzers

Accuracy Sensitivity Detection limit Response time Selectivity Stability Durability Range Interferences

Low Medium Variable Variable Variable Variable Short Variable Variable

High High Improved Improved High High Long Broadband Minimal

measure reasonable values and changes [15]. A qualitative comparison of the performance sensors-versus-analyzers is reported in the Table 12.7.

12.5 Air quality stationary sensor networks The recent rapid development of the low-cost sensors and related technologies and systems has been a strong drive to meet a wide variety of measurement needs and to deploy a distributed network of multiple sensor nodes in urban and rural areas for air quality monitoring. Such experiments aim to provide the atmospheric composition at high spatial and temporal resolution to supplement the existing air quality monitoring stations. In such cases, a side-by-side comparison of sensors-versus-analyzers has been realized to evaluate the accuracy of data generated by sensors compared with reference analyzers [19, 20]. Several demonstration projects for air quality monitoring using wireless sensor networks have been carried out at global level. The final aim is to understand local air quality conditions, identify sources of air pollution, implement educational/outreach programs, and test appropriate mitigation strategies involving citizens and policy-makers. Here, an overview of the most successful experiments based on wireless sensor networks deployed for urban air quality monitoring is given at worldwide level.

12.5.1 Air quality stationary sensor networks in Europe A brief of the use cases of sensor networks for air quality monitoring in selected European cities is reported in Table 12.8. The first use of electrochemical gas sensors for air quality monitoring was realized by the UK project Mobile Environment Sensor System Across GRID Environments (MESSAGE) running in the first test from October 2006 to October 2009 and next experimental campaigns till 2012, using at least 20 nodes distributed in the city of Cambridge [26, 107, 108]. The Prof. Jones team, from the University of Cambridge, delivered

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Table 12.8 Selected examples of wireless sensor networks and demonstration/pilot projects, deployed in the cities of Europe, for urban air quality monitoring Duration of operation

City (country)

Air pollutants monitored

Sensor network node number

Cambridge (UK)

CO, NO2, NO, T, RH

20

>3 years

Heathrow airport (London, UK)

NO, NO2, CO, CO2, SO2, O3, VOCs, PM10, PM2.5, PM1.0, T, RH, wind velocity/ direction NO2, O3, PM1.0, T, RH

50

>1 year

6

>1 year

Oslo (NO)

CO, NO, NO2, O3, PM10, PM2.5, T, RH

24

<1 year

Barcelona (ES) Belgrade (RS) Edinburgh (UK) Haifa (IL) Ljubljana (SI) Oslo (NO) Ostrava (CZ) Vienna (AT) Bari (IT)

CO, NO, NO2, O3, PM10, PM2.5, T, RH

12–24 (variable)

<1 year

NO2, O3, CO, SO2, tVOCs, PM10, CO2, T, RH PM10, PM2.5, black carbon

10 + 1 (mobile) Danish AirGIS model

2.5 years

Zurich (CH)

Copenhagen (DK)

–

References

Jones et al. [26, 107, 108] Jones et al. [27]

Mueller et al. [30, 31, 109] Schneider et al. [28] Castell et al. [29] Broday et al. [110] Citi-Sense H2020 project

Penza et al. [36, 37] Hvidtfeldt et al. [111]

pioneering studies focusing on CO, NO2, and NO gas detection in urban environment at the city of Cambridge. Fig. 12.6A shows a typical mapping of the urban NO2 and NO air pollution monitored by the sensor network deployed in Cambridge. Recently, low-cost sensor systems have been deployed as a wireless network providing unprecedented insights into the patterns of the pollutant emissions. A case study at London Heathrow airport (LHR) has been proposed by Jones and coworkers [27]. Measurements from the sensor network were used to unequivocally distinguish airport emissions from long-range transport and then to infer emission indices from the various

Low-cost sensors for outdoor air quality monitoring

Sensor units components Satellite navigation

Simple operation!

Mobile phone

Gas sensors 400 gm (incl. batteries)

(A) Anemometer GPS OPC inlet

GPRS CO2

Optical particle counter

USB memory

PID (VOCs)

T, RH

(B)

Electrochemical cells: NO, CO, NO2, SO2, O3

Power cable

Fig. 12.6 (A) Mapping of urban air pollution of NO2 and NO in the city of Cambridge by distributed and networked electrochemical gas sensors. (B) Air quality sensor network deployed around Heathrow airport using 50 nodes (NO, NO2, CO, CO2, SO2, O3, VOCs, PM10, PM2.5, PM1.0, T, and RH) for longterm campaign. ((A) Courtesy of Prof. R.L. Jones, University of Cambridge. (B) Reprinted with permission from O.A.M. Popoola, et al., Use of networks of low cost air quality sensors to quantify air quality in urban settings, Atmos. Environ. 194 (2018) 58–70.)

airport activities. Fig. 12.6B shows the mapping of the sensor network for air quality (SNAQ) deployed around Heathrow airport for air-pollution monitoring using a sensor node equipped by multiple air sensors based on electrochemical devices (NO, NO2, CO, SO2, and O3), photoionization detectors (VOCs), optical particle counters (PM10, PM2.5, and PM1.0), NDIR devices (CO2), and meteorological sensors (T, RH, and wind velocity/direction). EMPA studied air pollution in the city of Zurich (Switzerland) focusing on O3 and NO2 networked sensors [30, 109] and mobile PM optical counters [31]. Each stationary sensor node (NO2 and O3) was colocated near to a regulatory network station of reference air-pollution monitors. These low-cost O3 and NO2 gas sensors exhibited accuracy of a few ppb in the first 1–3 months of operation. Comparison with diffusion tube measurements and regulatory measurements from air quality monitoring stations revealed that this accuracy could not be maintained during the entire 1-year network deployment due to the interferences in the changing response of the air sensors. Several issues were encountered such as persistent decrease in the sensor accuracy. Hence, the air quality monitoring strategies have been advised as a prerequisite when low-cost sensors operate to assess the data quality.

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NILU studied air pollution in the city of Oslo (Norway) deploying 24 networked nodes based on electrochemical sensors (CO, NO, NO2, and O3) and optical particle counters (PM10 and PM2.5) using the colocation approach with sensor nodes working near to a station of the regulatory air quality monitoring network [28, 29]. The experimental campaign last at least 6 months (April-October 2015). Laboratory calibration of the deployed sensor nodes was carried out. A 3D Eulerian/Lagrangian dispersion model (EPISODE) was used to provide urban- and regional-scale air quality forecasting of the atmospheric pollutants. Data fusion was applied to combine sensor observations with model data. This methodology provides prediction of the targeted pollutant concentrations at unknown locations by interpolating the sensor observations and using the model data as proxy information to provide concentration data as spatial patterns (or temporal trends). Broday and CITI-Sense project collaborators [110] provided a study on urban air pollution monitored in eight cities (Barcelona, Belgrade, Edinburgh, Haifa, Ljubljana, Oslo, Ostrava, and Vienna) by a wireless sensor network including gases (NO, NO2, O3, and CO) and particulate matter (PM10 and PM2.5) for a variable period (< 1 year) of field test. The stationary sensor nodes were more reliable than personal/mobile nodes. The authors concluded that the sensor measurements tend to suffer from the interference of various environmental factors and require frequent calibrations. An Italian national project RES-NOVAE—Networks, Buildings, Streets: New Challenging Targets for Environment and Energy deployed one mobile and 10 stationary nodes that were installed in specific sites (buildings, offices, schools, streets, port, and airport), operated for 30 months, to enhance the citizen environmental awareness. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO2, O3, and SO2), an optical particle counter (PM10), a NDIR infrared sensor (CO2), and a photoionization detection (total VOCs), including low-cost sensors for temperature and relative humidity. As an example of the performance, the mean absolute error (MAE) of the PM10 for three locations was 5.6 μg/m3, while the accuracy was around 25% [36, 37]. Hvidtfeldt et al. [111] studied air pollution in terms of particulate matter and black carbon at Copenhagen (Denmark) by the AirGIS system. This is a fully deterministic dispersion modeling system, and its predictions are not dependent on any observed concentrations. The newly updated AirGIS has been validated, and the geographical and temporal variations have been found to be predicted well. For PM2.5, PM10, and black carbon (BC), the correlation coefficients between measured and modeled concentrations were in the range of 0.67–0.85, and 0.77–0.79, respectively, across different locations and measurement periods. Correlations achieved by comparing the modeled concentrations of NO2 and O3 to the measurements from permanent stations of the Danish Monitoring Network were equally high.

12.5.2 Air quality stationary sensor networks in United States A brief of the use cases of sensor networks and demonstration/pilot projects for air quality monitoring in selected US cities is reported in Table 12.9.

Low-cost sensors for outdoor air quality monitoring

Table 12.9 Selected examples of wireless sensor networks and demonstration/pilot projects, deployed in the cities of the United States, for urban and rural air quality monitoring Air pollutants monitored

Sensor network node number

Duration of operation

EPA Village Green Durham (North Carolina)

NO2, O3, PM2.5, CO2, T, RH

AQ-sensor station solar-powered and wirelessly streams real-time data

>5 years

CAIRSENSE project Suburban Atlanta area (Southern United States) BEACO2N Berkeley (California) San Francisco Bay

CO, NO, NO2, O3, SO2, PM2.5, PM10, T, RH

4

<1 year

CO2, CO, NO, NO2, O3, PM2.5, PM10

65

>5 years

O3

13

<1 year

CO, NO2, O3, VOCs, CO2, O3

9

1–3 months

7

3 months

PM1.0, UFP, NO2 SO2

8 RAMP—this study 50 RAMP deployed 9

<2 years

PM2.5, black carbon (BC), NOx, SO2 CO, NO, NO2, O3, CO2, PM2.5, PM10, T, RH

155 (samplers used)

2 years

12

1 year

City (country)

Riverside County (South California) Denver (Colorado) Boulder (Colorado) Pittsburgh (Pennsylvania) Pahala and Hilo (Hawaii) New York City

Boston airport (Massachusetts)

<6 months

References

EPA Village Green URL [112] Jiao et al. [113] Snyder et al. [14] Jiao et al. [38]

Shusterman et al. [39, 40] Kim et al. [41] Turner et al. [114] Sadighi et al. [115] Piedrahita et al. [116] Cheadle et al. [117] Li et al. [118] Hagan et al. [119] Clougherty et al. [120] ARISense [121]

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Measuring air pollution in more places is desired to address community concerns regarding local air quality impacts related to proximate sources, to provide data in areas lacking regional air monitoring, or to support environmental awareness and education. A pilot station [112] by US EPA—The Village Green—in Durham, North Carolina, has demonstrated the ability to monitor several common air pollutants in real time and make the data available online and accessed by smartphone. The Village Green Project [14, 113] model is expanding to other communities across the United States to increase awareness of this new community-based air quality monitoring system developed by EPA. The solar and wind powered station is a park bench structure with instruments that provide minute-to-minute air measurements for nitrogen dioxide, ozone, carbon monoxide, carbon dioxide, particle matter pollution, and weather conditions. To understand the capability of the emerging air sensors, the Community Air Sensor Network (CAIRSENSE) project [38] deployed low-cost and commercially available airpollution sensors at a regulatory air monitoring site and as a local sensor network based on four nodes over a surrounding 2-km area in the Southeastern United States at Atlanta suburban area. Collocation of sensors measuring nitrogen dioxide, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison with a reference monitor and the degree of multiple identical sensors producing the same signal. The Berkeley Atmospheric CO2 Observation Network (BEACO2N) [39–41, 114] operated in the San Francisco Bay Area is based on at least 65 nodes equipped by low-cost sensors for CO2, NO2, O3, CO, NO, and PM monitoring in continuous measurements from long time as at least 5 years. New dense observing sensor systems coupled to highresolution assimilation models are approaching these questions of air pollution. Instead of extrapolating from points, this network builds maps and making also movies combining remote sensing and satellite data. Fig. 12.7 shows the mapping of sensor nodes distributed in the SF Bay Area with a typical 1-week time series of air pollution (NO2, NO, CO, and O3) monitored by low-cost sensors and compared with reference analyzers [41]. The agreement is very good on short time scale of a week. Another study by Sadighi et al. [115] employs a 13-node network based on a U-Pod sensor node, constructed at the University of Colorado Boulder, to investigate spatial and temporal variability of O3 in a 200 km2 area of the Riverside County near Los Angeles, California. This tool contains low-cost sensors to collect ambient data at nonpermanent locations. Field validation of the O3 sensor measurements compared with the minuteresolution reference observations resulted in R2 and root-mean-square errors (RMSEs) of 0.95–0.97 and 4.4–5.9 ppbv, respectively. Hannigan and coworkers [116] from the University of Colorado Boulder studied air pollution at Denver (Colorado) using M-Pods homemade two types of low-cost sensors such as commercially available metal oxide semiconductor sensors used to measure CO, O3, NO2, and total VOCs and also NDIR sensors used to measure CO2. A regulatory

Low-cost sensors for outdoor air quality monitoring

Fig. 12.7 The Berkeley Atmospheric CO2 Observation Network (BEACO2N) operated in the San Francisco bay area using at least 65 sensor nodes for air pollution (CO2, CO, NO2, NO, and O3) monitoring: (A) mapping of the deployed sensor nodes; (B) design of a BEACO2N node; (C) time series of four air pollutants (NO2, O3, CO, and NO) of a single-node compared with related reference analyzer for 1-week operation—blue line by BEACO2N node; red line by reference independent analyzer. (J. Kim, et al., The Berkeley atmospheric CO2 observation network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech. 11 (2018) 1937–1946. Courtesy by Prof. R.C. Cohen, University of California, Berkeley.)

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monitoring station was used to calibrate in field the air sensors. The experiments last a few weeks. During collocation calibrations, median standard errors ranged between 4.0 and 6.1 ppb for O3, 6.4–8.4 ppb for NO2, 0.28–0.44 ppm for CO, and 16.8 ppm for CO2. The same team [117] from the University of Colorado Boulder deployed a network of seven low-cost ozone (O3) metal oxide sensor-systems (U-Pods) in both an open space and an urban location in Boulder (Colorado) during June-July 2015 to quantify ozone variations on spatial scales ranging up to 6.7 km with a measurement uncertainty as low as about 5 ppb. Also, the VOCs have been monitored by MOX sensors using a sensor network in the very close cities of Denver and Boulder in Colorado [122]. Li et al. [118], from Carnegie Mellon University, Pittsburgh (PA), reported on a sensor network based on real-time affordable multipollutant (RAMP) 50 nodes, including CO, O3, NO2, SO2, and PM2.5, deployed in the city of Pittsburgh. In this study, they measured NO2, ultrafine particle (UFP), and PM1.0 concentration with both stationary and mobile platforms in Pittsburgh, in 2016 and 2017. They sampled in eight neighborhoods (about 1 km2) representing different land-use and exposure regimes (e.g., urban and suburban and high and low traffic). They conclude that quantifying pollutant spatial patterns with high fidelity (e.g., < 2 ppb NO2 or < 1 μg/m3 PM1.0) seems unattainable in many urban areas unless the sampling network is significantly dense, with more than one or two nodes per km2. Hagan et al. [119], from MIT, reported on a SO2 sensor network based on nine colocated nodes deployed in the Hawaii islands for volcanic smog plume monitoring during an experimental campaign performed for at least 5 months in 2017. The performance of the electrochemical SO2 gas sensors is good at lower SO2 mixing ratios (<25 ppb), for which they exhibit an error of <2.5 ppb. Periodical calibration every 2 weeks has been performed during the campaign. The NYC Department of Health and Mental Hygiene [120] reported on urban air pollution at NYC using a passive sampler network at high spatial and temporal resolution. Two-week integrated samples of fine particles (PM2.5), black carbon (BC), nitrogen oxides (NOx), and sulfur dioxide (SO2) were collected at 155 city-wide street-level locations during winter 2008–09. Sites were selected using stratified random sampling, randomized across sampling sessions to minimize spatiotemporal confounding. LUR was used to identify GIS-based source indicators associated with higher concentrations. AIR Inc., Olin College, and Aerodyne plan to install 12 ARISense instruments [121] around East Boston, at a fraction of the cost of a single EPA monitoring site. Each sensor node will continuously measure the gas-phase pollutants carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), nitrogen dioxide (NO2), and ozone (O3), as well as the mass concentration of fine and coarse particulate matter (PM), and all relevant meteorological conditions. East Boston is host to Logan Airport, Boston’s international hub for air travel, and a significant source of noise and hazardous air pollutants to the surrounding communities.

Low-cost sensors for outdoor air quality monitoring

Finally, other key demonstration projects supported and/or managed by US EPA for air quality monitoring using low-cost sensor networks are listed as follows: • Weather Underground bringing air quality sensors to 250,000 network of personal weather stations—“Weather Underground.” • Google street-view cars mapping urban air pollution. • First array of Internet-of-Things sensors installed on Chicago streets (hundreds of sensors). • City of San Diego partnering with GE to deploy 3200 smart sensors.

12.5.3 Air quality stationary sensor networks in Asia A brief of the use cases of sensor networks and demonstration/pilot projects for air quality monitoring in selected Asia cities is reported in Table 12.10. The increased focus on the health effects of the air pollution and especially fine particulate matter (PM2.5) has led to new policies by Chinese Authorities aimed at controlling ambient air quality. The UN Environment Programme (UNEP) Report 2019—A review of 20 years on air pollution control in Beijing—reported on good performance of the city of Beijing and close area to abate urban air pollution [123]. In just 5 years, from 2013 to 2017, fine particle levels in Beijing and the surrounding region fell by around 35% and 25%, respectively. No other city or region on the planet has achieved such a feat. The strategy for abatement included smart mobility based on electric vehicles, efficient domestic burners, reduced biomass emissions, urban greening and multiple high-dense network monitoring based on routine monitoring network, high-density sensor monitoring network, composition observation network, and vertical monitoring network (satellite and remote sensing), as managed by Beijing Municipal Environmental Protection Bureau [124]. Fig. 12.8 shows the mapping of the city of Beijing with 2500 PM2.5 sensor nodes distributed in the metropolitan city with typical time series at high and low fine particle pollution. The high-density sensor monitoring network has been deployed to large-scale coverage of 460 km2 to monitor air quality (PM2.5) in Beijing for the first time starting from 2016, setting 2500 point locations, with the data sent out every 5 min. A national air quality monitoring network with nearly 950 monitoring stations was expected to be operational in 190 Chinese cities by the end of 2015. There are plans to build about 440 air quality observation points in 116 Chinese cities in 2016 after 496 such points were already built in 74 cities in 2012. The high-dense sensor network, which usually publicizes real-time monitoring data on air quality after being put into operation, offers an effective tool of supervision for local governments to tackle the air pollution. As an example, a sensor network of 8 PM2.5 sensor nodes has been deployed to supplement the scarce routine monitoring stations working in the industrial city of Xi’an in China [43].

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Table 12.10 Selected examples of wireless sensor networks and demonstration/pilot projects, deployed in the cities of Asia, for urban and rural air quality monitoring City (country)

Air pollutants monitored

Sensor network node number

Duration of operation

Beijing (China)

PM2.5

2500

>2 years

Xi’an (China) Beijing and other 367 cities in China Hong Kong (China) Taipei (Taiwan) Taipei and other 20 cities in Taiwan Seoul (South Korea) Tokyo (Japan) Dubai (UAE)

PM2.5

8

<1 month

CO, NO, NO2, O3, SO2, PM2.5, PM10, T, RH

Air quality management by forecast and highresolution monitoring by IBM China 6

Under test

Dong, Beijing Forum on Metropolitan Clean Air Actions [125] Sun et al. [42]

CO, NO2, O3, PM2.5, T, RH

References

UNEP Report [123] Baoxian [124] Gao et al. [43]

CO

9

Hong Kong Marathon 2015 <1 month

PM2.5

1500

<1 year

Chen et al. [127]

CO, NO2, VOC, PM10, CO2

< 10

Choi et al. [128]

NO2

6

Lab test ready for deployment <1 year

CO, NOx, O3, SO2, H2S, VOC, NMHC, CO2, TSP, PM10, PM2.5, PM1.0, T, RH, wind velocity/direction

14

>5 years

Liu et al. [126]

Tsujita et al. [129] Case-Study Aeroqual [130]

Furthermore, IBM China [125] reported on air quality management by combining forecast and high-resolution sensor network monitoring including CO, NO, NO2, O3, SO2, PM2.5, and PM10, for precise emission management to be used at Beijing and other 367 cities in China. Sun et al. [42] reported on an air quality monitoring network based on a set of six sensor systems deployed along the route of the Hong Kong Green Marathon 2015 including NO2, CO, and O3 electrochemical sensors and PM2.5 photometer.

Low-cost sensors for outdoor air quality monitoring

Fig. 12.8 The Beijing Municipal Environmental Protection Bureau manages a high-density sensor network based on 2500 sensor nodes for PM2.5 detection distributed in the city of Beijing. (A) Design of 2500 PM2.5 sensor nodes calibrated at Beijing Municipal Environmental Protection Bureau; (B) Beijing map with 2500 nodes of PM2.5 distributed in the city; (C) multiple parallel comparison of PM2.5 by sensors and reference stations in Beijing on February 2017. (UN Environment Program, A Review of 20 Years’ Air Pollution Control in Beijing, 2019. ISBN:978-92-8073743-1. Job No.: DTI/2228/PA; L. Baoxian, Air Quality Monitoring in Beijing: Application of Traditional and Innovative Technologies, in: Presentation From Beijing Municipal Environmental Monitoring Center at Conference ASIC 2018—Air Sensors International Conference, University of California Davis, 12 September 2018. Courtesy by Beijing Municipal Environmental Protection Bureau.)

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In Taiwan, different sensor networks have been developed such as for CO detection [126] using nine sensor nodes deployed in the city of Taipei and for PM2.5 monitoring [127] using 1500 sensor nodes deployed both in the city of Taipei capital and in other 20 cities in Taiwan. Choi et al. [128] from Yonsei University and Hongik University (South Korea) reported on wireless sensor networks equipped with various commercial gas sensors (CO, NO2, VOC, PM10, and CO2) used for air quality monitoring. Through extensive experiments and evaluation, they have determined the various characteristics of the gas sensors and their practical implications for air pollutant monitoring systems. Tsujita et al. [129] developed a dense real-time sensor network at Tokyo Institute of Technology in Tokyo (Japan) to monitor NO2 gas. A new autocalibration method was proposed to achieve the maintenance-free operation of the sensor network. The baseline of the gas sensor response was adjusted using the pollutant concentration values reported from the neighboring environmental monitoring station. The experimental results have shown that NO2 concentration can be measured with sufficient accuracy by incorporating appropriate temperature and humidity compensation into the calibration curves resulting useful for long-term operation. Dubai municipality [130] supported an environmental strategy to control urban air pollution by means of 14 air quality sensor stations including CO, NOx, O3, SO2, H2S, VOC, NMHC, CO2, TSP, PM10, PM2.5, PM1.0, T, and RH, wind velocity/direction installed in the city of Dubai in 2011.

12.6 Mobile sensing for air quality monitoring The air quality in urban areas is a major concern in modern cities due to significant impacts of air pollution on public health, global environment, and worldwide economy. Recent studies reveal the importance of microscale pollution information, including personal exposure and acute exposure to air pollutants, and local ecosystems. A real-time system with high spatiotemporal resolution is essential because of the limited data availability and nonscalability of the conventional air-pollution monitoring systems. The mobile sensing [102] is a key approach to enhance mapping of the urban air pollution. In fact, stationary air quality monitors/sensors atop buildings or poled to the ground give a full picture of the pollution over time but can mischaracterize air pollution just a few building blocks away. New-generation mobile sensors can cover an entire city but offer incomplete data over time. Here, an overview at the state of art of the urban mobile sensing based on air sensors mounted on ground vehicles (pedestrians, bikes, trams/buses, trucks, and cars) and unmanned aerial vehicles (UAV) is shortly reported.

12.6.1 Air quality mobile sensing by ground vehicles The impact of the mobile technologies on the Citizens’ Observatory in the sectors of air quality, environmental health, and climate change has the potential to significantly

Low-cost sensors for outdoor air quality monitoring

improve data coverage by the provision of near-real-time high-resolution data over urban areas. The participatory sensing involving citizens by new sensing technologies and smartphones can enable to generate big data in the context of the urban air quality monitoring to support environmental services for citizens and decision-making [131]. The mobile sensing of the urban air pollution has been studied using sensor systems mounted on ground vehicles (bikes, buses/trams, trucks, and cars) circulating in the cities and wearable devices by pedestrians for continuous measurements to assess human personal exposure. (a) Air sensors by pedestrians Recent high-concentration episodes of air pollutants in European cities highlighted the dynamic nature of human exposure and the gaps in data and knowledge about exposure patterns. A study by Steinle et al. [132] used time-based activity data to define six microenvironments (MEs) in the city of Edinburgh (Scotland) to assess everyday exposure of the individuals toward short-term PM2.5 concentrations. A Dylos monitor, adapted in a backpack, was combined with a GPS receiver to track movement and exposure of the individuals across the MEs. Seventeen volunteers collected 35 profiles of PM2.5 concentrations. A profile is a set of data (ambient concentrations, spatiotemporal information, and contextual data) collected by a volunteer over a period of time, designed to capture everyday activities. This pilot study has demonstrated that personal exposure monitoring is a viable method for improving knowledge about individual level exposure to environmental stressors. This methodology is based on a compromise between instrument precision and information content and is limited by feasibility and privacy issues. Another study by Zappi et al. [133] from the University of California San Diego reported on design of a wearable, low-power, air quality sensor node that can be used in mobile and stationary settings. The sensor board has been used as a part of a field study involving 16 users carrying it for 2–4 weeks during their commutes to and from work. The users enjoyed the ability to share their localized pollution data in real time via cell phones with friends in their social networks. This air quality mobile node was designed at low-power and low-cost sensors to sample air pollutants (CO, NO2, and O3) and environmental parameters (temperature, humidity, and barometric pressure) and communicate via Bluetooth with a smartphone. The node is powered through its own battery and operated by microcontroller. The electrochemical gas sensors showed enough sensitivity to measure CO, NO2, and O3 levels down to 1 ppm, 20 ppb, and 10 ppb, respectively. Such concentrations are of environmental interest in open air settings. (b) Air sensors by bikes Significant studies by VITO team from Belgium have been realized by a bike Aeroflex equipped by low-cost sensors for mobile sensing in urban areas [33, 35]. Fixed air quality stations have limitations when used to assess people’s real-life exposure to air pollution. Their spatial coverage is too limited to capture the spatial variability in an urban or industrial environment. Complementary mobile air quality measurements can be used as an

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additional tool. The Aeroflex, a bicycle for mobile air quality monitoring, is equipped with compact air quality measurement devices to monitor ultrafine particle (UFP), particulate matter (PM10), and black carbon (BC) concentrations at a high resolution (up to 1 s). Also, CO gas, temperature, and relative humidity sensors; noise device; and camera have been used in the mobile measurements. Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data preprocessing, and data visualization. The Aeroflex has been successfully used for high-resolution air quality mapping, exposure assessment, and hot-spot identification in the city of Antwerp [134] and jointly Antwerp and Mol [34]. Castell [135] from NILU (Norway) studied the air quality monitoring on mobile platforms in the city of Oslo as output of the EU project CITI-Sense-MOB (2013–14). The final goal was to sense urban air pollution in Oslo by stationary sensor nodes (NO2, NO, O3, CO, PM, CO2, RH, and T), including low-cost sensors mounted on buses circulating in city, low-cost sensors on bikes running in the city, and citizens using wearable air quality sensors for personal exposure monitoring. These challenges give great opportunities to improve air quality management and public health by engagement of citizens. A paper from Velasco et al. [136] from Politecnico di Torino (Italy) was published in 2016. This study deals with a mobile and low-cost system for environmental monitoring focusing on a case study in the city of Turin (Italy). Commercial PM10 and O3 sensors were incorporated into the system and were subsequently tested in a controlled environment and in the field. The test in the field, performed in collaboration with the local environmental protection agency, revealed that the sensors can provide accurate data if properly calibrated and maintained. Further tests in the metropolitan area of Turin were carried out by mounting the sensor system on bicycles to increase their mobility along urban and extra-urban routes. Jack [137] from Columbia University reported on air-pollution effects on human health by means of low-cost sensors and portable monitors (black carbon, PM2.5, CO, NO, and CO2), including smartphone app for GPS, used by bike commuters in the city of New York. Mapping of the air pollution along urban routes was realized to visualize critical hot spots for human health. They recruit bike commuters who ride at least 30 min each way. The volunteers self-deploy sensors for six 24-h monitoring sessions. They estimate potential inhaled dose using minute ventilation from a biometric shirt. (c) Air sensors by buses and trams The community sensing is a dynamic new form of mobile geo-sensor network for air quality monitoring. This vision was illustrated through OpenSense [138]—a large national project that aims to explore community sensing driven by mobile air-pollution monitoring using low-cost sensors and systems mounted on buses and trams circulating in the cities of Zurich and Lausanne in Switzerland. Hasenfratz et al. [32] from ETHZ, in

Low-cost sensors for outdoor air quality monitoring

collaboration with EPFL and EMPA, reported on measurements made over >2 years using mobile sensor nodes installed on top of public transport vehicles in Zurich. They collected >50 million measurements using electrochemical CO, NO2, and O3 gas sensors; UFP monitors; and PM optical counters. The authors focused on creating urban pollution maps by comparison of their model with reference data of the NABEL federal stations. They elaborate a vision of how sensing should be guided using complex utilitarian approaches for sustainability. A paper of Lopez-Pena et al. [139], from the University of La Coruna and University of Vigo, deals with the development of a mobile sensor-based opportunistic urban pollution monitoring network using the public transportation buses as platforms for its deployment. The basic sensing unit and its modular conversion into a sensing system is able to acquire data on several pollutants (CO, NO2, and SO2,) as well as CO2, temperature, humidity, and geo-location information. The different prototypes were tested on the public transportation system of the city of Vigo and on multiple test runs around the city of La Corun˜a in the northwest of Spain producing very promising results. Data of the air pollution were visualized along city routes. Another paper by Penza et al. [36] reported on a mobile sensor-system AIRBOX based on low-cost sensors (CO, NO2, PM10, and CO2) mounted on a public bus (AMTAB) running in the city of Bari (Italy) for urban air quality monitoring. By exploiting this cost-effective sensor system, it is possible to use a mobile node on public bus to achieve fine-grained monitoring at high spatial and temporal resolution, because when a public bus is moving, it could conduct environmental measurements at different locations in an urban microclimate monitoring scenario to enhance environmental awareness of the citizens. The mapped positions are correlated to the risk classification of the air pollution for human health, as agreed in the US EPA Air Quality Index (AQI) standard. (d) Air sensors by trucks A large number of vehicles routinely navigate through city streets; with on-board sensors, they can be transformed into a dynamic network that monitors the urban environment comprehensively and efficiently. Anjomshoaa et al. [140] from MIT discussed on urban mobile sensing for environmental monitoring applications. It is shown that the physical properties of the urban environment can be captured using sensors (CO, CO2, NO2, O3, SO2, PM2.5, PM10, T, RH, air pressure, and particle radiation) mounted on trucks. The spatiotemporal variations of these phenomena are discussed as well as their implications on discrete-time sampling. The mobility patterns of sensor-hosting vehicles play a major role in drive-by sensing. Vehicles with scheduled trajectories, for example, buses, and those with less predictable mobility patterns, for example, taxis, are investigated for sensing efficacy in terms of spatial and temporal coverage. City Scanner is a drive-by approach with a modular sensing architecture, which enables cost-effective mass data acquisition on a multitude of city features. The City Scanner framework follows a centralized IoT regime to generate a near-real-time visualization of sensed data. The sensing platform

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was mounted on top of garbage trucks and collected drive-by data for 8 months in Cambridge, Massachusetts, the United States. Acquired data were streamed to the cloud for processing and subsequent analyses. Based on a real-world application, the authors discuss and show the potential of using drive-by approaches to collect environmental data in urban areas using a variety of nondedicated land vehicles to optimize data collection in terms of spatiotemporal coverage. Also, New York City Environmental Justice Alliance [141] supported a pilot project at NYC on improving of the garbage truck traffic and routing by using AirBeams PM2.5 sensors mounted on trucks to enhance urban air quality and workers health. (e) Air sensors by cars Urban air-pollution concentrations vary sharply over short distances (<1 km) owing to unevenly distributed emission sources, dilutions, and physicochemical transformations. Apte et al. [142] demonstrate a measurement approach to reveal urban air-pollution patterns at 4–5 orders of magnitude greater spatial precision than current central-site ambient monitoring. They equipped Google Street View vehicles with a fast response pollution measurement platform and repeatedly sampled every street in a 30 km2 area of Oakland, California, developing the largest urban air quality data set. Resulting maps of annual daytime NO, NO2, and black carbon at 30 mt scale reveal stable and persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8 within individual city blocks. Messier et al. [143] reported on air-pollution measurements collected through systematic mobile monitoring campaigns providing outdoor concentration data at high spatial resolution. They equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, California. They explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, the authors explore a “data-only” approach where they attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, they combine measured data with a land-use regression (LUR) model to predict at unobserved locations.

12.6.2 Air quality mobile sensing by unmanned aerial vehicles (UAV) A review from Queensland University of Technology, Brisbane (Australia), by Villa et al. [144] published in 2016 deals with the development and validation of an unmanned aerial vehicle (UAV)-based system for air-pollution monitoring including CO, NO2, NO, O3, CO2, PM, and atmospheric aerosol. In some cases, UAV systems can provide knowledge of pollution emission sources not accessible otherwise such as atmospheric aerosols, greenhouse gases, gaseous pollutants, volcano emissions, typhoons data collection, Arctic and Antarctic environments, prevention and intervention, local gas emissions, and other possible future applications. The authors concluded that, while the potential of UAVs for air quality research has been established, several challenges still need to be addressed,

Low-cost sensors for outdoor air quality monitoring

including the flight endurance, payload capacity, sensor dimensions/accuracy, and sensitivity. However, the challenges are not simply technological; in fact, policies and regulations, which differ between countries, represent the greatest challenge to facilitating the wider use of the UAVs in atmospheric research. Kunz et al. [145] from Max Planck Institute for Biogeochemistry of Jena (Germany) reported on a developed UAV equipped by a COmpact Carbon dioxide analyzer for Airborne Platforms (COCAP). The accuracy of COCAP’s carbon dioxide (CO2) measurements based on low-cost NDIR sensors is ensured by calibration in an environmental chamber, regular calibration in the field, and chemical drying of sampled air. In addition, the package contains a lightweight thermal stabilization system that reduces the influence of ambient temperature changes on the CO2 sensor by two orders of magnitude. During the validation of COCAP’s CO2 measurements in simulated and real flights, the authors found a measurement error of 1.2 μmol/mol. COCAP is a self-contained package that has proven well suited for the operation on-board small UAVs. Besides carbon dioxide (CO2), it also measures air temperature, relative humidity, and ambient pressure. They describe the measurement system and calibration strategy in detail to support other teams in tapping the potential of UAVs for atmospheric trace gas measurements. Andersen et al. [146] from the University of Groningen (The Netherlands) reported on an UAV equipped by AirCore consisting of ad hoc analysis chamber with a micropump to pull air by a 50-m-long stainless steel tube and a trace analyzer for greenhouse gases (CO2 and CH4) and CO monitoring. They flew the active AirCore system on a UAV near the atmospheric measurement station at Lutjewad, located in the northwest of the city of Groningen in the Netherlands. Five consecutive flights took place over a 5-h period on the same morning, from sunrise until noon. The authors validated the measurements of CO2 and CH4 from the active AirCore against those from the 60-mt close Lutjewad station. The results show a good agreement between the measurements from the active AirCore and the atmospheric station. Louie and Ning [147] from Environmental Protection Department of Hong Kong and Hong Kong University of Science and Technology reported on the application of UAV-mounted sensor technology for ship emission monitoring and high-sulfur fuel screening in the Hong Kong port. The major contributors to local and regional airpollution problems are identified as SO2, NO2, and particulate matter (PM2.5). The UAV-based airborne monitoring consists of UAV-based sensor system including sensor array (SO2, NOx, CO2, VOCs, CO, and PM), visible and infrared camera for plume detection/tracking, and autodata transmission and real-time processing.

12.7 Outlook The air quality sensor market is growing in the last 20 years, especially in regulatory research, consumers, sustainable cities, sustainable industry, indoor air quality, green buildings, outdoor sensor network, and personal exposure. A dramatic increase in the

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number of low-cost sensors deployed worldwide might approach 100 million units in the 2025 time frame [148]. Fig. 12.9 shows this increasing trend in the use of air sensors in the last 10 years (2015–25). Particular research and innovation challenges are increase in sensor performance with enhanced data quality objectives, improving standards and protocols for business growth incorporating new sensor technologies and algorithms of artificial and augmented intelligence for processing of big data, demonstration of successful proof of concepts with benefits for citizens, and creation of ecosystems and market with users and suppliers. The value chain of the air sensors includes sensor manufacturers, sensor-system integrators, data aggregators, solution providers, and action/initiative organizations resulting in evident benefits for air quality, human health, and local ecosystems including money saving. Generally, few standards exist, and no regulations accept air sensors due to their limited data quality. To overcome this gap, the environmental research community and international environmental protection agencies (US EPA, WMO, and EEA) and standardization committee (CEN) are working to deliberate performance targets designing paths for emerging air sensors useful to the sustainable development. A recent strategic workshop [149] by US EPA in June 2018 at Durham, North Carolina, United States, defined the state of art in the sector of air quality sensors, and next steps were drafted with other efforts addressing open issues. The European Sensor Systems Cluster (ESSC) [150] defined a Roadmap Toward European Leadership in Sensor Systems depicting barriers for intensive commercialization of the Sensore market evolution 2015–25 Indoor personal Regulatory research

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Fig. 12.9 Air sensors global market evolution in the period 2015–25. (Courtesy by TD Environmental Services LLC, United States. TD Environmental Services, LLC. www.tdenviro.com.)

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sensor systems surveying more than European 100 SMEs and large companies. The impact of sensor systems included environment, building technology, water management, city management, safety and security, energy saving, and efficiency. Fig. 12.10 shows some examples of sensor systems developed as prototypes and demonstrated in various real-world pilots. Some open questions of the air quality sensors are outlined: • Lower accuracy compared with reference methods • Cross sensitivity and low selectivity • Low stability and drift to be corrected periodically • Calibration needs periodically (e.g., at least 1 calibration/month) • Regular maintenance of the in-field AQ-sensor nodes • Data Quality Objective (European Directive 2008/50/EC) to be addressed for Indicative Measurements by demonstration of the equivalence to use microsensors for AQ monitoring Advantages and benefits of the air quality sensors are defined: • Low cost for deployment in cities at high spatiotemporal resolution • Suitability for personal exposure studies • Suitability for emission source information • Outdoor monitoring of gases (NO2/NO, O3, CO, SO2, H2S, tVOCs, CO2, NH3, etc.) • Outdoor monitoring of particulate matter (PM10, PM2.5, PM1.0, and UFP) • Indoor monitoring of gases (CO, VOCs, benzene, formaldehyde, naphthalene, toluene, etc.) and PM (PM10, PM2.5, and PM1.0) • Combination of sensors with modeling for microscale analysis (1–2 mt resolution) The global economy will greatly benefit from low-cost sensor development due to significant economic leverage effect.

12.8 Conclusions At the current state of technology, the low-cost air sensors are not replacements for reference analyzers, but they can provide high spatial and temporal density and resolution required for real-time urban air quality mapping and infrastructure improvement for environmental monitoring. Environmental sensing covers a large range of applications and scenarios, varying widely in terms of sample types, background matrix, analytes of interest, frequency of measurements, concentration range, local context, and climate zones. Currently, developments in cloud computing and satellite-based remote sensing offer tremendous opportunities to create new synergies linking autonomous deployed sensor networks with satellite information and enhancing citizen participation (as information creators and consumers) through cloud-based social media. Sample types range across air

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Fig. 12.10 Examples of the air sensors and sensor systems for air quality monitoring. (Courtesy by European Sensor Systems Cluster (ESSC). Roadmap Towards European Leadership in Sensor Systems— Survey of Industrial Needs, edited by The European Sensor Systems Cluster (ESSC) under support by European Commission. http://www.cluster-essc.eu and COST Action TD1105 EuNetAir, European Network on New Sensing Technologies for Air Pollution Control and Environmental Sustainability. www.cost.eunetair.it.)

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quality control in sustainable and resilient cities, green ports/airports, critical urban hot spots, landfills, wastes, soil monitoring such as leachate, and agricultural emissions (e.g., NH3 and other nitrogen pollution). Parameters of interest vary from toxic gases, volatile organic compounds, particulate matter (PM10, PM2.5, and PM1.0) and ultrafine particles, black carbon, odorants, and pollen, covering a huge range of targets and environmental scenarios, each of which can have different analytical and legal demands. A research strategy will be required by transferring analytical platforms from laboratory to the field via autonomous sensors capable of long-term independent operation with new functionalities (e.g., data acquisition and storage, low power consumption, stand-alone operation, data communication, smart miniaturization, and system integration) using advanced materials and micro-/nanotechnologies. A variety of platform types are required including low-cost sensors and sensor systems that can be used by the general public and linked to mobile phone apps consistent with the increasing importance of the citizen science. The scene is also set for a dramatic increase in the volume of data generated; from citizens using smartphone-based sensors, to distributed sensor networks, to drone-based multispectral imaging and atmospheric monitoring, to satellite-based remote sensing. Also, defining of standards in data handling, including metadata, and protocols for measurements will be essential for sharing and accessing environmental big data. There is currently limited application of low-cost sensors to support regulatory activities due to their uncertainties and lack of certification for use, but this may change in the future. However, there is already potential room in many regulatory applications for devices that do not meet the certified standards. For example, if the low-cost sensors were able to meet the data quality objectives documented in the framework of the Indicative Measurements of the EU Directive on Ambient Air (2008/50/EC), such an application would be possible. This regulatory standard for indicative measurements is less stringent than the Fixed Measurements of the EU Directive, and that can be addressed by reference instruments only at present. A certain number of cases in the current literature have been documented, and some lessons learned can be listed as follows: • Low-cost air sensors should not substitute but supplement routine environmental monitoring equipments. • Functional materials and advanced nanomaterials for enhanced air quality sensors are key technologies for next generation of environmental monitoring. • Future routine environmental networks might look very different from today and include low-cost and accurate sensors, but it still remains a challenge. • Green routes through the city or access to information about air-pollution load at specific local address might be future goals, and demonstration pilots exist worldwide. • Pervasive low-cost sensors for indoor energy efficiency and air quality should be a must for future green buildings.

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• Simple environmental indicators (e.g., air quality index—AQI) should be provided on the long-term in relation to routine monitoring and public information to enhance environmental awareness. There are many emerging issues that could drive the need for sensor performance targets. For example, community air monitoring systems are required in locations that are adversely impacted by air pollution to provide information to help reduce exposures. It is anticipated that sensor technologies will play an important role in implementing global Air Acts and ensuring data quality will be imperative. A significant number of challenges exist in advancing the use of air sensors with enhanced functionalities that can be provided by more performing sensor materials for air quality monitoring.

Acknowledgments This work has been partially supported by COST Association through the project COST Action TD1105— New sensing technologies for air pollution control and environmental sustainability—EuNetAir; by European Sensor Systems Cluster (ESSC); and by Italian Ministry of Education, University and Research (MIUR) through the project PON R&C Networks, Buildings, Streets: New Objectives for Challenges in Environment and Energy (RES-NOVAE) and through the project PON SNSI Remotely Piloted Aerial Systems for envIroNment And terrItory monitoRing applications (RPASINAIR). The author acknowledges the support from all members of the EuNetAir, ESSC, RES-NOVAE, and RPASINAIR consortia.

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