Development and Evaluation of A Novel and Cost-Effective Approach for Low-Cost NO₂ Sensor Drift Correction

Air pollution exposure in active versus passive travel modes across five continents: A Bayesian random-effects meta-analysis

Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10, PM2.5) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO2). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM10 and PM2.5, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM10 and pedestrians for UFP) and higher exposures (compared to cyclist for PM2.5 and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.

Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality.

Development and machine learning-based calibration of low-cost multiparametric stations for the measurement of CO2 and CH4 in air

The pressing issue of atmospheric pollution has prompted the exploration of affordable methods for measuring and monitoring air contaminants as complementary techniques to standard methods, able to produce high-density data in time and space. The main challenge of this low-cost approach regards the in-field accuracy and reliability of the sensors. This study presents the development of low-cost stations for high-time resolution measurements of CO2 and CH4 concentrations calibrated via an in-field machine learning-based method. The calibration models were built based on measurements parallelly performed with the low-cost sensors and a CRDS analyzer for CO2 and CH4 as reference instrument, accounting for air temperature and relative humidity as external variables. To ensure versatility across locations, diversified datasets were collected, consisting of measurements performed in various environments and seasons. The calibration models, trained with 70 % for modeling, 15 % for validation, and 15 % for testing, demonstrated robustness with CO2 and CH4 predictions achieving R2 values from 0.8781 to 0.9827 and 0.7312 to 0.9410, and mean absolute errors ranging from 3.76 to 1.95 ppm and 0.03 to 0.01 ppm, for CO2 and CH4, respectively. These promising results pave the way for extending these stations to monitor additional air contaminants, like PM, NOx, and CO through the same calibration process, integrating them with remote data transmission modules to facilitate real-time access, control, and processing for end-users.

An analysis of degradation in low-cost particulate matter sensors

Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with ~ 15,000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM2.5. However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) Each PurpleAir sensor is comprised of two identical sensors and the divergence between their measurements can be observed, and (2) There are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of 'flagged' measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ~ 4% after 4 years of operation. Approximately 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by -0.12 μg/m3(95% CI: -0.13 μg/m3, -0.10 μg/m3) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Inter- and Intra-Individual Variability of Personal Health Risk of Combined Particle and Gaseous Pollutants across Selected Urban Microenvironments

Exposure surrogates, such as air quality measured at a fixed-site monitor (FSM) or residence, are typically used for health estimates. However, people spend various amounts of time in different microenvironments, including the home, office, outdoors and in transit, where they are exposed to different magnitudes of particle and gaseous air pollutants. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as the toxicity of pollutants. We evaluated individual and combined added health risks (AR) of exposure to PM_2.5, NO_2, and O₃ for 21 participants in their daily life based on real-world personal exposure measurements. Exposure errors from using surrogates were quantified. Inter- and intra-individual variability in health risks and key contributors in variations were investigated using linear mixed-effects models and correlation analysis, respectively. Substantial errors were found between personal exposure concentrations and ambient concentrations when using air quality measurements at either FSM or the residence location. The mean exposure errors based on the measurements taken at either the FSM or residence as exposure surrogates was higher for NO₂ than PM_2.5, because of the larger spatial variability in NO₂ concentrations in urban areas. The daily time-integrated AR for the combined PM_2.5, NO₂, and O₃ (TIAR_combine) ranged by a factor of 2.5 among participants and by a factor up to 2.5 for a given person across measured days. Inter- and intra-individual variability in TIAR_combine is almost equally important. Several factors were identified to be significantly correlated with daily TIAR_combine, with the top five factors, including PM_2.5, NO₂ and O₃ concentrations at ‘home indoor’, O₃ concentrations at ‘office indoor’ and ambient PM_2.5 concentrations. The results on the contributors of variability in the daily TIAR_combine could help in targeting interventions to reduce daily health damage related to air pollutants.

Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network

Traffic-related air pollutants are major contributors to deteriorating urban air quality and pose a serious threat to pedestrians. From both a scientific and a regulatory standpoint, it is important and challenging to understand the contributions of local and non-local sources to accurately apportion specific sources such as traffic emissions contribution to on-road and near-road microenvironment air quality. In this study, we deployed mobile sensors on-board buses to monitor NO, NO₂, CO and PM_2.5 along ten important routes in Hong Kong. The measurements include two seasons: April 2017 and July 2017. Two types of baseline extraction methods were evaluated and applied to separate local and background concentrations. The results show NO and NO₂ are locally dominated air pollutants in spring, constituting 72%-84% and 58%-71%, respectively, with large inter-road variation. PM_2.5 and CO largely arise from background sources, which contribute 55%-65% and 73%-79% respectively. PM_2.5 displays a homogeneous spatial pattern, and the contributions show seasonal change, decreasing during summer. Regional transport pollution is the primary contributor during high pollution episodes. Isolated vehicle plumes show highly skewed concentration distributions. There are characteristic polluted segments on routes and they are most evident at rush hours. The most polluted road segments (top 10%) cluster at tunnel entrances and congested points. Some of these polluted locations were observed in Hong Kong's Low Emission Zones and suggest limitations to the existing control strategies, which only address larger buses. Our work gives new insights in the importance of regional cooperation to improve background air pollution combined with local control strategies to improve roadside air quality in Hong Kong.

Characterizing the Aging of Alphasense NO2 Sensors in Long-Term Field Deployments

Low-cost NO₂ sensors have been widely deployed for atmospheric sampling. While their initial performance has been characterized, few studies have examined their long-term degradation. This study focused on the performance of Alphasense low-cost NO₂ sensors (NO2-B42F and NO2-B43F) over 4 years (2016-2020). A total of 29 NO₂ sensors from 10 batches were collocated 78 times at two sites with reference instruments. Raw signals from "functional" NO₂ sensors correlated linearly with reference NO₂ concentrations. After long-term deployment, sensor raw signals started to deviate from reference NO₂ concentrations due to sensor aging, an accumulated effect after sensor unpacking. Several sensors eventually became "non-functional" as sensor raw signals showed no correlation with reference NO₂ concentrations. Sensor aging and non-functionality may be primarily caused by expiration of the ozone (O₃) scrubber built into these sensors so that sensors responded to both ambient NO₂ and O₃. The influence of O₃ on sensor response is quantified through the permutation importance method. Most of the sensors are non-functional after approximately 200-400 days of deployment, and no sensor was functional after 400 days of deployment. This result agrees well with the estimated lifetime of the built-in ozone scrubbers considering the ambient ozone concentration in the Pittsburgh area where these sensors were deployed. To ensure reliable data quality in long-term field deployments, we recommend collocating NO₂ sensors with reference instruments regularly after 200-400 days of deployment to identify and replace non-functional sensors in a timely manner.

Reducing the Influence of Environmental Factors on Performance of a Diffusion-Based Personal Exposure Kit

Sensor technology has enabled the development of portable low-cost monitoring kits that might supplement many applications in conventional monitoring stations. Despite the sensitivity of electrochemical gas sensors to environmental change, they are increasingly important in monitoring polluted microenvironments. The performance of a compact diffusion-based Personal Exposure Kit (PEK) was assessed for real-time gaseous pollutant measurement (CO, O₃, and NO₂) under typical environmental conditions encountered in the subtropical city of Hong Kong. A dynamic baseline tracking method and a range of calibration protocols to address system performance were explored under practical scenarios to assess the performance of the PEK in reducing the impact of rapid changes in the ambient environment in personal exposure assessment applications. The results show that the accuracy and stability of the ppb level gas measurement is enhanced even in heterogeneous environments, thus avoiding the need for data post-processing with mathematical algorithms, such as multi-linear regression. This establishes the potential for use in personal exposure monitoring, which has been difficult in the past, and for reporting more accurate and reliable data in real-time to support personal exposure assessment and portable air quality monitoring applications.

Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring

This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO₂]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO₂] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.

Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools

School-age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real-time PM₁₀ , PM_2.5 , NO_2, and O₃ concentrations were measured in 76 classrooms and 23 non-classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM_2.5 , PM₁₀ , O_3, and NO₂ microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM_2.5 , PM₁₀ , O_3, and NO₂ microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.

Protocol development for real-time ship fuel sulfur content determination using drone based plume sniffing microsensor system

Pollutants from navigation sector are key contributors to emission inventories of most coastal cities with heavy port activities. The use of high fuel sulfur content (FSC) bunker oil by ocean going vessels (OGVs) has been identified as a major source of sulfur dioxide (SO₂). Government authorities all over the world, including Hong Kong government, have implemented air pollution control regulations to cap FSC of fuel used by OGVs to 0.5%, from the existing 3.5%, to reduce SO₂ emissions. However, the lack of efficient screening tools to identify non-compliant OGVs has prevented effective enforcement. This study developed and evaluated an unmanned aerial vehicle (UAV)-borne lightweight (750 g) microsensor system (MSS), which is capable of measuring ship plume SO₂, NO₂, NO, CO₂, CO, and particulate matter in real-time. Extensive experiments were conducted on the sensor system to evaluate its performance during laboratory and field operations. The effects of cross-sensitivity and meteorological conditions were studied and incorporated to account for the measurement conditions in dispersed ship plumes. The SO₂ to CO₂ concentration ratio-based FSC expression was formulated as per the 2016 European Union Directive and Regulations. Furthermore, the impact of plume dilution on the accuracy of FSC measurement was investigated at different stages using the MSS, with and without the UAV in both simulated conditions and real-world scenarios, maintaining a safe distance from the OGV exhaust stacks. The study demonstrates the robustness of using UAV-borne sensor system for ship emission sniffing and FSC determination. The results will assist in development of a technological framework for effective enforcement of ship emission control regulations.

Large-Area Flexible Printed Thin-Film Transistors with Semiconducting Single-Walled Carbon Nanotubes for NO2 Sensors

Development of large-area, low-cost, low-voltage, low-power consumption, flexible high-performance printed carbon nanotube thin-film transistors (TFTs) is helpful to promote their future applications in sensors and biosensors, wearable electronics, and the Internet of things. In this work, low-voltage, flexible printed carbon nanotube TFTs with a large-area and low-cost fabrication process were successfully constructed using ultrathin (∼3.6 nm) AlO_x thin films formed by plasma oxidation of aluminum as dielectrics and screen-printed silver electrodes as contact electrodes. The as-prepared bottom-gate/bottom-contact carbon nanotube TFTs exhibit a low leakage current (∼10^-10 A), a high charge carrier mobility (up to 9.9 cm² V^-1 s^-1), high on/off ratios (higher than 10⁵), and small subthreshold swings (80-120 mV/dec) at low operation voltages (from -1.5 to 1 V). At the same time, printed carbon nanotube TFTs showed a high response (ΔR/R = 99.6%) to NO₂ gas even at 16 ppm with a faster response and recovery speed (∼8 s, exposure to 0.5 ppm NO₂), a lower detection limit (0.069 ppm NO₂), and a low power consumption (0.86 μW, exposure to 16 ppm NO₂) at a gate voltage of 0.2 V at room temperature. Moreover, the printed carbon nanotube devices exhibited excellent mechanical flexibility and bias stress stability after 12,000 bending cycles at a radius of 5 mm and a bias stress test for 7200 s at a gate voltage of ±1 V, which originated from the ultrathin and compact AlO_x dielectric and the super adhesion force between screen-printed silver electrodes and polyethylene terephthalate substrates.

Effect of Relative Humidity and Air Temperature on the Results Obtained from Low-Cost Gas Sensors for Ambient Air Quality Measurements

Using low-cost gas sensors for air quality monitoring promises cost effective and convenient measurement systems. Nevertheless, the results obtained have a questionable quality due to different factors that can affect sensor performance. The most discussed ones are relative humidity and air temperature. This investigation aimed to assess the behavior of B4-series low-cost gas sensors from Alphasense for measuring CO, NO, NO₂, and O₃ for different levels of relative humidity and temperature. These low-cost gas sensors were tested for six relative humidity levels from 10% to 85% with increasing steps of 15% and four temperature levels of 10 °C, 25 °C, 35 °C, and 45 °C against reference instruments in the laboratory. The effect of these parameters on low-cost gas sensors was quantified in laboratory from which a correction algorithm was calculated, which was then applied to the field data. The applied algorithm improved the data quality of the low-cost gas sensors in most of the cases. Additionally, a low-cost dryer was assessed to reduce the influence of these factors on the low-cost gas sensors, which also proved to be suitable to enhance the data quality.

Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors

In this pilot study, low-cost air pollution sensor nodes were fitted in waste removal trucks, hospital vans and taxis to record drivers’ exposure to air pollution in Central London. Particulate matter (PM 2.5 and PM 10 ), CO 2 , NO 2 , temperature and humidity were recorded in real-time with nodes containing low-cost sensors, an electrochemical gas sensor for NO 2 , an optical particle counter for PM 2.5 and PM 10 and a non-dispersive infrared (NDIR) sensor for CO 2 , temperature and relative humidity. An intervention using a pollution filter to trap PM and NO 2 was also evaluated. The measurements were compared with urban background and roadside monitoring stations at Honor Oak Park and Marylebone Road, respectively. The vehicle records show PM and NO 2 concentrations similar to Marylebone Road and a higher NO 2 -to-PM ratio than at Honor Oak Park. Drivers are exposed to elevated pollution levels relative to Honor Oak Park: 1.72 μ g m − 3 , 1.92 μ g m − 3 and 58.38 ppb for PM 2.5 , PM 10 , and NO 2 , respectively. The CO 2 levels ranged from 410 to over 4000 ppm. There is a significant difference in average concentrations of PM 2.5 and PM 10 between the vehicle types and a non-significant difference in the average concentrations measured with and without the pollution filter within the sectors. In conclusion, drivers face elevated air pollution exposure as part of their jobs.

Thickness Optimization of Highly Porous Flame-Aerosol Deposited WO3 Films for NO2 Sensing at ppb

Nitrogen dioxide (NO2) is a major air pollutant resulting in respiratory problems, from wheezing, coughing, to even asthma. Low-cost sensors based on WO3 nanoparticles are promising due to their distinct selectivity to detect NO2 at the ppb level. Here, we revealed that controlling the thickness of highly porous (97%) WO3 films between 0.5 and 12.3 μm altered the NO2 sensitivity by more than an order of magnitude. Therefore, films of WO3 nanoparticles (20 nm in diameter by N2 adsorption) with mixed γ- and ε-phase were deposited by single-step flame spray pyrolysis without affecting crystal size, phase composition, and film porosity. That way, sensitivity and selectivity effects were associated unambiguously to thickness, which was not possible yet with other sensor fabrication methods. At the optimum thickness (3.1 μm) and 125 °C, NO2 concentrations were detected down to 3 ppb at 50% relative humidity (RH), and outstanding NO2 selectivity to CO, methanol, ethanol, NH3 (all > 105), H2, CH4, acetone (all > 104), formaldehyde (>103), and H2S (835) was achieved. Such thickness-optimized and porous WO3 films have strong potential for integration into low-power devices for distributed NO2 air quality monitoring.

Low-cost sensors as an alternative for long-term air quality monitoring

Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM_2.5 and an Alphasense CO-B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on the performance of the monitors, the responses of the PM_2.5 sensors to different types of aerosols, and the long-term stability of the PM_2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM_2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM_2.5 (0.44< R² < 0.91) and CO (0.37< R² < 0.90). The monitors performed well at relative humidity < 75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM_2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM_2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.

A Smart Rig for Calibration of Gas Sensor Nodes

Electrochemical gas sensors require regular maintenance to check and secure proper functioning. Standard procedures usually involve testing and recalibration of the sensors, for which working environments are needed. Periodic calibration is therefore necessary to ensure reliable and accurate measurements. This paper proposes a dedicated smart calibration rig with a set of novel features enabling simultaneous calibration of multiple sensors. The proposed calibration rig system comprises a gas mixing system, temperature control system, a test chamber, and a process-control PC that controls all calibration phases. The calibration process is automated by a LabVIEW-based platform that controls the calibration environment for the sensor nodes, logs sensor data, and best fit equation based on interpolation for every sensor on the node and uploads it to the sensor node for next deployments. The communication between the PC and the sensor nodes is performed using the same IEEE 802.15.4 (ZigBee) protocol that the nodes also use in field deployment for air quality measurement. The results presented demonstrate the effectiveness of the sensors calibration rig.

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

A growing number of companies have started commercializing low-cost sensors (LCS) that are said to be able to monitor air pollution in outdoor air. The benefit of the use of LCS is the increased spatial coverage when monitoring air quality in cities and remote locations. Today, there are hundreds of LCS commercially available on the market with costs ranging from several hundred to several thousand euro. At the same time, the scientific literature currently reports independent evaluation of the performance of LCS against reference measurements for about 110 LCS. These studies report that LCS are unstable and often affected by atmospheric conditions—cross-sensitivities from interfering compounds that may change LCS performance depending on site location. In this work, quantitative data regarding the performance of LCS against reference measurement are presented. This information was gathered from published reports and relevant testing laboratories. Other information was drawn from peer-reviewed journals that tested different types of LCS in research studies. Relevant metrics about the comparison of LCS systems against reference systems highlighted the most cost-effective LCS that could be used to monitor air quality pollutants with a good level of agreement represented by a coefficient of determination R2 > 0.75 and slope close to 1.0. This review highlights the possibility to have versatile LCS able to operate with multiple pollutants and preferably with transparent LCS data treatment.

Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.