Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

Advancing low-cost air quality monitor calibration with machine learning methods

Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, these monitors require calibration and validation in their specific environment using expensive reference instruments, which are often scarce and costly. This study proposes machine-learning calibration methods that utilize a single high-cost instrument as an active reference to improve the accuracy of large networks of low-cost monitors. Three machine learning models-linear regression, random forest, and Gradient Boosting Regression (GBR)-were employed. The proposed approach was tested in a controlled chamber under two conditions: environmental simulations with salt- and dust-based aerosols and occupational settings using three electronic cigarette (ECIG) brands. The study involved thirty low-cost GeoAir2 monitors, divided into ten groups of three. Initially, all groups were collocated with a high-cost monitor using Aerosol A to develop prediction and regression models. These models, along with intrinsic error measurements from one group, were then applied to improve data accuracy for the remaining groups using Aerosol B. The results demonstrated substantial improvements in accuracy, with r2 values ranging from 0.91 to 1.00 and RMSE reductions of up to 88 %, depending on the model and aerosol type. GBR consistently provided the highest accuracy and performance, particularly for complex, nonlinear patterns, while linear regression offered a faster, computationally efficient alternative suitable for less demanding scenarios. Random forest models performed moderately well, balancing accuracy and complexity. These methods provide a scalable and cost-effective solution for deploying networked low-cost sensors. Further research is needed to validate these findings in outdoor environments with meteorological and spatial influences, and indoor occupational settings where humidity effects may play a role.

Traditional and low-cost technical approaches for investigating greenhouse gases and particulate matter distribution along an urban-to-rural transect (Greve River Basin, Central Italy)

Human activities, largely tied to fossil fuels and intensive agriculture, emit massive amounts of climate-altering species and harmful pollutants into the atmosphere that affect soil, ecosystems, and water. Air quality monitoring is crucial to minimize harmful effects and protect human and environmental health. The Greve River basin (Tuscany, central Italy) represents an excellent example of an ecosystem affected by various anthropogenic air contaminants. The upstream areas are predominantly rural, while the downstream zones are characterized by urban and industrial development. Air pollutants throughout the basin were measured adopting two strategies: (i) fixed monitoring at five sites using multiparametric stations equipped with low-cost sensors for CO2, CH4, and PM2.5 concentrations; (ii) measurements along a transect using a mobile monitoring station equipped with a Picarro G2201-i analyzer for the determination of CO2 and CH4 concentrations and 13C/12C values of the two gases. Results revealed relatively high CO2 and CH4 concentrations downstream, mainly due to vehicular traffic based on the isotopic signature. The temporal and spatial distribution of the contaminants mirrored the evolution of the Planetary Boundary Layer, with peak concentrations in the early morning due to stable atmospheric conditions, and contaminant dilution due to air turbulence during the daytime. Particulate (PM2.5) distribution showed a trend similar to gaseous pollutants, being strongly dependent on wind speed and rainfall events. The high spatiotemporal resolution of data acquisition provided by the low-cost stations for air quality measurements represents an important advance for developing monitoring strategies, complementing the traditional instrumentation commonly used by agencies.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 μg/m3 in HS and 5.75 and 17.58 μg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 μg/m3 in HS and 11.72 to 38.86 μg/m3 in NHS.

Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers' health.

Multilevel sensor for monitoring external and internal environment of eggs

Although it is well known that incubation environment has a great influence on embryogenesis and post-hatching performance of birds, not much is known about how external thermal, sound and light stimuli are isolated by eggshells and perceived by embryos. In this context, this study aimed to develop, calibrate and evaluate a multilevel sensor for integrated monitoring of the external (incubator) and internal environment of eggs. The variables of interest for the external environment were air temperature and relative humidity. For the internal environment, shell temperature, internal temperature, luminosity and sound pressure level were considered. The sensor was developed with an ATmega328 microcontroller, in open-source prototyping, using electronic components which are compatible with the egg's physical structure. Calibrations were carried out in a controlled environment, comparing the multilevel sensor with commercial equipment, obtaining coefficients of determination of R 2 > 0.90 for all variables studied. The multilevel sensor was also validated, simulating a commercial incubation situation and comparing eggs with 2 shell colors (white and brown) and internal volume (intact and empty). Validation results showed that white-shelled eggs insulate less external light (P < 0.001) and full eggs presented higher internal temperatures, greater light and lower sound pressure levels compared to empty eggs (P < 0.001). The multilevel sensor developed here is an innovative proposal for monitoring, simultaneously and in real time, different variables of interest in the commercial incubation environment.

Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy)

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 μg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 μg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 μg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Indication Variability of the Particulate Matter Sensors Dependent on Their Location

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.

Air pollution forecasting based on wireless communications: review

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference-grade performance

Particulate Matter (PM) low-cost sensors (LCS) present a cost-effective opportunity to improve the spatiotemporal resolution of airborne PM data. Previous studies focused on PM-LCS-reported hourly data and identified, without fully addressing, their limitations. However, PM-LCS provide measurements at finer temporal resolutions. Furthermore, government bodies have developed certifications to accompany new uses of these sensors, but these certifications have shortcomings. To address these knowledge gaps, PM-LCS of two models, 8 Sensirion SPS30 and 8 Plantower PMS5003, were collocated for one year with a Fidas 200S, MCERTS-certified PM monitor and were characterised at 2 min resolution, enabling replication of certification processes, and highlighting their limitations and improvements. Robust linear models using sensor-reported particle number concentrations and relative humidity, coupled with 2-week biannual calibration campaigns, achieved reference-grade performance, at median PM_2.5 background concentration of 5.5 μg/m³, demonstrating that, with careful calibration, PM-LCS may cost-effectively supplement reference equipment in multi-nodes networks with fine spatiotemporality.

An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring

In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R², RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R²) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m³ to 12.09 µg/m³. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM_2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO₂, PM_2.5, and O₃ shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

Evaluation of a new low-cost particle sensor as an internet-of-things device for outdoor air quality monitoring

Many low-cost particle sensors are available for routine air quality monitoring of PM_2.5, but there are concerns about the accuracy and precision of the reported data, particularly in humid conditions. The objectives of this study are to evaluate the Sensirion SPS30 particulate matter (PM) sensor against regulatory methods for measurement of real-time particulate matter concentrations and to evaluate the effectiveness of the Intelligent Air^TM sensor pack for remote deployment and monitoring. To achieve this, we co-located the Intelligent Air^TM sensor pack, developed at Clemson University and built around the Sensirion SPS30, to collect data from July 29, 2019, to December 12, 2019, at a regulatory site in Columbia, South Carolina. When compared to the Federal Equivalent Methods, the SPS30 showed an average bias adjusted R² = 0.75, mean bias error of -1.59, and a root mean square error of 2.10 for 24-hour average trimmed measurements over 93 days, and R² = 0.57, mean bias error of -1.61, and a root mean square error of 3.029, for 1-hr average trimmed measurements over 2300 hours when the central 99% of data was retained with a data completeness of 75% or greater. The Intelligent Air^TM sensor pack is designed to promote long-term deployment and includes a solar panel and battery backup, protection from the elements, and the ability to upload data via a cellular network. Overall, we conclude that the SPS30 PM sensor and the Intelligent Air^TM sensor pack have the potential for greatly increasing the spatial density of particulate matter measurements, but more work is needed to understand and calibrate sensor measurements.Implications: This work adds to the growing body of research that indicates that low-cost sensors of particulate matter (PM) for air quality monitoring has a promising future, and yet much work is left to be done. This work shows that the level of data processing and filtering effects how the low-cost sensors compare to existing federal reference and equivalence methods: more data filtering at low PM levels worsens the data comparison, while longer time averaging improves the measurement comparisons. Improvements must be made to how we handle, calibrate, and correct PM data from low-cost sensors before the data can be reliably used for air quality monitoring and attainment.

Impacts of particulate matter (PM2.5) on the health status of outdoor workers: observational evidence from Malaysia

Ambient air pollution is a significant contributor to disease burden, leading to an estimated 4.2 million premature deaths and 103.1 million disability-adjusted life years (DALYs) annually worldwide. As industrialization and urbanization surge in Asia, air pollution and its corresponding health issues follow suit. Findings on disease burden in developing countries are extremely scanty. This study aimed to determine the concentration of PM_2.5 and its impact on respiratory health of outdoor workers in Malaysia. A 2-cycled 3-month cohort study involving 440 participants was conducted. Workers' health status was assessed via (1) Total Ocular Symptom Score (TOSS), (2) Total Nasal Symptom Score (TNSS), (3) St. George's Respiratory Questionnaire (SGPQ), and (4) Asthma Control Test (ACT). The maximum PM_2.5 concentration was measured at 122.90 ± 2.07 µg/m³ during third week of August 2016. Meanwhile, the minimum concentration was measured at 57.47 ± 3.80 µg/m³ and 57.47 ± 1.64 µg/m³ during fourth week of July 2016 and first week of August 2017 respectively. Findings revealed that TOSS, TNSS, and SGPQ changes were significantly (p < 0.05) associated with the concentration of PM_2.5. Outdoor workers were more significantly (p < 0.05) affected by changes in PM_2.5 compared to indoor workers with a moderate correlation (r value ranged from 0.4 to 0.7). Ironically, no significant association was found between ACT assessment and PM_2.5. Collectively, our findings suggested that changes in the concentration of PM_2.5 threatened the respiratory health of outdoor workers. The existing policy should be strengthened and preventive measures to be enforced safeguarding health status of outdoor workers.

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems.

Environmental Monitoring of the Littoral Zone of Lake Baikal Using a Network of Automatic Hydro-Meteorological Stations: Development and Trial Run

An automatic hydro-meteorological station (AHMS) was designed to monitor the littoral zone of Lake Baikal in areas with high anthropogenic pressure. The developed AHMS was installed near the Bolshiye Koty settlement (southern basin). This AHMS is the first experience focused on obtaining the necessary competencies for the development of a monitoring network of the Baikal natural territory. To increase the flexibility of adjustment and repeatability, we developed AHMS as a low-cost modular system. AHMS is equipped with a weather station and sensors measuring water temperature, pH, dissolved oxygen, redox potential, conductivity, chlorophyll-a, and turbidity. This article describes the main AHMS functions (hardware and software) and measures taken to ensure data quality control. We present the results of the first two periods of its operation. The data acquired during this periods have demonstrated that, to obtain accurate measurements and to detect and correct errors that were mainly due to biofouling of the sensors and calibration bias, a correlation between AHMS and laboratory studies is necessary for parameters such as pH and chlorophyll-a. The gained experience should become the basis for the further development of the monitoring network of the Baikal natural territory.

Real-Time Low-Cost Personal Monitoring for Exposure to PM2.5 among Asthmatic Children: Opportunities and Challenges

This study aims to evaluate the accuracy and effectiveness of real-time personal monitoring of exposure to PM concentrations using low-cost sensors, in comparison to conventional data collection method based on fixed stations. PM2.5 data were measured every 5 min using a low-cost sensor attached to a bag carried by 47 asthmatic children living in the Seoul Metropolitan area between November 2019 and March 2020, along with the real-time GPS location, temperature, and humidity. The mobile sensor data were then matched with station-based hourly PM2.5 data using the time and location. Despite some uncertainty and inaccuracy of the sensor data, similar temporal patterns were found between the two sources of PM2.5 data on an aggregate level. However, average PM2.5 concentrations via personal monitoring tended to be lower than those from the fixed stations, particularly when the subjects were indoors, during nighttime, and located farther from the fixed station. On an individual level, a substantial discrepancy is observed between the two PM2.5 data sources while staying indoors. This study provides guidance to policymakers and researchers on improving the feasibility of personal monitoring via low-cost mobile sensors as an alternative or supplement to the conventional station-based monitoring.

Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment

Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.

The Exposome in the Era of the Quantified Self

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

How Is Indoor Air Quality during Sleep? A Review of Field Studies

This review aimed to provide an overview of the characterisation of indoor air quality (IAQ) during the sleeping period, based only on real life conditions’ studies where, at least, one air pollutant was considered. Despite the consensual complexity of indoor air, when focusing on sleeping environments, the available scientific literature is still scarce and falls to provide a multipollutants’ characterisation of the air breathed during sleep. This review, following PRISMA’s approach, identified a total of 22 studies that provided insights of how IAQ is during the sleeping period in real life conditions. Most of studies focused on carbon dioxide (77%), followed by particles (PM2.5, PM10 and ultrafines) and only 18% of the studies focused on pollutants such as carbon monoxide, volatile organic compounds and formaldehyde. Despite the high heterogeneity between studies (regarding the geographical area, type of surrounding environments, season of the year, type of dwelling, bedrooms’ ventilation, number of occupants), several air pollutants showed exceedances of the limit values established by guidelines or legislation, indicating that an effort should be made in order to minimise human exposure to air pollutants. For instance, when considering the air quality guideline of World Health Organisation of 10 µg·m−3 for PM2.5, 86% of studies that focused this pollutant registered levels above this threshold. Considering that people spend one third of their day sleeping, exposure during this period may have a significant impact on the daily integrated human exposure, due to the higher amount of exposure time, even if this environment is characterised by lower pollutants’ levels. Improving the current knowledge of air pollutants levels during sleep in different settings, as well as in different countries, will allow to improve the accuracy of exposure assessments and will also allow to understand their main drivers and how to tackle them.

Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area

This paper analyzes 12 years of tropospheric ozone (O₃) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O₃ analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O₃ concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O₃ concentration levels during the study period were not harmful to human health.

Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets

The use of air sensor technology is increasing worldwide for a variety of applications, however, with significant variability in data quality. The United States Environmental Protection Agency held a workshop in July 2019 to deliberate possible performance targets for air sensors measuring particles with aerodynamic diameters of 10 μm or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). These performance targets were discussed from the perspective of non-regulatory applications and with the sensors operating primarily in a stationary mode in outdoor environments. Attendees included representatives from multiple levels of government organizations, sensor developers, environmental nonprofits, international organizations, and academia. The workshop addressed the current lack of sensor technology requirements, discussed fit-for-purpose data quality needs, and debated transparency issues. This paper highlights the purpose and key outcomes of the workshop. While more information on performance and applications of sensors is available than in past years, the performance metrics, or parameters used to describe data quality, vary among the studies reports and there is a need for more clear and consistent approaches for evaluating sensor performance. Organizations worldwide are increasingly considering, or are in the process of developing, sensor performance targets and testing protocols. Workshop participants suggested that these new guidelines are highly desirable, would help improve data quality, and would give users more confidence in their data. Given the wide variety of uses for sensors and user backgrounds, as well as varied sensor design features (e.g., communication approaches, data tools, processing/adjustment algorithms and calibration procedures), the need for transparency was a key workshop theme. Suggestions for increasing transparency included documenting and sharing testing and performance data, detailing best practices, and sharing data processing and correction approaches.

A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives

The concerns related to particulate matter's health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device's operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network

Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM_2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM_2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM_2.5 levels were compared with those of GRIMM-calibrated PM_2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m³, reduced from 18.4 ± 6.5 μg/m³ before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM_2.5 sensor networks.

Robust Estimation of Carbon Monoxide Measurements

This paper presents a robust analysis of carbon monoxide (CO) concentration measurements conducted at the Belisario air-quality monitoring station (Quito, Ecuador). For the analysis, the data collected from 1 January 2008 to 31 December 2019 were considered. Additionally, each of the twelve years analyzed was considered as a random variable, and robust location and scale estimators were used to estimate the central tendency and dispersion of the data. Furthermore, classic, nonparametric, bootstrap, and robust confidence intervals were used to group the variables into categories. Then, differences between categories were quantified using confidence intervals and it was shown that the trend of CO concentration at the Belisario station in the last twelve years is downward. The latter was proven with the precision provided by both nonparametric and robust statistical methods. The results of the research work robustly proved that the CO concentration at Belisario station in the last twelve years is not considered a health risk, according to the criteria established by the Quito Air Quality Index.

The Silver Lining of COVID-19: Estimation of Short-Term Health Impacts Due to Lockdown in the Yangtze River Delta Region, China

The outbreak of COVID-19 in China has led to massive lockdowns in order to reduce the spread of the epidemic and control human-to-human transmission. Subsequent reductions in various anthropogenic activities have led to improved air quality during the lockdown. In this study, we apply a widely used exposure-response function to estimate the short-term health impacts associated with PM_2.5 changes over the Yangtze River Delta (YRD) region due to COVID-19 lockdown. Concentrations of PM_2.5 during lockdown period reduced by 22.9% to 54.0% compared to pre-lockdown level. Estimated PM_2.5-related daily premature mortality during lockdown period is 895 (95% confidential interval: 637-1,081), which is 43.3% lower than pre-lockdown period and 46.5% lower compared with averages of 2017-2019. According to our calculation, total number of avoided premature death aassociated with PM_2.5 reduction during the lockdown is estimated to be 42.4 thousand over the YRD region, with Shanghai, Wenzhou, Suzhou (Jiangsu province), Nanjing, and Nantong being the top five cities with largest health benefits. Avoided premature mortality is mostly contributed by reduced death associated with stroke (16.9 thousand, accounting for 40.0%), ischemic heart disease (14.0 thousand, 33.2%), and chronic obstructive pulmonary disease (7.6 thousand, 18.0%). Our calculations do not support or advocate any idea that pandemics produce a positive note to community health. We simply present health benefits from air pollution improvement due to large emission reductions from lowered human and industrial activities. Our results show that continuous efforts to improve air quality are essential to protect public health, especially over city-clusters with dense population.

Study of the Influence of Technological Parameters on Generating Flat Part with Cylindrical Features in 3D Printing with Resin Cured by Optical Processing

The objective of this paper is to determine how the supporting structure in the DLP 3D printing process has influences on the characteristics of the flat and cylindrical surfaces. The part is printed by using the Light Control Digital (LCD) 3D printer technology. A Coordinate Measuring Machine (CMM) with contact probes is used for measuring the physical characteristics of the printed part. Two types of experiment were chosen by the authors to be made. The first part takes into consideration the influence of the density of the generated supports, at the bottom of the printed body on the characteristics of the flat surface. In parallel, it is studying the impact of support density on the dimension and quality of the surface. In the second part of the experiment, the influence of the printed supports dimension on the flatness, straightness and roundness of the printed elements were examined. It can be observed that both the numerical and dimensional optimum zones of the support structure for a prismatic element could be determined, according to two experiments carried out and the processing of the resulting data. Based on standardized data of flatness, straightness and roundness, it is possible to put in accord the values determined by measurement within the limits of standardized values.

Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM_2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM_2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R² = 0.83~0.90) and among the five sensors (R² = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM_2.5 concentrations. The relative bias reached −24.82% when the PM_2.5 concentration was >250 µg/m³. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM₁₀ concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary.

Atmospheric Pollutant Dispersion over Complex Terrain: Challenges and Needs for Improving Air Quality Measurements and Modeling

Pollutant dispersion processes over complex terrain are much more complicated than over flat areas, as they are affected by atmospheric interactions with the orography at different spatial scales. This paper reviews recent findings and progress in this field, focusing on both experimental and modeling perspectives. It highlights open questions and challenges to our capability for better understanding and representing atmospheric processes controlling the fate of pollutants over mountainous areas. In particular, attention is focused on new measurement techniques for the retrieval of spatially distributed turbulence information and air quality parameters, and on challenges for meteorological and dispersion models to reproduce fine-scale processes influenced by the orography. Finally, specific needs in this field are discussed, along with possible directions for future research efforts.

Monitoring of Sea-Ice-Atmosphere Interface in the Proximity of Arctic Tidewater Glaciers: The Contribution of Marine Robotics

The Svalbard archipelago, with its partially closed waters influenced by both oceanic conditions and large tidal glaciers, represents a prime target for understanding the effects of ongoing climate change on glaciers, oceans, and ecosystems. An understanding of the role played by tidewater glaciers in marine primary production is still affected by a lack of data from close proximity to glacier fronts, to which, for safety reasons, manned surface vessels cannot get too close. In this context, autonomous marine vehicles can play a key role in collecting high quality data in dangerous interface areas. In particular, the contribution given by light, portable, and modular marine robots is discussed in this paper. The state-of-the-art of technology and of operating procedures is established on the basis of the experience gained in campaigns carried out by Italian National Research Council (CNR) robotic researchers in Ny-Ålesund, Svalbard Islands, in 2015, 2017, and 2018 respectively. The aim was to demonstrate the capability of an Unmanned Semi-Submersible Vehicle (USSV): (i) To collect water samples in contact with the front of a tidewater glacier; (ii) to work in cooperation with Unmanned Aerial Vehicles (UAV) for sea surface and air column characterisation in the proximity of the fronts of the glaciers; and (iii) to perform, when equipped with suitable tools and instruments, repetitive sampling of water surface as well as profiling the parameters of the water and air column close to the fronts of the tidewater glaciers. The article also reports the issues encountered in navigating in the middle of bergy bits and growlers as well as the problems faced in using some sensors at high latitudes.

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m-3 for PM2.5 and ≈3 µg m-3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m-3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions

Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high wind speed. To address this research gap, a seven-month-long inter-comparison campaign was carried out at Mt. Tai (1534 m a.s.l.) from 20 April to 30 November 2018, covering a wide range of air temperatures, relative humidities (RHs), and wind speeds. The performance of three commonly used sensors for carbon monoxide (CO), ozone (O3), and particulate matter (PM2.5) was evaluated against the reference instruments. Strong positive linear relationships between sensors and the reference data were found for CO (r = 0.83) and O3 (r = 0.79), while the PM2.5 sensor tended to overestimate PM2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM2.5 between the sensor and reference data (r = 0.91). The impacts of temperature, RH, wind speed, and pressure on the sensor measurements were comprehensively assessed. Temperature showed a positive effect on the CO and O3 sensors, RH showed a positive effect on the PM sensor, and the influence of wind speed and air pressure on all three sensors was relatively minor. Two methods, namely a multiple linear regression model and a random forest model, were adopted to minimize the influence of meteorological factors on the sensor data. The multi-linear regression (MLR) model showed a better performance than the random forest (RF) model in correcting the sensors’ data, especially for O3 and PM2.5. Our results demonstrate the capability and potential of the low-cost sensors for the measurement of trace gases and aerosols at high mountain sites with complex weather conditions.

MoreAir: A Low-Cost Urban Air Pollution Monitoring System

MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5 μm) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a-trimmed mean and Winsorized standard error of order a, location and scale estimators based on the Andrew's wave, biweight location and scale estimators, and estimators based on the bootstrap-t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

Methanol and Humidity Capacitive Sensors Based on Thin Films of MOF Nanoparticles

The successful development of modern gas sensing technologies requires high sensitivity and selectivity coupled to cost effectiveness, which implies the necessity to miniaturize devices while reducing the amount of sensing material. The appealing alternative of integrating nanoparticles of a porous metal-organic framework (MOF) onto capacitive sensors based on interdigitated electrode (IDE) chips is presented. We report the deposition of MIL-96(Al) MOF thin films via the Langmuir-Blodgett (LB) method on the IDE chips, which allowed the study of their gas/vapor sensing properties. First, sorption studies of several organic vapors like methanol, toluene, chloroform, etc. were conducted on bulk MOF. The sorption data revealed that MIL-96(Al) presents high affinity toward water and methanol. Later on, ordered LB monolayer films of MIL-96(Al) particles of ∼200 nm were successfully deposited onto IDE chips with homogeneous coverage of the surface in comparison to conventional thin film fabrication techniques such as drop-casting. The sensing tests showed that MOF LB films were selective for water and methanol, and short response/recovery times were achieved. Finally, chemical vapor deposition (CVD) of a porous thin film of Parylene C (thickness ∼250-300 nm) was performed on top of the MOF LB films to fabricate a thin selective layer. The sensing results showed an increase in the water selectivity and sensitivity, while those of methanol showed a huge decrease. These results prove the feasibility of the LB technique for the fabrication of ordered MOF thin films onto IDE chips using very small MOF quantities.

Out of sight, out of mind: participatory sensing for monitoring indoor air quality

In southern Chile, epidemiological studies have linked high levels of air pollution produced by the use of wood-burning stoves with the incidence of numerous diseases. Using a quasi-experimental design, this study explores the potential of participatory sensing strategies to transform experiences, perceptions, attitudes, and daily routine activities in 15 households equipped with wood-burning stoves in the city of Temuco, Chile. The results suggest that the experience of using a low-cost sensor improves household members' awareness levels of air pollution. However, the information provided by the sensors does not seem to improve the participants' self-efficacy to control air quality and protect themselves from pollution. The high degree of involvement with the participatory sensing experience indicates that the distribution of low-cost sensors could be a key element in the risk communication policies.

Low Cost Autonomous Lock-In Amplifier for Resistance/Capacitance Sensor Measurements

This paper presents the design and experimental characterization of a portable high-precision single-phase lock-in instrument with phase adjustment. The core consists of an analog lock-in amplifier IC prototype, integrated in 0.18 µm CMOS technology with 1.8 V supply, which features programmable gain and operating frequency, resulting in a versatile on-chip solution with power consumption below 834 µW. It incorporates automatic phase alignment of the input and reference signals, performed through both a fixed −90° and a 4-bit digitally programmable phase shifter, specifically designed using commercially available components to operate at 1 kHz frequency. The system is driven by an Arduino YUN board, thus overall conforming a low-cost autonomous signal recovery instrument to determine, in real time, the electrical equivalent of resistive and capacitive sensors with a sensitivity of 16.3 µV/Ω @ εrS < 3% and 37 kV/F @ εrS < 5%, respectively.

Robust Analysis of PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

In this article, a robust statistical analysis of particulate matter (PM2.5) concentration measurements is carried out. Here, the region chosen for the study was the urban park La Carolina, which is one of the most important in Quito, Ecuador, and is located in the financial center of the city. This park is surrounded by avenues with high traffic, in which shopping centers, businesses, entertainment venues, and homes, among other things, can be found. Therefore, it is important to study air pollution in the region where this urban park is located, in order to contribute to the improvement of the quality of life in the area. The preliminary study presented in this article was focused on the robust estimation of both the central tendency and the dispersion of the PM2.5 concentration measurements carried out in the park and some surrounding streets. To this end, the following estimators were used: (i) for robust location estimation: α-trimmed mean, trimean, and median estimators; and (ii) for robust scale estimation: median absolute deviation, semi interquartile range, biweight midvariance, and estimators based on a subrange. In addition, nonparametric confidence intervals were established, and air pollution levels due to PM2.5 concentrations were classified according to categories established by the Quito Air Quality Index. According to these categories, the results of the analysis showed that neither the streets that border the park nor the park itself are at the Alert level. Finally, it can be said that La Carolina Park is fulfilling its function as an air pollution filter.

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.

Outlier detection and gap filling methodologies for low-cost air quality measurements

Air pollution is a major environmental health problem around the world, which needs to be monitored. In recent years, a new generation of low-cost air pollution sensors has emerged. Poor or unknown data quality, resulting from the intrinsic properties of the sensor as well as the lack of a consensus on data processing methodologies for these sensors, has, among other factors, prevented widespread adoption of these sensors. To contribute to the creation of this consensus, we reviewed the available methodologies for quality control, outlier detection and gap filling and applied two outlier detection methodologies and five gap filling methodologies to a case study (consisting of an 11-month long air quality data set from a low-cost sensor). We showed that erroneous data can be detected in a fully automated way, and that point and contextual outlier detection methodologies can be applied to low-cost air pollution data and yield meaningful results. The linear interpolation showed the best performance for gap filling for low-cost air pollution sensors. In conclusion, data cleaning procedures are important, and the presented methods can form part of a generalised data processing methodology for low-cost air pollution sensors.

On the Security and Data Integrity of Low-Cost Sensor Networks for Air Quality Monitoring

The emerging connected, low-cost, and easy-to-use air quality monitoring systems have enabled a paradigm shift in the field of air pollution monitoring. These systems are increasingly being used by local government and non-profit organizations to inform the public, and to support decision making related to air quality. However, data integrity and system security are rarely considered during the design and deployment of such monitoring systems, and such ignorance leaves tremendous room for undesired and damaging cyber intrusions. The collected measurement data, if polluted, could misinform the public and mislead policy makers. In this paper, we demonstrate such issues by using a.com, a popular low-cost air quality monitoring system that provides an affordable and continuous air quality monitoring capability to broad communities. To protect the air quality monitoring network under this investigation, we denote the company of interest as a.com. Through a series of probing, we are able to identify multiple security vulnerabilities in the system, including unencrypted message communication, incompetent authentication mechanisms, and lack of data integrity verification. By exploiting these vulnerabilities, we have the ability of "impersonating" any victim sensor in the a.com system and polluting its data using fabricated data. To the best of our knowledge, this is the first security analysis of low-cost and connected air quality monitoring systems. Our results highlight the urgent need in improving the security and data integrity design in these systems.