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

BioSense: An automated sensing node for organismal and environmental biology

Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.

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.

Portable Sensors for Dynamic Exposure Assessments in Urban Environments: State of the Science

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO2, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O3). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R2 = 0.68-0.9, Uexp = 16-66%, BSU = 0.1-0.7 µg/m3) and BC (R2 = 0.82-0.83), but maturity of the tested NO2 sensors is still low (R2 = 0.38-0.55, Uexp = 111-614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R2 = 0.75-0.83, Uexp = 37-44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

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.

Advancing air quality monitoring: A low-cost sensor network in motion - Part I

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments.

Particulate matter forecast and prediction in Curitiba using machine learning

Introduction

Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5.

Methods

Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline.

Results

RF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables.

Discussion

The RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

Calibration Methods for Low-Cost Particulate Matter Sensors Considering Seasonal Variability

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added.

Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors

Conventional air quality monitoring has been traditionally carried out in a few fixed places with expensive measuring equipment. This results in sparse spatial air quality data, which do not represent the real air quality of an entire area, e.g., when hot spots are missing. To obtain air quality data with higher spatial and temporal resolution, this research focused on developing a low-cost network of cloud-based air quality measurement platforms. These platforms should be able to measure air quality parameters including particulate matter (PM10, PM2.5, PM1) as well as gases like NO, NO2, O3, and CO, air temperature, and relative humidity. These parameters were measured every second and transmitted to a cloud server every minute on average. The platform developed during this research used one main computer to read the sensor data, process it, and store it in the cloud. Three prototypes were tested in the field: two of them at a busy traffic site in Stuttgart, Marienplatz and one at a remote site, Ötisheim, where measurements were performed near busy railroad tracks. The developed platform had around 1500 € in materials costs for one Air Quality Sensor Node and proved to be robust during the measurement phase. The notion of employing a Proportional-Integral-Derivative (PID) controller for the efficient working of a dryer that is used to reduce the negative effect of meteorological parameters such as air temperature and relative humidity on the measurement results was also pursued. This is seen as one way to improve the quality of data captured by low-cost sensors.

Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands

This study presents the results of the long-term monitoring of PM10 and PM2.5 concentrations using a low-cost particle sensor installed in a suburban environment in the Canary Islands. A laser-scattering Nova Fitness SDS011 sensor was operated continuously for approximately three and a half years, which is longer than most other studies using this type of sensor. The impact of African dust outbreaks on the aerosol concentrations was assessed, showing a significant increase in both PM10 and PM2.5 concentrations during the outbreaks. Additionally, a good correlation was found with a nearby reference instrument of the air quality network of the Canary Islands' government. The correlation between the PM10 and PM2.5 concentrations, the effect of relative humidity, and the stability of the sensor were also investigated. This study highlights the potential of this kind of sensor for long-term air quality monitoring with a view to developing extensive and dense low-cost air quality networks that are complementary to official air quality networks.

End-user perspective of low-cost sensors for urban stormwater monitoring: a review

The large-scale deployment of low-cost monitoring systems has the potential to revolutionize the field of urban hydrology monitoring, bringing improved urban management, and a better living environment. Even though low-cost sensors emerged a few decades ago, versatile and cheap electronics like Arduino could give stormwater researchers a new opportunity to build their own monitoring systems to support their work. To find out sensors which are ready for low-cost stormwater monitoring systems, for the first time, we review the performance assessments of low-cost sensors for monitoring air humidity, wind speed, solar radiation, rainfall, water level, water flow, soil moisture, water pH, conductivity, turbidity, nitrogen, and phosphorus in a unified metrological framework considering numerous parameters. In general, as these low-cost sensors are not initially designed for scientific monitoring, there is extra work to make them suitable for in situ monitoring, to calibrate them, to validate their performance, and to connect them with open-source hardware for data transmission. We, therefore, call for international cooperation to develop uniform low-cost sensor production, interface, performance, calibration and system design, installation, and data validation guides which will greatly regulate and facilitate the sharing of experience and knowledge.

Exposure Monitoring Strategies for Applying Low-Cost PM Sensors to Assess Flour Dust in Industrial Bakeries

Low-cost particulate matter (PM) sensors provide new methods for monitoring occupational exposure to hazardous substances, such as flour dust. These devices have many possible benefits, but much remains unknown about their performance for different exposure monitoring strategies in the workplace. We explored the performance of PM sensors for four different monitoring strategies (time-weighted average and high time resolution, each quantitative and semi-quantitative) for assessing occupational exposure using low-cost PM sensors in a field study in the industrial bakery sector. Measurements were collected using four types of sensor (PATS+, Isensit, Airbeam2, and Munisense) and two reference devices (respirable gravimetric samplers and an established time-resolved device) at two large-scale bakeries, spread over 11 participants and 6 measurement days. Average PM2.5 concentrations of the low-cost sensors were compared with gravimetric respirable concentrations for 8-h shift periods and 1-min PM2.5 concentrations of the low-cost sensors were compared with time-resolved PM2.5 data from the reference device (quantitative monitoring strategy). Low-cost sensors were also ranked in terms of exposure for 8-h shifts and for 15-min periods with a shift (semi-quantitative monitoring strategy). Environmental factors and methodological variables, which can affect sensor performance, were investigated. Semi-quantitative monitoring strategies only showed more accurate results compared with quantitative strategies when these were based on shift-average exposures. The main factors that influenced sensor performance were the type of placement (positioning the devices stationary versus personal) and the company or workstation where measurements were collected. Together, these findings provide an overview of common strengths and drawbacks of low-cost sensors and different ways these can be applied in the workplace. This can be used as a starting point for further investigations and the development of guidance documents and data analysis methods.

A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities

Air pollution is an important source of morbidity and mortality. It is essential to understand to what levels of air pollution citizens are exposed, especially in urban areas. Low-cost sensors are an easy-to-use option to obtain real-time air quality (AQ) data, provided that they go through specific quality control procedures. This paper evaluates the reliability of the ExpoLIS system. This system is composed of sensor nodes installed in buses, and a Health Optimal Routing Service App to inform the commuters about their exposure, dose, and the transport's emissions. A sensor node, including a particulate matter (PM) sensor (Alphasense OPC-N3), was evaluated in laboratory conditions and at an AQ monitoring station. In laboratory conditions (approximately constant temperature and humidity conditions), the PM sensor obtained excellent correlations (R²≈1) against the reference equipment. At the monitoring station, the OPC-N3 showed considerable data dispersion. After several corrections based on the k-Köhler theory and Multiple Regression Analysis, the deviation was reduced and the correlation with the reference improved. Finally, the ExpoLIS system was installed, leading to the production of AQ maps with high spatial and temporal resolution, and to the demonstration of the Health Optimal Routing Service App as a valuable tool.

Effect of PM2.5 Levels on ED Visits for Respiratory Causes in a Greek Semi-Urban Area

Fine particulate matter that have a diameter of <2.5 μm (PM2.5) are an important factor of anthropogenic pollution since they are associated with the development of acute respiratory illnesses. The aim of this prospective study is to examine the correlation between PM2.5 levels in the semi-urban city of Volos and Emergency Department (ED) visits for respiratory causes. ED visits from patients with asthma, pneumonia and upper respiratory infection (URI) were recorded during a one-year period. The 24 h PM2.5 pollution data were collected in a prospective manner by using twelve fully automated air quality monitoring stations. PM2.5 levels exceeded the daily limit during 48.6% of the study period, with the mean PM2.5 concentration being 30.03 ± 17.47 μg/m3. PM2.5 levels were significantly higher during winter. When PM2.5 levels were beyond the daily limit, there was a statistically significant increase in respiratory-related ED visits (1.77 vs. 2.22 visits per day; p: 0.018). PM2.5 levels were also statistically significantly related to the number of URI-related ED visits (0.71 vs. 0.99 visits/day; p = 0.01). The temperature was negatively correlated with ED visits (r: −0.21; p < 0.001) and age was found to be positively correlated with ED visits (r: 0.69; p < 0.001), while no statistically significant correlation was found concerning humidity (r: 0.03; p = 0.58). In conclusion, PM2.5 levels had a significant effect on ED visits for respiratory causes in the city of Volos.

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.

Low-cost monitoring of atmospheric PM-development and testing

Ambient particulate matter (PM) pollution is a significant problem in many urban and rural regions and has severe human health implications. Real-time, spatially dense monitoring using a network of low-cost sensors (LCS) was previously proposed as a way to alleviate the problem of PM. In this study, the performance of an LCS (Plantower PMS7003), a candidate for use in such a network, was investigated. The sensor was calibrated in a controlled climate chamber against a standard reference aerosol monitor. Reproducibility and calibration were evaluated in laboratory tests. Long-term, in-field performance was studied via deploying an LCS assembly at an environmental monitoring station. Results indicated excellent unit-to-unit consistency; however, each sensor needed to be calibrated individually as their characteristics varied slightly. Based on the results of a 15-month field test, quantitative and indicative LCS performance appeared promising: overall indicative accuracy was approximately 73-75% with comparable precision and recall. It is advised that the LCS are cleaned after 6-8 months of operation. Overall, the LCS appeared suitable for low-cost monitoring.

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.

Energy poverty influences urban outdoor air pollution levels during COVID-19 lockdown in south-central Chile

The effect of COVID-19 lockdowns on ambient air pollution levels in urban south-central Chile, where outdoor air pollution primarily originates indoors from wood burning for heating, may differ from trends in cities where transportation and industrial emission sources dominate. This quasi-experimental study compared hourly fine (PM_2.5) and coarse (PM₁₀) particulate matter measurements from six air monitors (three beta attenuation monitors; three low-cost sensors) in commercial and low/middle-income residential areas of Temuco, Chile between 2019 and 2020. The potential impact of varying annual meterological conditions on air quality was also assessed. During COVID-19 lockdown, average monthly ambient PM_2.5 concentrations in a commercial and middle-income residential neighborhood of Temuco were up to 50% higher (from 12 to 18 μg/m³) and 59% higher (from 22 to 35 μg/m³) than 2019 levels, respectively. Conversely, PM_2.5 levels decreased by up to 52% (from 43 to 21 μg/m³) in low-income areas. The fine fraction of PM₁₀ in April 2020 was 48% higher than in April 2017-2019 (from 50% to 74%) in a commercial area. These changes did not appear to result from meterological differences between years. During COVID-19 lockdown, higher outdoor PM_2.5 pollution from wood heating existed in more affluent areas of Temuco, while PM_2.5 concentrations declined among poorer households refraining from wood heating. To reduce air pollution and energy poverty in south-central Chile, affordability of clean heating fuels (e.g. electricity) should be a policy priority.

Measurement of Air Pollution Parameters in Montenegro Using the Ecomar System

Particulate matter air pollution is one of the most dangerous pollutants nowadays and an indirect cause of numerous diseases. A number of these consequences could possibly be avoided if the right information about air pollution were available at a large number of locations, especially in urban areas. Unfortunately, this is not the case today. In the whole of Europe, there are just approximately 3000 automated measuring stations for PM10, and only about 1400 stations equipped for PM2.5 measurement. In order to improve this issue and provide availability of real-time data about air pollution, different low-cost sensor-based solutions are being considered both on-field and in laboratory research. In this paper, we will present the results of PM particle monitoring using a self-developed Ecomar system. Measurements are performed in two cities in Montenegro, at seven different locations during several periods. In total, three Ecomar systems were used during 1107 days of on-field measurements. Measurements performed at two locations near official automated measuring stations during 610 days justified that the Ecomar system performance is satisfying in terms of reliability and measurement precision (NRMSE 0.33 for PM10 and 0.44 for PM2.5) and very high in terms of data validity and operating stability (Ecomar 94.13%-AMS 95.63%). Additionally, five distant urban/rural locations with different traffic, green areas, and nearby industrial objects were utilized to highlight the need for more dense spatial distributions of measuring locations. To our knowledge, this is the most extensive study of low-cost sensor-based air quality measurement systems in terms of the duration of the on-field tests in the Balkan region.

Effect of PM2.5 Levels on Respiratory Pediatric ED Visits in a Semi-Urban Greek Peninsula

Ambient air pollution accounts for an estimated 4.2 million deaths worldwide. Particulate matter (PM)_2.5 particles are believed to be the most harmful, as when inhaled they can penetrate deep into the lungs. The aim of this study was to examine the relationship between PM_2.5 daily air concentrations and pediatric emergency department (ED) visits for respiratory diseases in a Greek suburban area. All pediatric ED visits for asthma-, pneumonia- and upper respiratory infection (URI)-related complaints were recorded during the one-year period. The 24-h PM_2.5 air pollution data were prospectively collected from twelve fully automated air quality monitoring stations. The mean annual concentration of PM_2.5 was 30.03 μg/m³ (World Health Organization (WHO) Air Quality Guidelines (AQG) Annual mean concentration: 10 μg/m³). PM_2.5 levels rose above the WHO Air Quality Guidelines (AQG) 24-h concentrations (25 μg/m³)), 178 times (48.6% of the study period). When PM_2.5 levels were above the daily limit, an increase of 32.44% (p < 0.001) was observed in daily pediatric ED visits for respiratory diseases and the increase was much higher during spring (21.19%, p = 0.018). A 32% (p < 0.001) increase was observed in URI-related visits, when PM_2.5 levels were ≥25 μg/m³, compared to the mean daily visits when PM_2.5 levels were <25 μg/m³. Air pollution levels were associated with increased pediatric ED visits for respiratory-related diseases.

A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices

Few studies have investigated the effect of personal PM_2.5 and PM₁ exposures on heart rate variability (HRV) for a community-based population, especially in Asia. This study evaluates the effects of personal PM_2.5 and PM₁ exposure on HRV during two seasons for 35 healthy adults living in an urban community in Taiwan. The low-cost sensing (LCS) devices were used to monitor the PM levels and HRV, respectively, for two consecutive days. The mean PM_2.5 and PM₁ concentrations were 13.7 ± 11.4 and 12.7 ± 10.5 μg/m³ (mean ± standard deviation), respectively. Incense burning was the source that contributed most to the PM_2.5 and PM₁ concentrations, around 9.2 μg/m³, while environmental tobacco smoke exposure had the greatest impacts on HRV indices, being associated with the highest decrease of 20.2% for high-frequency power (HF). The results indicate that an increase in PM_2.5 concentrations of one interquartile range (8.7 μg/m³) was associated with a change of -1.92% in HF and 1.60% in ratio of LF to HF power (LF/HF). Impacts on HRV for PM₁ were similar to those for PM_2.5. An increase in PM₁ concentrations of one interquartile range (8.7 μg/m³) was associated with a change of -0.645% in SDNN, -1.82% in HF and 1.54% in LF/HF. Stronger immediate and lag effects of PM_2.5 exposure on HRV were observed in overweight/obese subjects (body mass index (BMI) ≥24 kg/m²) compared to the normal-weight group (BMI <24 kg/m²). These results indicate that even low-level PM concentrations can still cause changes in HRV, especially for the overweight/obese population.

Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.

Investigating a Low-Cost Dryer Designed for Low-Cost PM Sensors Measuring Ambient Air Quality

Air pollution in urban areas is a huge concern that demands an efficient air quality control to ensure health quality standards. The hotspots can be located by increasing spatial distribution of ambient air quality monitoring for which the low-cost sensors can be used. However, it is well-known that many factors influence their results. For low-cost Particulate Matter (PM) sensors, high relative humidity can have a significant impact on data quality. In order to eliminate or reduce the impact of high relative humidity on the results obtained from low-cost PM sensors, a low-cost dryer was developed and its effectiveness was investigated. For this purpose, a test chamber was designed, and low-cost PM sensors as well as professional reference devices were installed. A vaporizer regulated the humid conditions in the test chamber. The low-cost dryer heated the sample air with a manually adjustable intensity depending on the voltage. Different voltages were tested to find the optimum one with least energy consumption and maximum drying efficiency. The low-cost PM sensors with and without the low-cost dryer were compared. The experimental results verified that using the low-cost dryer reduced the influence of relative humidity on the low-cost PM sensor results.

Comparison of Low-Cost Particulate Matter Sensors for Indoor Air Monitoring during COVID-19 Lockdown

This study shows the results of air monitoring in high- and low-occupancy rooms using two combinations of sensors, AeroTrak8220(TSI)/OPC-N3 (AlphaSense, Great Notley, UK) and OPC-N3/PMS5003 (Plantower, Beijing, China), respectively. The tests were conducted in a flat in Warsaw during the restrictions imposed due to the COVID-19 lockdown. The results showed that OPC-N3 underestimates the PN (particle number concentration) by about 2-3 times compared to the AeroTrak8220. Subsequently, the OPC-N3 was compared with another low-cost sensor, the PMS5003. Both devices showed similar efficiency in PN estimation, whereas PM (particulate matter) concentration estimation differed significantly. Moreover, the relationship among the PM₁-PM_2.5-PM₁₀ readings obtained with the PMS5003 appeared improbably linear regarding the natural indoor conditions. The correlation of PM concentrations obtained with the PMS5003 suggests an oversimplified calculation method of PM. The studies also demonstrated that PM₁, PM_2.5, and PM₁₀ concentrations in the high- to low-occupancy rooms were about 3, 2, and 1.5 times, respectively. On the other hand, the use of an air purifier considerably reduced the PM concentrations to similar levels in both rooms. All the sensors showed that frying and toast-making were the major sources of particulate matter, about 10 times higher compared to average levels. Considerably lower particle levels were measured in the low-occupancy room.

Spatial and Temporal Exposure Assessment to PM2.5 in a Community Using Sensor-Based Air Monitoring Instruments and Dynamic Population Distributions

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM2.5 was 42.1 µg/m3, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM2.5 was 32.4 µg/m3. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30' N, 11°21' E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.