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

Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon

High-rise buildings form deep urban street canyons and restrict the dispersion of vehicle emissions, posing severe health risks to the public by aggravating roadside air quality. Field measurements are important for understanding the dispersion process of tailpipe emissions in street canyons, while a major challenge is the lack of a suitable tracer gas. Carbon dioxide (CO2), which is safe to the public and inexpensive to obtain, can be reliably measured by existing gas analysers. This study investigated the suitability of using CO2 as a tracer gas for characterising vehicle emission dispersion in a real-world street canyon. The tracer gas was released via a line or point source, whose dispersion was characterised by a sensors network comprising low-cost air quality sensors. The results showed that the CO2 contained in the exhaust gas of a test vehicle itself had unmeasurable effect at roadsides. Both the line and point sources produced obvious CO2 level elevations at approximately 30 s after the test vehicle passed by. In addition, for both line and point sources, the CO2 elevations were much more distinct at the roadside next to tailpipe exit than the opposite side, and were higher at 0.8 m than 1.6 m above the ground. The present study demonstrated that using CO2 as a tracer gas is feasible for investigating vehicle emission dispersion in real-world street canyons. Future studies are needed to improve the gas release rate of the developed tracer gas systems for more reliable measurements and larger street canyons.

Calibration and validation-based assessment of low-cost air quality sensors

BACKGROUND

Air pollution poses a significant threat to public health. Low-cost air quality sensors (LCSs) can provide a data foundation for air quality monitoring, particularly supplementing the regulatory monitoring network and identifying local air quality issues. However, the performance varies considerably, and questions remain regarding reliability and accuracy of LCS data.

METHODS

We evaluated the accuracy, stability and precision of six LCSs over a three-month period of collocation with reference instruments at two locations. A mathematical workflow including calibration and validation was developed for accuracy and stability, incorporating a combination of environmental factors (e.g., temperature, relative humidity), linear and nonlinear regression, followed by precision evaluation by Bland-Altman plots.

RESULTS

For particulate matter, data from LCSs was found to be reliable after simple linear regression (R2 > 0.9 for both calibration and validation). For gas sensors including nitrogen dioxide, carbon monoxide, and Ozone, nonlinear methods that met the validation requirements also performed well using simple linear regression models (R2 > 0.7 for both calibration and validation), whereas machine learning models, such as random forest, could not pass the validation, and require cautious application. In non-laboratory environments, incorporating environmental factors into the calibration function may lead to subsequent performance instability. Regarding precision between LCSs, unstable measurement biases among devices have been observed.

CONCLUSIONS

Linear regression method is recommended as the preferred method for onsite calibration of LCSs, with caution advised when incorporating environmental factors due to increased uncertainty. Furthermore, when deploying LCSs, it is important to consider their varying responses to high or low pollutant concentrations.

Long-term monitoring of particulate matter in an Asian community using research-grade low-cost sensors

Particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5) poses significant health risks, necessitating comprehensive exposure assessment. Long-term community monitoring can provide representative exposure levels for environmental epidemiological studies. This study deployed nine research-grade low-cost sensors (AS-LUNG-O) for 3.5 years of street-level PM2.5 monitoring in an Asian community, evaluating temporospatial variations, hotspots, and emission sources. The hourly mean PM2.5 concentrations from December 2017 to July 2021 were 24.3 ± 14.1 µg/m3. PM2.5 levels were typically higher in winter, on weekends, and during religious events compared to summer, weekdays, and typical days, with some peak concentrations occurring randomly. Daytime PM2.5 levels generally exceeded nighttime background levels by 30-50%, with certain religious activities causing up to 80% increases. Spatial analysis identified temples and markets as pollution hotspots. Using a generalized additive mixed model, we found that the COVID-19 pandemic shutdown and higher wind speeds negatively impacted PM concentrations. Religious events, traffic, and vendors were significant PM sources, continually influencing community air quality throughout the 3.5-year monitoring period. This study demonstrates the value of long-term PM monitoring in capturing unexpected peaks, identifying critical sources, and revealing intricate temporospatial distributions. Research-grade low-cost sensor networks complement traditional monitoring stations by facilitating source identification in targeted communities and providing representative PM exposure data for long-term environmental epidemiological research.

Laboratory Cross-Sensitivity Evaluation of Low-Cost Electrochemical Formaldehyde Sensors

Formaldehyde is the most abundant carbonyl globally and the biggest driver of cancer risk in the United States among hazardous air pollutants. Ambient formaldehyde concentration measurements are generally sparse due to high measurement costs and limited measurement infrastructure. Recent studies have used low-cost air quality sensors to affordably improve spatial coverage and provide real-time measurements. Our previous research evaluated the laboratory performance of a low-cost electrochemical formaldehyde sensor (Sensirion SFA30) over formaldehyde concentrations ranging from 0 to 76 ppb. The sensors exhibited good linearity of response, a low limit of detection, and good accuracy in detecting formaldehyde. This study evaluated the cross-sensitivity of the SFA30 and the Gravity sensors (electrochemical formaldehyde sensors) over formaldehyde concentrations ranging from 0 to 326 ppb in a laboratory evaluation system, with broadband cavity-enhanced absorption spectroscopy used to obtain the reference measurements. We evaluated the sensors in a mixture of formaldehyde with five outdoor trace gases (CO, NO, NO2, O3, and isobutylene) and two indoor VOCs (methanol and isopropyl alcohol). The results suggest that the Gravity sensors may be useful for outdoor formaldehyde measurements when formaldehyde levels are well above background levels and that the SFA30 sensors may be useful screening tools for indoor environments, if properly calibrated.

A critical review of sensors for detecting per- and polyfluoroalkyl substances: Focusing on diverse molecular probes

Per and Polyfluoroalkyl Substances (PFASs) pose a severe threat to the ecological environment and human health due to their persistence, bioaccumulation, and potential toxicity in the environment. Currently, the detection methods of PFASs generally rely on the combination of chromatographic techniques and mass spectrometry, which are typically suitable for laboratory testing. To meet the requirements of on-site detection, there is an urgent need to develop convenient and efficient detection methods. Sensors, as the preferred alternative, have been widely studied. In order to deeply investigate the mechanism of sensors in recognizing PFASs, this review, from the unique perspective of molecular probes, summarizes the construction and recognition mechanisms of four molecular probes: antibodies, aptamers, synthesized micromolecules, and synthesized polymers for PFASs. This review focuses on PFOA and PFOS as representative perfluoroalkyl substances and systematically investigates their properties and effects. It also analyzes the respective advantages, disadvantages, and applicable scenarios, and discusses the future development trends.

Low-Cost PM2.5 Sensor Performance Characteristics against Meteorological Influence in Sub-Saharan Africa: Evidence from the Air Sensor Evaluation and Training Facility for the West Africa Project

Fine particulate matter (PM2.5) pollution represents a major environmental health risk in Africa. The use of low-cost sensors (LCS) for air quality monitoring for policy and civic engagement in sub-Saharan Africa (SSA) has become paramount, as access to traditional reference-grade instruments is still sparse. Yet, studies pertaining to sensor performance under SSA's meteorological conditions and diverse emission sources are limited. Hence, we tested eight low-cost PM2.5 sensors on the market from different manufacturers containing Plantower PMS, Alphasense OPC-N3, and AVO-Sensor sensors by collocating them with the federal equivalent method Teledyne T640 to ascertain data accuracy, reliability, and responsiveness during wet and dry periods. After 6 months of collocation, PM2.5 concentrations from the LCS showed low intrasensor variability in both the wet and dry periods, but high intersensor variability with the Teledyne T640. A strong relationship existed between the LCS and Teledyne T640, with average coefficient of determination (R2) values of 0.7 (range: 05-0.9) and 0.8 (0.64-0.97) in the wet and dry periods, respectively. Larger errors were also associated with LCS data during the dry than the wet period, with the average mean absolute error and root mean squared error, respectively, 4.5 and 5.3 times higher in the dry period. Uncertainties with large errors were also observed with high PM2.5 measured in the wet period, levels that were more common during the dry period typically characterized by long-range transport of PM2.5 pollution. The results show that season significantly affects LCS performance and data quality and that care must be taken during deployment and data usage in SSA, with regular maintenance, particularly in the dry season. Strong collaborative efforts between governmental agencies, industries, and civil society are needed to come up with an effective framework for their application.

Relationships between fixed-site ambient measurements of nitrogen dioxide, ozone, and particulate matter and personal exposures in Grand Paris, France: the MobiliSense study

BACKGROUND

Past epidemiological studies, using fixed-site outdoor air pollution measurements as a proxy for participants' exposure, might have suffered from exposure misclassification.

METHODS

In the MobiliSense study, personal exposures to ozone (O3), nitrogen dioxide (NO2), and particles with aerodynamic diameters below 2.5 μm (PM2.5) were monitored with a personal air quality monitor. All the spatial location points collected with a personal GPS receiver and mobility survey were used to retrieve background hourly concentrations of air pollutants from the nearest Airparif monitoring station. We modeled 851,343 min-level observations from 246 participants.

RESULTS

Visited places including the residence contributed the majority of the minute-level observations, 93.0%, followed by active transport (3.4%), and the rest were from on-road and rail transport, 2.4% and 1.1%, respectively. Comparison of personal exposures and station-measured concentrations for each individual indicated low Spearman correlations for NO2 (median across participants: 0.23), O3 (median: 0.21), and PM2.5 (median: 0.27), with varying levels of correlation by microenvironments (ranging from 0.06 to 0.35 according to the microenvironment). Results from mixed-effect models indicated that personal exposure was very weakly explained by station-measured concentrations (R2 < 0.07) for all air pollutants. The R2 for only a few models was higher than 0.15, namely for O3 in the active transport microenvironment (R2: 0.25) and for PM2.5 in active transport (R2: 0.16) and in the separated rail transport microenvironment (R2: 0.20). Model fit slightly increased with decreasing distance between participants' location and the nearest monitoring station.

CONCLUSIONS

Our results demonstrated a relatively low correlation between personal exposure and station-measured air pollutants, confirming that station-measured concentrations as proxies of personal exposures can lead to exposure misclassification. However, distance and the type of microenvironment are shown to affect the extent of misclassification.

Evaluation of In-Situ Low-Cost Sensor Network in a Tropical Valley, Colombia

The increase in yearly particulate matter concentrations has been a constant issue since 2017 in the Aburrá Valley, located in Antioquia, Colombia. Although local certified air quality monitors provide high accuracy, they are limited in spatial coverage, limiting chemical transport and pollution dynamic studies in this mountainous environment. In this work, a local, Low-Cost Sensor network is proposed as an alternative and has been installed around the valley in representative locations and heights. To calibrate PM2.5 and O3 sensors used by the network, temporal delays were analyzed with Dynamic Time Warping and the linear scale was corrected with a Single Linear Regression model. As a result, the correlation coefficient R2 of the sensor reached values of 0.8 and 0.9 after calibration. For all network stations, rescaled data agrees with official historical reports on the behavior of pollutant concentrations and meteorological variables. The ability to compare the network results with certified data confirms the success of the calibration/validation method employed and contributes to the growing field of low-cost air quality sensors in Latin America.

Real-time evolution characteristics and potential reactions of contaminants in commuter bus cabin air

Despite the increasing use of motor vehicles, the impact of airborne pollutants and their health risks inside public transportation, such as commuter buses, is not well understood. This study assessed air quality inside an urban commuter bus by continuously monitoring PM10, PM2.5, and CO concentrations during both driving and parking periods. Our findings revealed that the ventilation system of the bus significantly reduced the infiltration of outdoor particulate matter and water vapor. However, CO concentrations were considerably higher inside the bus than outside, primarily due to vehicular self-emission. The ineffection of the ventilation system to remove CO potentially increases long-term exposure risks for passengers. The study identified ozone as a key oxidant in the cabin. Besides vehicle emissions, C3-C10 saturated aldehydes and carbonyl compounds were detected, including acetone, propanal, and hexanal. The presence of 6-MHO, an oxidation product of squalene, suggests that passengers contribute to VOCs load through direct emissions or skin surface reactions. Additionally, human respiration was found to significantly contribute to isoprene levels, estimated at 81.7 %. This research underscores the need for further investigation into the cumulative effects of stable compounds in cabin air and provides insights for developing healthier public transportation systems.

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data

Air sensors can provide valuable non-regulatory and supplemental data as they can be affordably deployed in large numbers and stationed in remote areas far away from regulatory air monitoring stations. Air sensors have inherent limitations that are critical to understand before collecting and interpreting the data. Many of these limitations are mechanistic in nature, which will require technological advances. However, there are documented quality assurance (QA) methods to promote data quality. These include laboratory and field evaluation to quantitatively assess performance, the application of corrections to improve precision and accuracy, and active management of the condition or state of health of deployed air quality sensors. This paper summarizes perspectives presented at the U.S. Environmental Protection Agency's 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop) by stakeholders (e.g., manufacturers, researchers, air agencies) and identifies the most pressing needs. These include QA protocols, streamlined data processing, improved total volatile organic compound (TVOC) data interpretation, development of speciated VOC sensors, and increased documentation of hardware and data handling. Community members using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. In addition to identifying the vital next steps, this work provides a set of common QA and QC actions aimed at improving and homogenizing air sensor QA that will allow stakeholders with varying fields and levels of expertise to effectively leverage air sensor data to protect human health.

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.

QUANT: a long-term multi-city commercial air sensor dataset for performance evaluation

The QUANT study represents the most extensive open-access evaluation of commercial air quality sensor systems to date. This comprehensive study assessed 49 systems from 14 manufacturers across three urban sites in the UK over a three-year period. The resulting open-access dataset captures high time-resolution measurements of a variety of gasses (NO, NO2, O3, CO, CO2), particulate matter (PM1, PM2.5, PM10), and key meteorological parameters (humidity, temperature, atmospheric pressure). The quality and scope of the dataset is enhanced by reference monitors' data and calibrated products from sensor manufacturers across the three sites. This publicly accessible dataset serves as a robust and transparent resource that details the methods used for data collection and procedures to ensure dataset integrity. It provides a valuable tool for a wide range of stakeholders to analyze the performance of air quality sensors in real-world settings. Policymakers can leverage this data to refine sensor deployment guidelines and develop standardized protocols, while manufacturers can utilize it as a benchmark for technological innovation and product certification. Moreover, the dataset has supported the development of a UK code of practice, and the certification of one of the participating companies, underscoring the dataset's utility and reliability.

Particulate matter 1µm (PM1) dataset collected by low-cost sensors in residential and industrial areas at the neighborhood level

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions of particles (PM10, PM2.5, and PM1) allows the characterization of the concentration levels of specific sources or events, including the analysis of ultrafine fractions (PM1). Several studies have documented adverse effects on human health due to exposure to PM1, such as morbidity and mortality from respiratory, cardiovascular, and, in some cases, carcinogenic diseases. Hence, studying the concentration levels and the sources that cause PM1 is imperative. LCS is an alternative to understanding contaminant concentration levels by considering spatial and temporal community dynamics by monitoring critical zones. Furthermore, collecting and managing large amounts of data through automatic processing and analysis generates information to support decision-making to reduce exposure and risks to people's health. The dataset presents the concentration level of PM1 (µg/m3) calculated from the particles of size 0.03 µm, 0.05 µm, and 1.0 µm recorded and counted by the sensor in a sample per minute for 24 h for seven continuous days. The values of the meteorological factors of relative humidity, temperature, and heat index complement these attributes. The dataset comprises records collected (in the same period) at four particulate matter monitoring stations, which compose an LCS network supported by Internet of Things (IoT) technologies. The data collection points were located in different areas of Reynosa, Mexico, considering strategic places for monitoring environmental pollution, such as industrial parks, residential areas, avenues with high vehicular traffic and transportation of heavy cargo, and an airport.

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

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

Associations between community green view index and fine particulate matter from Airboxes

Urban greenery can help to improve air quality, reduce health risks and create healthy livable urban communities. This study aimed to explore the role of urban greenery in reducing air pollution at the community level in Tainan City, Taiwan, using air quality sensors and street-view imagery. We also collected the number of road trees around each air quality sensor site and identified the species that were best at absorbing PM2.5. Three greenness metrics were used to assess community greenery in this study: two Normalized Difference Vegetation Indices (NDVI) from different satellites and the Green View Index (GVI) from Google Street View (GSV) images. Land-use Regression (LUR) was used for statistical analysis. The results showed that a higher GVI within a 500 m buffer was significantly associated with decreased PM2.5. Neither NDVI metrics within a 500 m circular buffer were significantly associated with decreased PM2.5. Evergreen trees were significantly associated with lower ambient PM2.5, compared with deciduous and semi-deciduous trees. Because localized changes in air quality profoundly affect public health and environmental equity, our findings provide evidence for future urban community greenspace planning and its beneficial impacts on reducing air pollution.

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Carbon monoxide as an indicator of indoor air quality

Carbon monoxide is a priority pollutant that is suitable as an indicator for assessing indoor air quality. Monitoring should preferably be embedded in an intelligent network of different sensors.

Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia

Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution. The Washington passive sampler (WPS), an ultra-low-cost method for measuring the long-term average levels of light-absorbing carbon (LAC) air pollution, uses digital images to measure the changes in the reflectance of a passively exposed paper filter. A prior publication on WPS reported high precision and reproducibility. Here, we deployed three methods to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir for PM2.5; two ultrasonic personal aerosol samplers (UPAS) with quartz filters for the thermal-optical analysis of elemental carbon (EC); and two WPS for LAC. We compared multiple rounds of 4-week-average measurements. The analyses calibrating the LAC to the elemental carbon measurement suggest that 1 µg of EC/m3 corresponds to 62 PI/month (R2 = 0.83). The EC-LAC calibration curve indicates an accuracy (root-mean-square error) of 3.1 µg of EC/m3, or ~21% of the average elemental carbon concentration. The RMSE values observed here for the WPS are comparable to the reported accuracy levels for other methods, including reference methods. Based on the precision and accuracy results shown here, as well as the increased simplicity of deployment, the WPS may merit further consideration for studying air quality in homes that use solid fuels.

Advantages of Slow Sensing for Ambient Monitoring: A Practical Perspective

Air pollution is a ubiquitous threat, affecting 99% of the global populace and causing millions of premature deaths annually. Monitoring ambient air quality is essential, aiding policymakers and environmental agencies in timely interventions. This study delves into the advantages of slower gas sensors over their ultrafast counterparts, with a keen focus on their practicality in real-world scenarios. Slow sensors offer accurate time-averaged exposure assessments, harmonizing with established regulatory benchmarks. Their heightened precision and reliability, complemented by their cost-effectiveness, render them eminently suitable for large-scale deployment. The slow sensing ensures compatibility with regulations, fostering robust risk management practices. In contrast, ultrafast sensors, while claiming rapid detection, despite touting swift detection capabilities, grapple with formidable challenges. The sensitivity of ultrafast sensors to uncontrolled atmospheric effects, fluctuations in pressure, rapid response times, and uniform gas dispersion poses significant hurdles to their reliability. Addressing these issues assumes paramount significance in upholding the integrity of air quality assessments.

COVID-19 New Cases and Environmental Factors During Wet and Dry Seasons in West and Southern Africa

Sub-Saharan Africa has been the last continent to experience a significant number of cases in the novel Coronavirus (COVID-19). Studies suggest that air pollution is related to COVID-19 mortality; poor air quality has been linked to cardiovascular, cerebrovascular, and respiratory diseases, which are considered co-morbidities linked to COVID-19 deaths. We examine potential connections between country-wide COVID-19 cases and environmental conditions in Senegal, Cabo Verde, Nigeria, Cote D'Ivorie, and Angola. We analyze PM2.5 concentrations, temperatures from cost-effective in situ measurements, aerosol optical depth (AOD), and fire count and NO2 column values from space-borne platforms from 1 January 2020 through 31 March 2021. Our results show that the first COVID-19 wave in West Africa began during the wet season of 2020, followed by a second during the dry season of 2020. In Angola, the first wave starts during the biomass burning season but does not peak until November of 2020. Overall PM2.5 concentrations are the highest in Ibadan, Nigeria, and coincided with the second wave of COVID-19 in late 2021 and early 2022. The COVID-19 waves in Cabo Verde are not in phase with those in Senegal, Nigeria, and Cote, lagging by several months in general. Overall, the highest correlations occurred between weekly new COVID-19 cases meteorological and air quality variables occurred in the dry season.

Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain

Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 μm suggest that particle size significantly affected the LCSs' response. The LCSs could accurately detect number concentrations for particles smaller than 1 μm in the urban (R² = 0.9) and background sites (R² = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R² = 0.69). However, their performance was rather poor (R² < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R² values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors' capabilities to estimate PM_2.5 and PM₁₀ concentrations, since their size distribution did not agree with the reference instruments.

Impact of building types and CHP plants on air quality (2019-2021) in central-eastern European monocentric agglomeration

The quality of city air is influenced by many factors, including the density of buildings, the roughness of the terrain, the presence of street canyons, the heat sources in buildings, the types of industry, the topography, and meteorological conditions. Official air quality monitoring systems measure a very limited number of points, making local analysis impossible without the use of mathematical modeling programs. Here, we present an analysis of local air quality in an urban agglomeration. Data were collected over three years (2019, 2020, 2021), using commercial sensors located throughout the area of investigation. Dense downtown buildings equipped with individual heat sources were not found to have any impact on local air quality. The local municipal combined heat and power (CHP) plants contributed <1 ‰ of the measured concentration of particulate matter. Land height and the density of single-family housing were found to significantly affect air quality. We also took into account the influence of weather conditions, wind speed, and wind direction on the concentrations of particulate matter. High concentrations of particulate matter occurred only during heating periods when wind speeds were moderate. Wind direction did not have a direct impact on air quality, despite the expected benefits of ventilation through air corridors.

Performance-based protocol for selection of economical portable sensor for air quality measurement

An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS.

Quantifying the dynamic characteristics of indoor air pollution using real-time sensors: Current status and future implication

People generally spend most of their time indoors, making indoor air quality be of great significance to human health. Large spatiotemporal heterogeneity of indoor air pollution can be hardly captured by conventional filter-based monitoring but real-time monitoring. Real-time monitoring is conducive to change air assessment mode from static and sparse analysis to dynamic and massive analysis, and has made remarkable strides in indoor air evaluation. In this review, the state of art, strengths, challenges, and further development of real-time sensors used in indoor air evaluation are focused on. Researches using real-time sensors for indoor air evaluation have increased rapidly since 2018, and are mainly conducted in China and the USA, with the most frequently investigated air pollutants of PM_2.5. In addition to high spatiotemporal resolution, real-time sensors for indoor air evaluation have prominent advantages in 3-dimensional monitoring, pollution peak and source identification, and short-term health effect evaluation. Huge amounts of data from real-time sensors also facilitate the modeling and prediction of indoor air pollution. However, challenges still remain in extensive deployment of real-time sensors indoors, including the selection, performance, stability, as well as calibration of sensors. In future, sensors with high performance, long-term stability, low price, and low energy consumption are welcomed. Furthermore, more target air pollutants are also expected to be detected simultaneously by real-time sensors in indoor air monitoring.

Laboratory and field evaluation of a low-cost methane sensor and key environmental factors for sensor calibration

Low-cost sensors enable finer-scale spatiotemporal measurements within the existing methane (CH4) monitoring infrastructure and could help cities mitigate CH4 emissions to meet their climate goals. While initial studies of low-cost CH4 sensors have shown potential for effective CH4 measurement at ambient concentrations, sensor deployment remains limited due to questions about interferences and calibration across environments and seasons. This study evaluates sensor performance across seasons with specific attention paid to the sensor's understudied carbon monoxide (CO) interferences and environmental dependencies through long-term ambient co-location in an urban environment. The sensor was first evaluated in a laboratory using chamber calibration and co-location experiments, and then in the field through two 8 week co-locations with a reference CH4 instrument. In the laboratory, the sensor was sensitive to CH4 concentrations below ambient background concentrations. Different sensor units responded similarly to changing CH4, CO, temperature, and humidity conditions but required individual calibrations to account for differences in sensor response factors. When deployed in-field, co-located with a reference instrument near Baltimore, MD, the sensor captured diurnal trends in hourly CH4 concentration after corrections for temperature, absolute humidity, CO concentration, and hour of day. Variable performance was observed across seasons with the sensor performing well (R 2 = 0.65; percent bias 3.12%; RMSE 0.10 ppm) in the winter validation period and less accurately (R 2 = 0.12; percent bias 3.01%; RMSE 0.08 ppm) in the summer validation period where there was less dynamic range in CH4 concentrations. The results highlight the utility of sensor deployment in more variable ambient CH4 conditions and demonstrate the importance of accounting for temperature and humidity dependencies as well as co-located CO concentrations with low-cost CH4 measurements. We show this can be addressed via Multiple Linear Regression (MLR) models accounting for key covariates to enable urban measurements in areas with CH4 enhancement. Together with individualized calibration prior to deployment, the sensor shows promise for use in low-cost sensor networks and represents a valuable supplement to existing monitoring strategies to identify CH4 hotspots.

Assessment of aerosol persistence in ICUs via low-cost sensor network and zonal models

The COVID-19 pandemic raised public awareness about airborne particulate matter (PM) due to the spread of infectious diseases via the respiratory route. The persistence of potentially infectious aerosols in public spaces and the spread of nosocomial infections in medical settings deserve careful investigation; however, a systematic approach characterizing the fate of aerosols in clinical environments has not been reported. This paper presents a methodology for mapping aerosol propagation using a low-cost PM sensor network in ICU and adjacent environments and the subsequent development of the data-driven zonal model. Mimicking aerosol generation by a patient, we generated trace NaCl aerosols and monitored their propagation in the environment. In positive (closed door) and neutral-pressure (open door) ICUs, up to 6% or 19%, respectively, of all PM escaped through the door gaps; however, the outside sensors did not register an aerosol spike in negative-pressure ICUs. The K-means clustering analysis of temporospatial aerosol concentration data suggests that ICU can be represented by three distinct zones: (1) near the aerosol source, (2) room periphery, and (3) outside the room. The data suggests two-phase plume behavior: dispersion of the original aerosol spike throughout the room, followed by an evacuation phase where "well-mixed" aerosol concentration decayed uniformly. Decay rates were calculated for positive, neutral, and negative pressure operations, with negative-pressure rooms clearing out nearly twice as fast. These decay trends closely followed the air exchange rates. This research demonstrates the methodology for aerosol monitoring in medical settings. This study is limited by a relatively small data set and is specific to single-occupancy ICU rooms. Future work needs to evaluate medical settings with high risks of infectious disease transmission.

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

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.

PM sensors as an indicator of overall air quality: Pre-COVID and COVID periods

Nowadays, there has been a substantial proliferation in the use of low-cost particulate matter (PM) sensors and facilitating as an indicator of overall air quality. However, during COVID-19 epidemics, air pollution sources have been deteriorated significantly, and given offer to evaluate the impact of COVID-19 on air quality in the world's most polluted city: Delhi, India. To address low-cost PM sensors, this study aimed to a) conduct a long-term field inter-comparison of twenty-two (22) low-cost PM sensors with reference instruments over 10-month period (evaluation period) spanning months from May 2019 to February 2020; b) trend of PM mass and number count; and c) probable local and regional sources in Delhi during Pre-CVOID (P-COVID) periods. The comparison of low-cost PM sensors with reference instruments results found with R² ranging between 0.74 and 0.95 for all sites and confirm that PM sensors can be a useful tool for PM monitoring network in Delhi. Relative reductions in PM_2.5 and particle number count (PNC) due to COVID-outbreaks showed in the range between (2-5%) and (4-13%), respectively, as compared to the P-COVID periods. The cluster analysis reveals air masses originated ∼52% from local, while ∼48% from regional sources in P-COVID and PM levels are encountered 47% and 66-70% from local and regional sources, respectively. Overall results suggest that low-cost PM sensors can be used as an unprecedented aid in air quality applications, and improving non-attainment cities in India, and that policy makers can attempt to revise guidelines for clean air.

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework

BACKGROUND

Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.

OBJECTIVES

To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.

METHODS

We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations.

RESULTS

The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.

DISCUSSION

The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Feasibility of low-cost particle sensor types in long-term indoor air pollution health studies after repeated calibration, 2019-2021

Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

Evaluating the applicability of the ratio of PM2.5 and carbon monoxide as source signatures

Air pollution is among the top risk faced by people around the world, and therefore combating it is among the top priorities. It begins with identifying the sources that contribute the most to local air pollution to prioritize their control. There are advanced methods for source identification and apportionment, but such methods are not available in many low-income countries and not everywhere in all high-income countries. We propose a simplified method by using source the signatures to help obtain information about the local source contribution if no other methods are available. Using low-cost monitors, particle mass (PM_2.5) and carbon monoxide (CO) concentrations were measured and the ratio of CO/PM_2.5 was determined. We investigated outdoor and indoor sources, including vehicular exhaust, combustion of biomass, incense and mosquito coil burning, and cigarette smoking. The results show that the ratios differed significantly between certain pollutant sources. Compressed natural gas (CNG) engines have a high ratio (mean value of 972 ± 419), which is attributed to relatively low PM_2.5 emissions, while ship emissions and cigarette smoke recorded a relatively low ratio. Most traffic emissions recorded higher ratios than those of bushfire emissions, and ratios of most outdoor pollutant sources were much higher than those of indoor pollutant sources. There is a clear trend for ratios to decrease from high to low for CNG, petrol, diesel for buses, and fuel for ships. Our results suggest that the ratio of CO/PM_2.5 can be used as an effective method to identify pollution sources.

A Simple Optical Aerosol Sensing Method of Sauter Mean Diameter for Particulate Matter Monitoring

Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.

Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors

This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse resolution and poor selectivity limit their performance. Thus, we adopted recurrent neural network (RNN) models to extract the time-series concentration data characteristics and improve the detection accuracy. Firstly, four types of RNN models, LSTM and GRU, Bi-LSTM, and Bi-GRU, were optimized to define the best-performance single weak models for CO, O₃, and NO₂ gases, respectively. Next, ensemble models which integrate multiple single weak models with a dynamic model were defined and trained. The testing results show that the ensemble models perform better than the single weak models. Further, a retraining procedure was proposed to make the ensemble model more flexible to adapt to environmental conditions. The significantly improved determination coefficients show that the retraining helps the ensemble models maintain long-term stable sensing performance in an atmospheric environment. The result can serve as an essential reference for the applications of IoT devices with commercial gas sensors in environment condition monitoring.

Investigating the Sources of Urban Air Pollution Using Low-Cost Air Quality Sensors at an Urban Atlanta Site

Advances in low-cost sensors (LCS) for monitoring air quality have opened new opportunities to characterize air quality in finer spatial and temporal resolutions. In this study, we deployed LCS that measure both gas (CO, NO, NO₂, and O₃) and particle concentrations and co-located research-grade instruments in Atlanta, GA, to investigate the capability of LCS in resolving air pollutant sources using non-negative matrix factorization (NMF) in a moderately polluted urban area. We provide a comparison of applying the NMF technique to both normalized and non-normalized data sets. We identify four factors with different temporal trends and properties for both normalized and non-normalized data sets. Both normalized and non-normalized LCS data sets can resolve primary organic aerosol (POA) factors identified from research-grade instruments. However, applying normalization provides factors with more diverse compositions and can resolve secondary organic aerosol (SOA). Results from this study demonstrate that LCS not only can be used to provide basic mass concentration information but also can be used for in-depth source apportionment studies even in an urban setting with complex pollution mixtures and relatively low aerosol loadings.

Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations. The models for all five pollutants produced high Pearson correlation coefficients (r > 0.85), and the hourly averaged calibrated sensor and reference concentrations from the evaluation period were within 3-12%. Each sensor required a distinct set of predictors to achieve the lowest possible root-mean-square error (RMSE). All five sensors responded to environmental factors, and three sensors exhibited cross-sensitives to another air pollutant. We compared the RMSE from models (NO₂, O₃, and NO) that used colocated regulatory instruments and colocated sensors as predictors to address the cross-sensitivities to another gas, and the corresponding model RMSEs for the three gas models were all within 0.5 ppb. This indicates that low-cost sensor networks can yield useable data if the monitoring package is designed to comeasure key predictors. This is key for the utilization of low-cost sensors by diverse audiences since this does not require continual access to regulatory grade instruments.

Improving the Quality of Measurements Made by Alphasense NO2 Non-Reference Sensors Using the Mathematical Methods

Conventional NO₂ monitoring devices are relatively cumbersome, expensive, and have a relatively high-power consumption that limits their use to fixed sites. On the other hand, they offer high-quality measurements. In contrast, the low-cost NO₂ sensors offer greater flexibility, are smaller, and allow greater coverage of the area with the measuring devices. However, their disadvantage is much lower accuracy. The main goal of this study was to investigate the measurement data quality of NO2-B43F Alphasense sensors. The measurement performance analysis of Alphasense NO2-B43F sensors was conducted in two research areas in Poland. Sensors were placed near fixed, professional air quality monitoring stations, carrying out measurements based on reference methods, in the following periods: July-November, and December-May. Results of the study show that without using sophisticated correction methods, the range of measured air pollution concentrations may be greater than their actual values in ambient air-measured in the field by fixed stations. In the case of summer months (with air temperature over 30 °C), the long-term mean absolute percentage error was over 150% and the sensors, using the methods recommended by the manufacturer, in the case of high temperatures could even show negative values. After applying the mathematical correction functions proposed in this article, it was possible to significantly reduce long-term errors (to 40-70% per month, regardless of the location of the measurements) and eliminate negative measurement values. The proposed method is based on the recalculation of the raw measurement, air temperature, and air RH and does not require the use of extensive analytical tools.

Development and field deployment of a mid-infrared CO and CO2 dual-gas sensor system for early fire detection and location

In order to realize early fire detection and location, a mid-infrared carbon monoxide (CO) and carbon dioxide (CO₂) dual-gas sensor system was developed, which mainly includes a gas pretreatment module, a CO₂ sensor module, a CO sensor module, and a laptop monitoring platform. CO₂ and CO absorption lines located at 4.26 μm and 4.66 μm, respectively, were selected to ensure good selectivity of the sensor system. A series of experiments were carried out to evaluate the sensor performance. The 10-90% response time of the CO and CO₂ sensor modules was measured to be ∼ 30 s at a flow rate of 1 L/min, and the limits of detection (LoD) of CO₂ and CO were assessed to be 5.66 parts per million by volume (ppmv) and 0.94 ppmv, respectively, when the averaging time was 0.25 s. According to the correlation between CO₂ and CO concentration in the early fire stage, a method of early fire detection was studied and proposed using the normalized concentration ratio between CO and CO₂ (C(CO)/C(CO₂)) as the key alarm parameter. Based on gas turbulent diffusion (GTD) model combined with particle swarm optimization (PSO) algorithm, a mobile early fire location method was presented. Correlative experiment results verified that the reported sensor system has a good performance for early fire detection and location.

Characterization of Ambient Air Quality in Selected Urban Areas in Uganda Using Low-Cost Sensing and Measurement Technologies

Air pollution is prevalent in cities and urban centers in developing countries including sub-Saharan Africa, but ground monitoring data on local pollution remain inadequate, hindering effective mitigation. We employed low-cost sensing and measurement technologies to quantify pollution levels based on particulate matter (PM_2.5), NO₂, and O₃ over a 6 month period for selected urban centers in three of the four macroregions in Uganda. PM_2.5 diurnal profiles exhibited consistent patterns across all monitoring locations with higher pollution levels manifesting from 18:00 to 00:00 and from 06:00 to 09:00; while the periods from 00:00 to 05:00 and from 09:00 to 17:00 had the lowest levels. Daily PM_2.5 varied widely between 34 and 107 μg/m³ over a 7 day period, well within unhealthy levels (55.5-150.4 μg/m³) for short-term exposure. The inconsistent daily trend are instructive for multiple pollutant assessment to aid specific policy initiatives. The results also show inverse relations between seasonal particulate levels and precipitation, that is, R (correlation coefficient) = -0.93 and -0.62 for Kampala and Wakiso, R = -0.49 and -0.44 for the Eastern region, and R = -0.65 and -0.96 for the Western region. NO₂ monthly concentrations replicated PM_2.5 spatial levels, whereas O₃ exhibited inverse relations probably due to a higher retention time in less-urbanized environments. Both PM_2.5 and NO₂ correlated positively with the resident population. Our findings show significant spatiotemporal variations and exceedances of health guidelines by about 4-6 times across most study locations (with two exceptions) for longer-term exposure. This paper demonstrably highlights the practicability and potential of low-cost approaches for air quality monitoring, with strong prospects for citizen science. This paper also provides novel information regarding air pollution that is needed to improve control strategies for reducing exposures.

Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale

Given the growing interest in community air quality monitoring using low-cost sensors, 30 PurpleAir II sensors (12 outdoor and 18 indoor) were deployed in partnership with community members living adjacent to a major interstate freeway from December 2017- June 2019. Established quality assurance/quality control techniques for data processing were used and sensor data quality was evaluated by calculating data completeness and summarizing PM_2.5 measurements. To evaluate outdoor sensor performance, correlation coefficients (r) and coefficients of divergence (CoD) were used to assess temporal and spatial variability of PM_2.5 between sensors. PM_2.5 concentrations were also compared to traffic levels to assess the sensors' ability to detect traffic pollution. To evaluate indoor sensors, indoor/outdoor (I/O) ratios during resident-reported activities were calculated and compared, and a linear mixed-effects regression model was developed to quantify the impacts of ambient air quality, microclimatic factors, and indoor human activities on indoor PM_2.5. In general, indoor sensors performed more reliably than outdoor sensors (completeness: 73% versus 54%). All outdoor sensors were highly temporally correlated (r > 0.98) and spatially homogeneous (CoD<0.06). The observed I/O ratios were consistent with existing literature, and the mixed-effects model explains >85% of the variation in indoor PM_2.5 levels, indicating that indoor sensors detected PM_2.5 from various sources. Overall, this study finds that community-maintained sensors can effectively monitor PM_2.5, with main data quality concerns resulting from outdoor sensor data incompleteness.

The Role of Meteorological Variables and Aerosols in the Transmission of COVID-19 During Harmattan Season

The role of atmospheric parameters and aerosols in the transmission of COVID-19 within tropical Africa, especially during the harmattan season, has been under-investigated in published papers. The harmattan season within the West African region is associated with significant dust incursion from the Bodele depression and biomass burning. In this study, the correlation between atmospheric parameters (temperature and humidity) and aerosols with COVID-19 cases and fatalities within seven locations in tropical Nigeria during the harmattan period was investigated. COVID-19 infection cases were found to be significantly positively correlated with atmospheric parameters (temperature and humidity) in the southern part of the country while the number of fatalities showed weaker significant correlation with particulate matters only in three locations. The significant correlation values were found to be between 0.22 and 0.48 for particulate matter and -0.19 to -0.32 for atmospheric parameters. Although, temperature and humidity showed negative correlations in some locations, the impact is smaller compared to particulate matter. In December, COVID-19 cases in all locations showed strong correlation with particulate matter except in Kano State. It is suggested that a reduction in atmospheric particulate matter can be used as a control measure for the spread of COVID-19.

Key Concerns and Drivers of Low-Cost Air Quality Sensor Use

Low-cost sensors are revolutionizing air pollution monitoring by providing real-time, highly localized air quality information. The relatively low-cost nature of these devices has made them accessible to the broader public. Although there have been several fitness-of-purpose appraisals of the various sensors on the market, little is known about what drives sensor usage and how the public interpret the data from their sensors. This article attempts to answer these questions by analyzing the key themes discussed in the user reviews of low-cost sensors on Amazon. The themes and use cases identified have the potential to spur interventions to support communities of sensor users and inform the development of actionable data-visualization strategies with the measurements from such instruments, as well as drive appropriate ‘fitness-of-purpose’ appraisals of such devices.

Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.

Air quality biomonitoring through Olea europaea L.: The study case of "Land of pyres"

The "Land of pyres", namely "La Terra dei Fuochi", is an area of Campania region (South-Italy), highly inhabited and comprises between the Provinces of Naples and Caserta, sadly known worldwide for the criminal activities related to the illegal waste disposal and burning. These fires, concomitantly with traffic emissions, might be the source of potential toxic element (PTE) dangerous for the human health and causing pathologies. In the framework of Correlation Health-Environment project, funded by the Campania region, eight municipalities (of area "Land of pyres") and three remote sites have been bio-monitored using the olive (Olea europaea L.) plants as biomonitors. Leaves of olive plants were collected in each assayed municipality and the concentration of 11 metal(loid)s was evaluated by means of ICP-OES. Our findings revealed that the air of these municipalities was limitedly contaminated by PTE; in fact, only Sb, Al and Mn were detected in the olive leaves collected in some of the assayed municipalities and showed a high enrichment factors (EC) manly due, probably, to the vehicular traffic emissions. Furthermore, the concentrations of the other assayed PTEs were lower than those of Sb, Al and Mn. For these reasons we suppose that their emissions in the troposphere have been and are limited, and they mainly have a crustal origin. Even if our data are very comforting for those urban area, regarded by many as one of the most contaminated one in Italy, a great environment care, in any case, is always needed.

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM2.5 concentrations (r2 = 0.93-0.99) and good accuracy (mean absolute error (MAE) < 2 μg m-3). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r2 = 0.87-95) and good accuracy (MAE of 2.5-3.0 μg m-3). The PM2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied.

Ambient PM2.5 and PM10 Exposure and Respiratory Disease Hospitalization in Kandy, Sri Lanka

Evidence of associations between exposure to ambient air pollution and health outcomes are sparse in the South Asian region due to limited air pollution exposure and quality health data. This study investigated the potential impacts of ambient particulate matter (PM) on respiratory disease hospitalization in Kandy, Sri Lanka for the year 2019. The Generalized Additive Model (GAM) was applied to estimate the short-term effect of ambient PM on respiratory disease hospitalization. As the second analysis, respiratory disease hospitalizations during two distinct air pollution periods were analyzed. Each 10 μg/m3 increase in same-day exposure to PM2.5 and PM10 was associated with an increased risk of respiratory disease hospitalization by 1.95% (0.25, 3.67) and 1.63% (0.16, 3.12), respectively. The effect of PM2.5 or PM10 on asthma hospitalizations were 4.67% (1.23, 8.23) and 4.04% (1.06, 7.11), respectively (p < 0.05). The 65+ years age group had a higher risk associated with PM2.5 and PM10 exposure and hospital admissions for all respiratory diseases on the same day (2.74% and 2.28%, respectively). Compared to the lower ambient air pollution period, higher increased hospital admissions were observed among those aged above 65 years, males, and COPD and pneumonia hospital admissions during the high ambient air pollution period. Active efforts are crucial to improve ambient air quality in this region to reduce the health effects.

CdO-ZnO nanorices for enhanced and selective formaldehyde gas sensing applications

This paper reports synthesis, properties and gas sensing applications of ZnO nanoflowers and CdO-ZnO nanorices prepared by hydrothermal process. The morphological characterizations confirmed the formation of well-defined nanoflowers and nanorices structures for ZnO and CdO-ZnO nanomaterials, respectively. The structural properties revealed the wurtzite hexagonal phase of the synthesized materials. The sensor devices based on ZnO nanoflowers and CdO-ZnO nanorices were fabricated and tested towards various gases including ethanol, methanol, ammonia, carbon monoxide, methane and formaldehyde. The fabricated gas sensor based on CdO-ZnO nanorices exhibited a high response (34.5) towards 300 ppm formaldehyde gas at 350 °C compared to ZnO nanoflowers (14.5) under the same experimental conditions. The response and recovery times for ZnO nanoflowers-based sensor were~9.8 s and ~6 s while for CdO-ZnO based sensor, these were ~10s and ~6s, respectively. A rapid response (34.5) for CdO-ZnO nanorices based formaldehyde gas sensor was observed as compared to other gases such as ammonia (12.3), methanol (16.5), ethanol (20), carbon monoxide (16.3) and methane (12.4), which confirm the high-selectivity towards formaldehyde gas. Finally, a plausible formaldehyde gas sensing mechanism is proposed.