Low-cost Air Quality Sensors Research Articles

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models.While, due to the non-linear relationship to reference instruments, fine particulate matter (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> ) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) does not present correlation to reference instrument. As a result, the LCS for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is not feasible to be calibrated. Hence, to estimate the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration,mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

Development of a multiple regression model to calibrate a low-cost sensor considering reference measurements and meteorological parameters.

Low-cost air quality sensors are widely used to improve temporal and spatial resolution of air quality data. In Lima, Peru, only a limited number of reference air quality monitors have been installed, which has led to a lack of data for establishing environmental and health policies. Low-cost technology is promising for developing countries because it is small and inexpensive to operate and maintain. However, considerable work remains to be done to improve data quality. In this study, a low-cost sensor was installed with a reference monitor station as the first stage for the calibration process, and a multiple regression model was developed based on reference measurements as an outcome variable using sensor data, temperature, and relative humidity as the predictive parameters. The results show that this particular technology exhibits a promising performance in measuring PM2.5 and PM10 (particulate matter with diameter aerodynamic less than 2.5μm and 10μm, respectively); however, the correlation for PM2.5 appears to be better. Temperature and relative humidity data from the sensor were only partially analyzed due to the evident low correlation with the reference meteorological data. The objective of this study is to begin analyzing the performance of low-cost sensors that have already been introduced to the Peruvian market and selecting those that perform better to provide for informed decision-making.

Translational data analytics in exposure science and environmental health: a citizen science approach with high school students

BackgroundTranslational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline.MethodsWe implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities.ResultsThe citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx.ConclusionsInitial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships.

Baseline drift estimation for air quality data using quantile trend filtering

We address the problem of estimating smoothly varying baseline trends in time series data. This problem arises in a wide range of fields, including chemistry, macroeconomics and medicine; however, our study is motivated by the analysis of data from low cost air quality sensors. Our methods extend the quantile trend filtering framework to enable the estimation of multiple quantile trends simultaneously while ensuring that the quantiles do not cross. To handle the computational challenge posed by very long time series, we propose a parallelizable alternating direction method of multipliers (ADMM) algorithm. The ADMM algorthim enables the estimation of trends in a piecewise manner, both reducing the computation time and extending the limits of the method to larger data sizes. We also address smoothing parameter selection and propose a modified criterion based on the extended Bayesian information criterion. Through simulation studies and our motivating application to low cost air quality sensor data, we demonstrate that our model provides better quantile trend estimates than existing methods and improves signal classification of low-cost air quality sensor output.

Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors

The concentrations of fine and coarse fractions of airborne particulate matter (PM) and meteorological variables (wind speed, wind direction, temperature and relative humidity) were measured at six selected locations in Ile Ife, a prominent university town in Nigeria using a network of low-cost air quality (AQ) sensor units. The objective of the deployment was to collate baseline air quality data and assess the impact of prevailing meteorological conditions on PM concentrations in selected residential communities downwind of an iron smelting facility. The raw data obtained from OPC-N2 of the AQ sensor units was corrected using the RH correction factor developed based k-Kohler theory. This PM (corrected) fast time resolution data (20 s) from the AQ sensor units were used to create daily averages. The overall mean mass concentrations for PM2.5 and PM10 were 213.3, 44.1, 23.8, 27.7, 20.2 and 41.5 μg/m3 and; 439.9, 107.1, 55.0, 72.4, 45.5 and 112.0 μg/m3 for Fasina (Iron-Steel Smelting Factory, ISSF), Modomo, Eleweran, Fire Service, O.A.U. staff quarters and Obafemi Awolowo University Teaching and Research Farm (OAUTRF), respectively. PM concentration and wind speed showed a negative exponential distribution curve with the lowest exponential fit coefficient of determination (R2) values of 0.08 for PM2.5 and 0.03 for PM10 during nighttime periods at Eleweran and Fire service sites, respectively. The relationship between PM concentration and temperature gave a decay curve indicating that higher PM concentrations were observed at lower temperatures. The exponential distribution curve for the relationship between PM concentration and relative humidity (RH) showed that PM concentrations do not vary for RH < 80 % while stronger relationship was noticed with higher PM concentration for RH > 80 % for both day and nighttime. The performances of the MLR model were slightly poor and as such not too reliable for predicting the concentration but useful for improving predictive model accuracy when other variables contributing to the variability of PM is considered. The study concluded that the anthropogenic and industrial activities at the smelting factory contribute significantly to the elevated PM mass concentration measured at the study locations.

Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tušimice Observatory

With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.

Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

Abstract PM2.5 poses a serious threat to public health, however its spatial concentrations are not well characterized due to the sparseness of regulatory air quality monitoring (AQM) stations. This motivates novel low-cost methods to estimate ground-level PM2.5 at a fine spatial resolution so that PM2.5 exposure in epidemiological research can be better quantified. Satellite-retrieved aerosol products are widely used to estimate the spatial distribution of ground-level PM2.5. However, these aerosol products can be subject to large uncertainties due to many approximations and assumptions made in multiple stages of their retrieval algorithms. Therefore, estimating ground-level PM2.5 directly from satellites (e.g. satellite images) by skipping the intermediate step of aerosol retrieval can potentially yield lower errors because it avoids retrieval error propagating into PM2.5 estimation and is desirable compared to current ground-level PM2.5 retrieval methods. Additionally, the spatial resolutions of estimated PM2.5 are usually constrained by those of the aerosol products and are currently largely at a comparatively coarse 1 km or greater resolution. Such coarse spatial resolutions are unable to support scientific studies that thrive on highly spatially-resolved PM2.5. These limitations have motivated us to devise a computer vision algorithm for estimating ground-level PM2.5 at a high spatiotemporal resolution by directly processing the global-coverage, daily, near real-time updated, 3 m/pixel resolution, three-band micro-satellite imagery of spatial coverages significantly smaller than 1 × 1 km (e.g., 200 × 200 m) available from Planet Labs. In this study, we employ a deep convolutional neural network (CNN) to process the imagery by extracting image features that characterize the day-to-day dynamic changes in the built environment and more importantly the image colors related to aerosol loading, and a random forest (RF) regressor to estimate PM2.5 based on the extracted image features along with meteorological conditions. We conducted the experiment on 35 AQM stations in Beijing over a period of ~3 years from 2017 to 2019. We trained our CNN-RF model on 10,400 available daily images of the AQM stations labeled with the corresponding ground-truth PM2.5 and evaluated the model performance on 2622 holdout images. Our model estimates ground-level PM2.5 accurately at a 200 m spatial resolution with a mean absolute error (MAE) as low as 10.1 μg m−3 (equivalent to 23.7% error) and Pearson and Spearman r scores up to 0.91 and 0.90, respectively. Our trained CNN from Beijing is then applied to Shanghai, a similar urban area. By quickly retraining only RF but not CNN on the new Shanghai imagery dataset, our model estimates Shanghai 10 AQM stations' PM2.5 accurately with a MAE and both Pearson and Spearman r scores of 7.7 μg m−3 (18.6% error) and 0.85, respectively. The finest 200 m spatial resolution of ground-level PM2.5 estimates from our model in this study is higher than the vast majority of existing state-of-the-art satellite-based PM2.5 retrieval methods. And our 200 m model's estimation performance is also at the high end of these state-of-the-art methods. Our results highlight the potential of augmenting existing spatial predictors of PM2.5 with high-resolution satellite imagery to enhance the spatial resolution of PM2.5 estimates for a wide range of applications, including pollutant emission hotspot determination, PM2.5 exposure assessment, and fusion of satellite remote sensing and low-cost air quality sensor network information.

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

Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM2.5 and an Alphasense CO–B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on the performance of the monitors, the responses of the PM2.5 sensors to different types of aerosols, and the long-term stability of the PM2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM2.5 (0.44< R2 < 0.91) and CO (0.37< R2 < 0.90). The monitors performed well at relative humidity < 75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.

Citizen-Based Air Quality Monitoring: The Impact on Individual Citizen Scientists and How to Leverage the Benefits to Affect Whole Regions

Air pollution is a serious problem that is causing increasing concern among European citizens. It is responsible for more than 400,000 premature deaths in Europe each year and considerably damages human health, agriculture, and the natural environment. Despite these facts, the readiness and power of citizens to take actions is limited. To address this challenge, the citizen science project CAPTOR was launched in 2016. Using low-cost measurement devices, citizens in three European testbeds supported the monitoring of tropospheric ozone. This paper presents the results from 53 interviews with involved residents and shows that the active involvement of individuals in a complex process such as measuring tropospheric ozone can have important impacts on their knowledge and attitudes. In an attempt to expand the benefits of low-cost air quality sensors from an individual to a regional level, certain preconditions are key. Strong support in assuring data quality, visibility of the collected data in online and offline media, broad dissemination of results, and intensified communication with political decision-makers are needed.

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

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

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

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

Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5

Airborne particulate matter (PM) mass concentrations measured by conventional monitoring stations are reliable data sources for air quality communication, pollution mapping, and exposure estimation. A high spatiotemporal density of monitoring stations can provide a better understanding of PM transport on a regional and global scale and help to reduce exposure misclassification leading to a better assessment of the health impacts associated with PM exposure. However, due to the cost and operational complexities, only a limited number of such PM monitors can be deployed. Apart from conventional measurements, PM mass concentration can be estimated from aerosol optical depth (AOD) data observations by using empirical, semi-empirical or modeling methods, but these datasets are usually compromised by weather conditions and lack of knowledge of aerosol properties. In addition to the above methods, a network of low-cost PM sensors is also a promising approach to increase the measurement density. In this study, we propose an integration of information from multiple measurement approaches. We demonstrate this approach by synergizing the data from 75 monitoring stations, 2,363 AirBox low-cost sensors (the amount of data entries is ∼10 million), and the Terra remote sensing satellite to estimate surface concentrations of PM for Taiwan Main Island during July 14 2018 to Oct 31 in 2018. A machine learning method selects the useful data from the low-cost sensor datasets, and the ordinary Kriging method is used to create a visual daily PM distribution map. The integration of datasets can enhance the overall data quantity and quality, leading to more accurate pollution maps with greater detail. The maps created from these three data sources demonstrate an approximate 30-fold synergistic improvement in the spatial resolution of PM mapping. The Root Mean Square Error (RMSE) of the predicted maps was analyzed through leave-one-out cross-validation, ten-fold cross-validation, and standard data validation. It shows that including low-cost PM sensor data brings in greater detail and largely enhances the spatial distribution while maintaining the pollution mapping characteristics. The approach described here will greatly assist the validation of PM transport models and enhance the accuracy of exposure estimations in future studies.

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale.

Low-cost air quality sensors are promising supplements to regulatory monitors for PM2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM2.5 estimates. The calibration reduced PurpleAir's systematic bias to ∼0 μg/m3 and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R2 of 0.86, an improved spatial CV R2 of 0.81, and a lower prediction error. The temporal CV R2 did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM2.5 spatial details and hotspots.

Utility of a Low-Cost, Dense Sensor Network for the Study of Air Quality Impact Upon Human Health in Urban and Rural Areas.

The chemical composition of air changes from moment to moment. While the atmosphere may appear clear and cloudless to the human eye, gases, aerosols, and particulates are in constant interaction with Earth's surface under the influence of meteorological conditions. The recent emergence of low-cost, dense environmental air quality monitoring networks suggests growing interest in highly granular temporospatial exposure assessments by scientists and citizens. This chapter describes the utility of leveraging partnerships and resources to collocate a dense network of low-cost air quality sensors with meteorological sensors across a predominantly rural state located in the southeastern U.S. Construction of the network will improve knowledge on the daily, diurnal, and seasonal variations of pollutant exposures in rural and urban areas, the public health impact of extreme climatological and atmospheric events, and socioeconomic factors that heighten risk of exposures and health outcomes.

Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea

Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models – linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms – which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution ‘hotspots.’ The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.

Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives

Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Governmental Organisations (NGOs), and academic researchers to explore how we can collaborate and better leverage each other’s work. This statement captures our joint findings and recommendations. Our key observations include: Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics. Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics.

A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California.

Ambient particulate matter (PM) pollution is a major environmental health risk in urban areas. Dense networks of low-cost air quality sensors are emerging to characterize the spatially heterogeneous concentrations that are typical of urban settings, but are not adequately captured using traditional regulatory monitors at central sites. In this study, we present the 100×100 BC Network, a 100-day deployment of low-cost black carbon (BC) sensors across 100 locations in West Oakland, California. This 15 km2 community is surrounded by freeways and affected by emissions associated with local port and industrial activities. We assess the reliability of the sensor hardware and data collection systems, and identify modes of failure to both quantify and qualify network performance. We illustrate how dynamic, local emission sources build upon background BC concentrations. BC concentrations varied sharply over short distances (∼100 m) and timespans (∼1 hour), depending on surrounding land use, traffic patterns, and downwind distance from pollution sources. Strong BC concentration fluctuations were periodically observed over the diurnal and weekly cycles, reflecting the impact of localized traffic emissions and industrial facilities in the neighborhood. Overall, the results demonstrate how distributed sensor networks can reveal the complex spatiotemporal dynamics of combustion-related air pollution within urban neighborhoods.

Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives

Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Governmental Organisations (NGOs), and academic researchers to explore how we can collaborate and better leverage each other’s work. This statement captures our joint findings and recommendations. Our key observations include: Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics. Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics.

Calibration of low-cost NO2 sensors in an urban air quality network

Low-cost air quality sensors measuring air quality at fine spatio-temporal resolutions, typically suffer from sensor drift and interference. Field calibration is typically performed at one location, while little is known about the spatial transferability of correction factors. We evaluated three calibration methods using a year of hourly nitrogen dioxide (NO2) observations from low-cost sensors, collocated at two sites with a conventional monitor as reference: (1) an iterative Bayesian approach for daily estimation of the parameters in a multiple linear regression model, (2) a daily updated correction factor and (3) a correction factor updated only when concentrations are uniformly low. We compared the performance of the calibration methods in terms of temporal stability, spatial transferability, and sensor specificity. We documented drift within the 1-year period. The correction factor updated under uniformly low concentrations performed poorly. The iterative Bayesian approach and daily correction factor reduced the root mean squared error (RMSE) by 21–46% at the calibration locations, but did not reduce RMSE at the other location. By examining the posterior distributions of the regression coefficients, we found that the poor spatial transferability is consistent with different responses of individual sensors to environmental factors. We conclude that the spatial and temporal variability in the calibration parameters requires them to be updated regularly, including sensor-specific recalibrations.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps.

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of “low cost air quality monitors” (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.