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

How to use efficiently airborne criteria pollutants and radon-222 in source apportionment: A self-organizing maps approach

Pollutant source apportionment represents one of the fundamental activities in environmental science. Several efficient chemometric tools are available to the scope, mostly based on multivariate techniques and usually applied to aerosol chemical speciation data. In the present work, an alternative source profiling method is proposed, based on the self-organizing maps (SOM) algorithm. Moreover, the dataset used includes typical criteria pollutants and physical parameters related to airborne particulate matter widely used as a complement of aerosol source apportionment and largely available at a higher time resolution than bulk aerosol samplings, allowing the information on the dynamic behavior of the local airshed to be extended. In this work, data was collected at a coastal location in NW Italy, between January and July 2012. Hourly concentrations of typical gaseous pollutants (SO2, NO, NO2, benzene, toluene, (m-p)-xylene, o-xylene), black-carbon and particle number concentrations by an optical particle sizer (OPS) were collected. The dataset was integrated with radon-222 activity concentration and meteorological parameters to enrich and refine the information obtained by SOM computation as well as to improve the air pollution source localization. Despite the lower specificity of criteria pollutants, the approach developed was capable of revealing distinct pollution sources such as the urban background traffic, the coal-fired power plant active at the time of the study, and the harbor, in agreement with previous PM-based source apportionment studies carried out locally, while enlightening peculiar dynamical conditions detectable at the sub-daily time scale. The application of the SOM algorithm, with the integration of meteorological parameters and atmospheric radon, proved to be very efficient in unveiling the air pollution sources.

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

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

Spatial Distribution of PM2.5 Mass and Number Concentrations in Paris (France) from the Pollutrack Network of Mobile Sensors during 2018-2022

The presence of particulate matter smaller than 2.5 µm in diameter (PM2.5) in ambient air has a direct pejorative effect on human health. It is thus necessary to monitor the urban PM2.5 values with high spatial resolution to better evaluate the different exposure levels that the population encounters daily. The Pollutrack network of optical mobile particle counters on the roofs of hundreds of vehicles in Paris was used to produce maps with a 1 km2 resolution (108 squares to cover the Paris surface). The study was conducted during the 2018-2022 period, showing temporal variability due to different weather conditions. When averaging all the data, the highest air pollution was found along the Paris motorway ring. Also, the mean mass concentrations of PM2.5 pollution increased from southwest to northeast, due to the typology of the city, with the presence of canyon streets, and perhaps due to the production of secondary aerosols during the transport of airborne pollutants by the dominant winds. The number of days above the new daily threshold of 15 µg.m-3 recommended by the WHO in September 2021 varies from 3.5 to 7 months per year depending on the location in Paris. Pollutrack sensors also provide the number concentrations for particles greater than 0.5 µm. Using number concentrations of very fine particles instead of mass concentrations corresponding to the dry residue of PM2.5 is more representative of the pollutants citizens actually inhale. Some recommendations for the calibration of the sensors used to provide such number concentrations are given. Finally, the consequences of such pollution on human health are discussed.

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020

In this paper, a comparative analysis between the PM2.5 concentration in downtown Quito, Ecuador, during the COVID-19 pandemic in 2020 and the previous five years (from 2015 to 2019) was carried out. Here, in order to fill in the missing data and achieve homogeneity, eight datasets were constructed, and 35 different estimates were used together with six interpolation methods to put in the estimated value of the missing data. Additionally, the quality of the estimations was verified by using the sum of squared residuals and the following correlation coefficients: Pearson's r, Kendall's τ, and Spearman's ρ. Next, feature vectors were constructed from the data under study using the wavelet transform, and the differences between feature vectors were studied by using principal component analysis and multidimensional scaling. Finally, a robust method to impute missing data in time series and characterize objects is presented. This method was used to support the hypothesis that there were significant differences between the PM2.5 concentration in downtown Quito in 2020 and 2015-2019.

Band-Sensitive Calibration of Low-Cost PM2.5 Sensors by LSTM Model with Dynamically Weighted Loss Function

Particulate matter has become one of the major issues in environmental sustainability, and its accurate measurement has grown in importance recently. Low-cost sensors (LCS) have been widely used to measure particulate concentration, but concerns about their accuracy remain. Previous research has shown that LCS data can be successfully calibrated using various machine learning algorithms. In this study, for better calibration, dynamic weight was introduced to the loss function of the LSTM model to amplify the loss, especially in a specific band. Our results showed that the dynamically weighted loss function resulted in better calibration in the specific band, where the model accepts the loss more sensitively than outside of the band. It was also confirmed that the dynamically weighted loss function can improve the calibration of the LSTM model in terms of both overall performance and local performance in bands. In a test case, the overall calibration performance was improved by about 12.57%, from 3.50 to 3.06, in terms of RMSE. The local calibration performance in the band improved from 4.25 to 3.77. Such improvements were achieved by varying coefficients of the dynamic weight. The results from different bands also indicated that having more data in a band will guarantee better improvement.

Seasonal Changes in Urban PM2.5 Hotspots and Sources from Low-Cost Sensors

PM2.5 concentrations in urban areas are highly variable, both spatially and seasonally. To assess these patterns and the underlying sources, we conducted PM2.5 exposure measurements at the adult breath level (1.6 m) along three ~5 km routes in urban districts of Mainz (Germany) using portable low-cost Alphasense OPC-N3 sensors. The survey took place on five consecutive days including four runs each day (38 in total) in September 2020 and March 2021. While the between-sensor accuracy was tested to be good (R² = 0.98), the recorded PM2.5 values underestimated the official measurement station data by up to 25 µg/m3. The collected data showed no consistent PM2.5 hotspots between September and March. Whereas during the fall, the pedestrian and park areas appeared as hotspots in >60% of the runs, construction sites and a bridge with high traffic intensity stuck out in spring. We considered PM2.5/PM10 ratios to assign anthropogenic emission sources with high apportionment of PM2.5 in PM10 (>0.6), except for the parks (0.24) where fine particles likely originated from unpaved surfaces. The spatial PM2.5 apportionment in PM10 increased from September (0.56) to March (0.76) because of a pronounced cooler thermal inversion accumulating fine particles near ground. Our results showed that highly resolved low-cost measurements can help to identify PM2.5 hotspots and be used to differentiate types of particle sources via PM2.5/PM10 ratios.

Evaluating the Performance of Low-Cost Air Quality Monitors in Dallas, Texas

The emergence of low-cost air quality sensors may improve our ability to capture variations in urban air pollution and provide actionable information for public health. Despite the increasing popularity of low-cost sensors, there remain some gaps in the understanding of their performance under real-world conditions, as well as compared to regulatory monitors with high accuracy, but also high cost and maintenance requirements. In this paper, we report on the performance and the linear calibration of readings from 12 commercial low-cost sensors co-located at a regulatory air quality monitoring site in Dallas, Texas, for 18 continuous measurement months. Commercial AQY1 sensors were used, and their reported readings of O₃, NO₂, PM_2.5, and PM₁₀ were assessed against a regulatory monitor. We assessed how well the raw and calibrated AQY1 readings matched the regulatory monitor and whether meteorology impacted performance. We found that each sensor’s response was different. Overall, the sensors performed best for O₃ (R² = 0.36–0.97) and worst for NO₂ (0.00–0.58), showing a potential impact of meteorological factors, with an effect of temperature on O₃ and relative humidity on PM. Calibration seemed to improve the accuracy, but not in all cases or for all performance metrics (e.g., precision versus bias), and it was limited to a linear calibration in this study. Our data showed that it is critical for users to regularly calibrate low-cost sensors and monitor data once they are installed, as sensors may not be operating properly, which may result in the loss of large amounts of data. We also recommend that co-location should be as exact as possible, minimizing the distance between sensors and regulatory monitors, and that the sampling orientation is similar. There were important deviations between the AQY1 and regulatory monitors’ readings, which in small part depended on meteorology, hindering the ability of the low-costs sensors to present air quality accurately. However, categorizing air pollution levels, using for example the Air Quality Index framework, rather than reporting absolute readings, may be a more suitable approach. In addition, more sophisticated calibration methods, including accounting for individual sensor performance, may further improve performance. This work adds to the literature by assessing the performance of low-cost sensors over one of the longest durations reported to date.

Practical Particulate Matter Sensing and Accurate Calibration System Using Low-Cost Commercial Sensors

Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m3 and the standard deviation was approximately 1.1 μg/m3 in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

Small-Angle Particle Counting Coupled Photometry for Real-Time Detection of Respirable Particle Size Segmentation Mass Concentration

Respirable particulate matter air pollution is positively associated with SARS-CoV-2 mortality. Real-time and accurate monitoring of particle concentration changes is the first step to prevent and control air pollution from inhalable particles. In this research, a new light scattering instrument has been developed to detect the mass concentration of inhalable particles. This instrument couples the forward small-angle single particle counting method with the lateral group particle photometry method in a single device. The mass concentration of four sizes of inhalable particles in the environment can be detected simultaneously in a large area in real-time without using a particle impactor. Different from the traditional light scattering instrument, this new optical instrument can detect darker particles with strong light absorption, and the measurement results mainly depend on the particle size and ignore the properties of the particles. Comparative experiments have shown that the instrument can detect particles with different properties by simply calibrating the environmental density parameters, and the measurement results have good stability and accuracy.

Assessment of Low-Cost Particulate Matter Sensor Systems against Optical and Gravimetric Methods in a Field Co-Location in Norway

The increased availability of commercially-available low-cost air quality sensors combined with increased interest in their use by citizen scientists, community groups, and professionals is resulting in rapid adoption, despite data quality concerns. We have characterized three out-the-box PM sensor systems under different environmental conditions, using field colocation against reference equipment. The sensor systems integrate Plantower 5003, Sensirion SPS30 and Alphasense OCP-N3 PM sensors. The first two use photometry as a measuring technique, while the third one is an optical particle counter. For the performance evaluation, we co-located 3 units of each manufacturer and compared the results against optical (FIDAS) and gravimetric (KFG) methods for a period of 7 weeks (28 August to 19 October 2020). During the period from 2nd and 5th October, unusually high PM concentrations were observed due to a long-range transport episode. The results show that the highest correlations between the sensor systems and the optical reference are observed for PM1, with coefficients of determination above 0.9, followed by PM2.5. All the sensor units struggle to correctly measure PM10, and the coefficients of determination vary between 0.45 and 0.64. This behavior is also corroborated when using the gravimetric method, where correlations are significantly higher for PM2.5 than for PM10, especially for the sensor systems based on photometry. During the long range transport event the performance of the photometric sensors was heavily affected, and PM10 was largely underestimated. The sensor systems evaluated in this study had good agreement with the reference instrumentation for PM1 and PM2.5; however, they struggled to correctly measure PM10. The sensors also showed a decrease in accuracy when the ambient size distribution was different from the one for which the manufacturer had calibrated the sensor, and during weather conditions with high relative humidity. When interpreting and communicating air quality data measured using low-cost sensor systems, it is important to consider such limitations in order not to risk misinterpretation of the resulting data.

High Resolution Mapping of PM2.5 Concentrations in Paris (France) Using Mobile Pollutrack Sensors Network in 2020

There is a need for accurate monitoring of PM2.5 that adversely affects human health. Consequently, in addition to the monitoring performed by fixed microbalance instruments installed under legal obligation, we are proposing to deploy the Pollutrack network of mobile sensors within the city of Paris (France). The measurements are performed by mobile aerosol counters mounted on the roof of cars, providing a constant series of readings in the 0.3–10 µm size range that are then aggregated to identify areas of mass concentrations of pollution. The performance of the Pollutrack sensors has been established in ambient air in comparison with the microbalance measurement devices and with the Light Optical Aerosols Counter (LOAC) aerosol counter. A measurement uncertainty of about 5 µg. m−3 is obtained with absolute values from the Pollutrack measurements made at a given location. Instead of the current modelizations based on very few PM2.5 values, maps built from real measurements with a spatial resolution down to 100 m can now be produced each day for Paris, and potentially for specific times of the day, thanks to the high number of measurements achievable with the Pollutrack system (over 70,000 on weekdays). Interestingly, the global trend of PM2.5 content shows several significant pollution events in 2020 despite the COVID-19 crisis and the lockdown. The Pollutrack pollution maps recorded during different PM2.5 pollution conditions in the city frequently identified a strong spatial heterogeneity where the North and the East of Paris were more polluted than the west. These “hot spots” could be due to the city topology and its sensitivity to wind direction and intensity. These high-resolution maps will be crucial in creating evidence for the relevant authorities to respond appropriately to local sources of pollution and to improve the understanding of transportation of urban PM.

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

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

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

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