Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing

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

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

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Fundamentals of low-cost aerosol sensor design and operation

Most evaluations of low-cost aerosol sensors have focused on their measurement bias compared to regulatory monitors. Few evaluations have applied fundamental principles of aerosol science to increase our understanding of how such sensors work and could be improved. We examined the Plantower PMS5003 sensor's internal geometry, laser properties, photodiode responses, microprocessor output, flow rates, and response to mono- and poly-disperse aerosols. We developed a physics-based model of particle light scattering within the sensor, which we used to predict counting and sizing efficiency for 0.30 to 10 μm particles. We found that the PMS5003 counts single particle scattering events, acting like an imperfect optical particle counter, rather than a nephelometer. As particle flow is not focused into the core of the laser beam, >99% of particles that flow through the PMS5003 miss the laser, and those that intercept the laser usually miss the focal point and are subsequently undersized, resulting in erroneous size distribution data. Our model predictions of PMS5003 response to varying particle diameters, aerosol compositions, and relative humidity were consistent with laboratory data. Computational fluid dynamics simulations of the PurpleAir monitor housing showed that for wind-speeds less than 3 m s-1, fine and coarse particles were representatively aspired to the PMS5003 inlet. Our measurements and models explain why the PurpleAir overstates regulatory PM2.5 in some locations but not others; why the PurpleAir PM10 is unresponsive to windblown dust; and why it reports a similar particle size distribution for coarse particles as it does for smoke and ambient background aerosol.

Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution

Particulate matter (PM) air pollution is a major health hazard. The health effects of PM are closely linked to particle size, which governs its deposition in (and penetration through) the respiratory tract. In recent years, low-cost sensors that report particle concentrations for multiple-sized fractions (PM1.0, PM2.5, PM10) have proliferated in everyday use and scientific research. However, knowledge of how well these sensors perform across the full range of reported particle size fractions is limited. Unfortunately, erroneous particle size data can lead to spurious conclusions about exposure, misguided interventions, and ineffectual policy decisions. We assessed the linearity, bias, and precision of three low-cost sensor models, as a function of PM size fraction, in an urban setting. Contrary to manufacturers' claims, sensors are only accurate for the smallest size fraction (PM1). The PM1.0-2.5 and PM2.5-10 size fractions had large bias, noise, and uncertainty. These results demonstrate that low-cost aerosol sensors (1) cannot discriminate particle size accurately and (2) only report linear and precise measures of aerosol concentration in the accumulation mode size range (i.e., between 0.1 and 1 μm). We recommend that crowdsourced air quality monitoring networks stop reporting coarse (PM2.5-10) mode and PM10 mass concentrations from these sensors.

Temporal trends in ambient fine particulate matter and the impacts of COVID-19 on this pollutant in Grenada, West Indies

Most Caribbean islands do not have air pollution surveillance programs. Those who live in these countries are exposed to ambient air pollution from a variety of sources including motor vehicles, ocean-going vessels, and Saharan dust. We conducted an air sampling exposure study in Grenada to describe daily changes in fine particulate matter (PM_2.5) pollution, and during Saharan dust episodes. Further, we assessed the impacts of COVID-19 public health interventions on PM_2.5 concentrations in 2020. Four fixed-site PurpleAir monitors were installed throughout Grenada, and one on the neighboring island of Carriacou. PM_2.5 was measured between January 6 and December 31, 2020. We classified each of these days based on whether COVID-19 public health mitigation measures were in place or not. Descriptive analyses were performed to characterize fluctuations in PM_2.5, and we assessed the impacts of public health restrictions on PM_2.5 using multivariate regression. The mean daily PM_2.5 concentration in 2020 was 4.4 μg/m³. During the study period, the minimum daily PM_2.5 concentration was 0.7 μg/m³, and the maximum was 20.4 μg/m³. Daily mean PM_2.5 concentrations more than doubled on Saharan dust days (8.5 vs 3.6 μg/m³; p < 0.05). The daily mean PM_2.5 concentrations were estimated to be 1.2 μg/m³ lower when COVID-19 restrictions were in effect. Ambient PM_2.5 concentrations in Grenada are relatively low compared to other countries; however, Saharan dust episodes represent an important source of exposure. Low-cost sensors provide an opportunity to increase surveillance of air pollution in the Caribbean, however their value could be enhanced with the development of correction algorithms that more closely approximate values from reference-grade monitors.Implications: This study describes daily fluctuations in ambient PM_2.5 concentrations in Grenada in 2020. Overall, concentrations of PM_2.5 were low; however, we found that Saharan dust events cause daily exceedances in PM_2.5 above the current 24-hr limits of the World Health Organization. Moreover, the constructed models suggest that public health interventions to reduce the spread of COVID-19 reduced PM_2.5 concentrations by 27%.

The Impact of Meteorological Conditions and Agricultural Waste Burning on PM Levels: A Case Study of Avellino (Southern Italy)

In this work, the effect of the meteorological conditions and the agricultural waste burning on PM air pollution levels has been investigated in the city of Avellino, located in the Sabato Valley (southern Italy). Avellino has been described among the most polluted towns in Italy in terms of particulate matter (PM) during the last 10 years. The main aim of this study was to analyze the air quality data collected in Avellino and its surroundings during September 2021. In this period, the air quality in the Sabato Valley has been adversely affected by agricultural practices, which represent a significant source of PM. The impact of agricultural waste burning on PM levels in Avellino has been determined through an integrated monitoring network, consisting of two fixed urban reference stations and by several low-cost sensors distributed in the Sabato Valley. In the considered period, the two reference stations recorded several exceedances of the daily average PM₁₀ legislative limit value (50 µg m^-3) in addition to high concentrations of PM_2.5. Moreover, we provide a detailed description of the event that took place on 25 September 2021, when the combined effect of massive agricultural practices and very stable atmospheric conditions produced a severe pollution episode. Results show PM exceedances in Avellino concurrent with high PM values in the areas bordering the city due to agricultural waste burning and adverse meteorological conditions, which inhibit PM dispersion in the atmosphere.

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.

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.

Low-Cost Thermohygrometers to Assess Thermal Comfort in the Built Environment: A Laboratory Evaluation of Their Measurement Performance

A thermohygrometer is an instrument that is able to measure relative humidity and air temperature, which are two of the fundamental parameters to estimate human thermal comfort. To date, the market offers small and low-cost solutions for this instrument, providing the opportunity to bring electronics closer to the end-user and contributing to the proliferation of a variety of applications and open-source projects. One of the most critical aspects of using low-cost instruments is their measurement reliability. This study aims to determine the measurement performance of seven low-cost thermohygrometers throughout a 10-fold repeatability test in a climatic chamber with air temperatures ranging from about −10 to +40 °C and relative humidity from approximately 0 to 90%. Compared with reference sensors, their measurements show good linear behavior with some exceptions. A sub-dataset of the data collected is then used to calculate two of the most used indoor (PMV) and outdoor (UTCI) comfort indexes to define discrepancies between the indexes calculated with the data from the reference sensors and the low-cost sensors. The results suggest that although six of the seven low-cost sensors have accuracy that meets the requirements of ISO 7726, in some cases, they do not provide acceptable comfort indicators if the values are taken as they are. The linear regression analysis suggests that it is possible to correct the output to reduce the difference between reference and low-cost sensors, enabling the use of low-cost sensors to assess indoor thermal comfort in terms of PMV and outdoor thermal stress in UTCI and encouraging a more conscious use for environmental and human-centric research.

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.

New Calibration System for Low-Cost Suspended Particulate Matter Sensors with Controlled Air Speed, Temperature and Humidity

This paper presents a calibration system for low-cost suspended particulate matter (PM) sensors, consisting of reference instruments, enclosed space in a metal pipe (volume 0.145 m³), a duct fan, a controller and automated control software. The described system is capable of generating stable and repeatable concentrations of suspended PM in the air duct. In this paper, as the final result, we presented the process and effects of calibration of two low-cost air pollution stations—university measuring stations (UMS)—developed and used in the scientific project known as Storm&DustNet, implemented at the Jagiellonian University in Kraków (Poland), for the concentration range of PM from a few up to 240 µg·m^–3. Finally, we postulate that a device of this type should be available for every system composed of a large number of low-cost PM sensors.

Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2

Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3-0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.

Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors

This paper aims to monitor the ambient level of particulate matter less than 2.5 μm (PM2.5) by learning from multi-weather sensors. Over the past decade, China has established a high-density network of automatic weather stations. In contrast, the number of PM monitors is much smaller than the number of weather stations. Since the haze process is closely related to the variation of meteorological parameters, it is possible and promising to calculate the concentration of PM2.5 by studying the data from weather sensors. Here, we use three machine learning methods, namely multivariate linear regression, multivariate nonlinear regression, and neural network, in order to monitor PM2.5 by exploring the data of multi-weather sensors. The results show that the multivariate linear regression method has the root mean square error (RMSE) of 24.6756 μg/m3 with a correlation coefficient of 0.6281, by referring to the ground truth of PM2.5 time series data; and the multivariate nonlinear regression method has the RMSE of 24.9191 μg/m3 with a correlation coefficient of 0.6184, while the neural network based method has the best performance, of which the RMSE of PM2.5 estimates is 15.6391 μg/m3 with the correlation coefficient of 0.8701.