Particulate Matter Sensors Research Articles

A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda

The application of land use regression (LUR) modeling for estimating air pollution exposure has been used only rarely in sub-Saharan Africa (SSA). This is generally due to a lack of air quality monitoring networks in the region. Low cost air quality sensors developed locally in sub-Saharan Africa presents a sustainable operating mechanism that may help generate the air monitoring data needed for exposure estimation of air pollution with LUR models. The primary objective of our study is to investigate whether a network of locally developed low-cost air quality sensors can be used in LUR modeling for accurately predicting monthly ambient fine particulate matter (PM2.5) air pollution in urban areas of central and eastern Uganda. Secondarily, we aimed to explore whether the application of machine learning (ML) can improve LUR predictions compared to ordinary least squares (OLS) regression. We used data for the entire year of 2020 from a network of 23 PM2.5 low-cost sensors located in urban municipalities of eastern and central Uganda. Between January 1, 2020 and December 31, 2020, these sensors collected highly time-resolved measurement data of PM2.5 air concentrations. We used monthly-averaged PM2.5 concentration data for LUR prediction modeling of monthly PM2.5 concentrations. We used eight different ML base-learner algorithms as well as ensemble modeling. We applied 5-fold cross validation (80% training/20% test random splits) to evaluate the models with resampling and Root mean squared error (RMSE). The relative explanatory power and accuracy of the ML algorithms were evaluated by comparing coefficient of determination (R2) and RMSE, using OLS as the reference approach. The overall average PM2.5 concentration during the study period was 52.22 μg/m3 (IQR: 38.11, 62.84 μg/m3)—well above World Health Organization PM2.5 ambient air guidelines. From the base-learner and ensemble models, RMSE and R2 values ranged between 7.65 μg/m3 – 16.85 μg/m3 and 0.24–0.84, respectively. Extreme gradient boosting (xgbTree) performed best out of the base learner algorithms (R2 = 0.84; RMSE = 7.65 μg/m3). Model performance from ensemble modeling with Lasso and Elastic-Net Regularized Generalized Linear Models (glmnet) did not outperform xgbTree, but prediction performance was comparable to that of xgbTree. The most important temporal and spatial predictors of monthly PM2.5 levels were monthly precipitation, percent of the population using solid fuels for cooking, distance to Lake Victoria, and greenspace (NDVI) within a 500-m buffer of air monitors. In conclusion, data from locally developed low-cost PM sensors provide evidence that they can be used for spatio-temporal prediction modeling of air pollution exposures in Uganda. Moreover, the non-parametric ML and ensemble approaches to LUR modeling clearly outperformed OLS regression algorithm for the prediction of monthly PM2.5 concentrations. Deploying low-cost air quality sensors in concert with implementation of data quality control measures, can help address the critical need for expanding and improving air quality monitoring in resource-constrained settings of sub-Saharan Africa. These low-cost sensors, in conjunction with non-parametric ML algorithms, may provide a rapid path forward for PM2.5 exposure assessment and to spur air pollution epidemiology research in the region.

Review of the Newly Developed, Mobile Optical Sensors for Real-Time Measurement of the Atmospheric Particulate Matter Concentration.

Due to the adverse effects on human health and the environment, air quality monitoring, specifically particulate matter (PM), has received increased attention over the last decades. Most of the research and policy actions have been focused on decreasing PM pollution and the development of air monitoring technologies, resulting in a decline of total ambient PM concentrations. For these reasons, there is a continually increasing interest in mobile, low-cost, and real-time PM detection instruments in both indoor and outdoor environments. However, to the best of the authors’ knowledge, there is no recent literature review on the development of newly designed mobile and compact optical PM sensors. With this aim, this paper gives an overview of the most recent advances in mobile optical particle counters (OPCs) and camera-based optical devices to detect particulate matter concentration. Firstly, the paper summarizes the particulate matter effects on human health and the environment and introduces the major particulate matter classes, sources, and characteristics. Then, it illustrates the different theories, detection methods, and operating principles of the newly developed portable optical sensors based on light scattering (OPCs) and image processing (camera-based sensors), including their advantages and disadvantages. A discussion concludes the review by comparing different novel optical devices in terms of structures, parameters, and detection sensitivity.

Improving data reliability: A quality control practice for low-cost PM2.5 sensor network.

Low-cost air quality sensor networks have been increasingly used for high spatial resolution air quality monitoring in recent years. Ensuring data reliability during continuous operation is critical for these sensor networks. Using particulate matter sensor as an example, this study reports a data quality control method, including sensor selection, pre-calibration, and online inspection. It was used in developing and operating the dense low-cost particle sensor networks in two Chinese cities. Firstly, seven mainstream sensors were tested and one model of particle sensor was selected due to its better linearity and stability. For a batch of sensors of the same model, although they were calibrated after manufactured, there are differences in response toward the same concentration of pollutants. The systematical variation of sensors was corrected and unified through pre-calibration. After deploying them in the field, a data analysis method is established for online inspecting their working status. Using data from these sensors, it evaluates parameters such as intraclass correlation coefficients and normalized root mean square error. These two metrics help to construct a two-dimensional coordinate system and to classify sensors into four status, including normal, fluctuation, hotspots, and malfunction. During a one-month operation in the two cities, 8 (out of 82) and 10 (out of 59) sensors with suspected malfunctions were screened out for further on-site inspection. Moreover, the sensor networks show potential in identifying illegal emission sources that cannot be typically detected by sparse regulatory air quality monitoring stations.

Fire Detection Factor of Polyurethane Foam Fire Experiment in UL 268

This study focused on fire detection factors by confirming the adaptation level of fire detection due to black smoke generated in the case of polyurethane foam fire, and it compared and analyzed the change in combustion products. An experiment was conducted to derive fire detection factors using the UL 268 polyurethane foam real fire test standard. Combustion products were measured using a particulate matter sensor (PMS), combustion gas analyzer (CGA), and gas analyzer (GA). As a result of the experiment, it was confirmed that the adaptability of the existing fire detector deteriorated when black smoke was generated. The fire detection factor was derived using the measured values of combustion products. In particular, among the measured factors, the most adaptable combustion products were found to be PM 2.5, PM 10, NO, and CO. They were considered to improve reliability when used as fire detection factors, as compared to existing fire detectors.

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Technical note: Understanding the effect of COVID-19 on particle pollution using a low-cost sensor network

The 2020 coronavirus pandemic and the following quarantine measures have led to significant changes in daily life worldwide. Preliminary research indicates that air quality has improved in many urban areas as a result of these measures. This study takes a neighborhood-scale approach to quantifying this change in pollution. Using data from a network of citizen-hosted, low-cost particulate matter (PM) sensors, called Air Quality & yoU (AQ&U), we obtained high-spatial resolution measurements compared to the relatively sparse state monitoring stations. We compared monthly average estimated PM2.5 concentrations from February 11 to May 11, 2019 at 71 unique locations in Salt Lake County, UT, USA with the same (71) sensors’ measurements during the same timeframe in 2020. A paired t-test showed significant reductions (71.1% and 21.3%) in estimated monthly PM2.5 concentrations from 2019 to 2020 for the periods from March 11-April 10 and April 11-May 10, respectively. The March time period corresponded to the most stringent COVID-19 related restrictions in this region. Significant decreases in PM2.5 were also reported by state monitoring sites during March (p < 0.001 compared to the previous 5-year average). While we observed decreases in PM2.5 concentrations across the valley in 2020, it is important to note that the PM2.5 concentrations did not improve equally in all locations. We observed the greatest reductions at lower elevation, more urbanized areas, likely because of the already low levels of PM2.5 at the higher elevation, more residential areas, which were generally below 2 μg/m3 in both 2019 and 2020. Although many of measurements during March and April were near or below the estimated detection limit of the low-cost PM sensors and the federal equivalent measurements, every low-cost sensor (51) showed a reduction in PM2.5 concentration in March of 2020 compared to 2019. These results suggest that the air quality improvement seen after March 11, 2020 is due to quarantine measures reducing traffic and decreasing pollutant emissions in the region.

Diurnal, Temporal and Spatial Variations of Main Air Pollutants Before and during Emergency Lockdown in the City of Novi Sad (Serbia)

Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO2 levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO2, NO, and NOX during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors’ usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.

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.

On-field test and data calibration of a low-cost sensor for fine particles exposure assessment

BackgroundAccurate individual exposure assessment is crucial for evaluating the health effects of particulate matter (PM). Various portable monitors built upon low-cost optical sensors have emerged. However, the main challenge for their application is to guarantee accuracy of measurements. ObjectiveTo assess the performance of a newly developed PM sensor, and to develop methods for post-hoc data calibration to optimize its data quality. MethodWe conducted a series of laboratory experiments and field evaluations to quantify the reproducibility within Plantower PM sensors 7003 (PMS 7003) and the consistency between sensors and two established PM2.5 measurement methods [tapered element oscillating microbalances (TEOM) and gravimetric method (GM)]. Post-hoc data calibration methods for sensors were based on a multiple linear regression model (MLRM) and a random forest model (RFM). Ratios of raw and calibrated readings over the data of reference methods were calculated to examine the improvement after calibration. ResultsStrong correlations (≥0.82) and relatively small relative standard deviations (16–21%) between sensors were found during the laboratory and the field sampling. Compared with the reference methods, moderate to strong coefficients of determination (0.56–0.83) were observed; however, significant deviations were presented. After calibration, the ratios of PMS measurements over that of two reference methods both became convergent. ConclusionsOur study validated low-cost optical PM sensors under a wide range of PM2.5 concentrations (8–167 μg/m3). Our findings indicated potential applicability of PM sensors in PM2.5 exposure assessment, and confirmed a need of calibration. Linear calibration methods may be sufficient for ambient monitoring using TEOM as a reference, while nonlinear calibration methods may be more appropriate for indoor monitoring using GM as a reference.

Anomaly detection in particulate matter sensor using hypothesis pruning generative adversarial network

World Health Organization (WHO) provides the guideline for managing the Particulate Matter (PM) level because when the PM level is higher, it threats the human health. For managing PM level, the procedure for measuring PM value is needed firstly. We use Tapered Element Oscillating Microbalance (TEOM)-based PM measuring sensors because it shows higher cost-effectiveness than Beta Attenuation Monitor (BAM)-based sensor. However, TEOM-based sensor has higher probability of malfunctioning than BAM-based sensor. In this paper, we call the overall malfunction as an anomaly, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that named as Hypothesis Pruning Generative Adversarial Network (HP-GAN). We experimentally compare the several anomaly detection architectures to certify ours performing better.

Evaluation of optical particulate matter sensors under realistic conditions of strong and mild urban pollution

Abstract. In this paper we evaluate characteristics of three optical particulate matter sensors/sizers (OPS): high-end spectrometer 11-D (Grimm, Germany), low-cost sensor OPC-N2 (Alphasense, United Kingdom) and in-house developed MAQS (Mobile Air Quality System), which is based on another low-cost sensor – PMS5003 (Plantower, China), under realistic conditions of strong and mild urban pollution. Results were compared against a reference gravimetric system, based on a Gemini (Dadolab, Italy), 2.3 m3 h−1 air sampler, with two channels (simultaneously measuring PM2.5 and PM10 concentrations). The measurements were performed in Sarajevo, the capital of Bosnia-Herzegovina, from December 2019 until May 2020. This interval is divided into period 1 – strong pollution – and period 2 – mild pollution. The city of Sarajevo is one of the most polluted cities in Europe in terms of particulate matter: the average concentration of PM2.5 during the period 1 was 83 µg m−3, with daily average values exceeding 500 µg m−3. During period 2, the average concentration of PM2.5 was 20 µg m−3. These conditions represent a good opportunity to test optical devices against the reference instrument in a wide range of ambient particulate matter (PM) concentrations. The effect of an in-house developed diffusion dryer for 11-D is discussed as well. In order to analyse the mass distribution of particles, a scanning mobility particle sizer (SMPS), which together with the 11-D spectrometer gives the full spectrum from nanoparticles of diameter 10 nm to coarse particles of diameter 35 µm, was used. All tested devices showed excellent correlation with the reference instrument in period 1, with R2 values between 0.90 and 0.99 for daily average PM concentrations. However, in period 2, where the range of concentrations was much narrower, R2 values decreased significantly, to values from 0.28 to 0.92. We have also included results of a 13.5-month long-term comparison of our MAQS sensor with a nearby beta attenuation monitor (BAM) 1020 (Met One Instruments, USA) operated by the United States Environmental Protection Agency (US EPA), which showed similar correlation and no observable change in performance over time.

A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives.

The concerns related to particulate matter’s health effects alongside the increasing demands from citizens for more participatory, timely, and diffused air quality monitoring actions have resulted in increasing scientific and industrial interest in low-cost particulate matter sensors (LCPMS). In the present paper, we discuss 50 LCPMS models, a number that is particularly meaningful when compared to the much smaller number of models described in other recent reviews on the same topic. After illustrating the basic definitions related to particulate matter (PM) and its measurements according to international regulations, the device’s operating principle is presented, focusing on a discussion of the several characterization methodologies proposed by various research groups, both in the lab and in the field, along with their possible limitations. We present an extensive review of the LCPMS currently available on the market, their electronic characteristics, and their applications in published literature and from specific tests. Most of the reviewed LCPMS can accurately monitor PM changes in the environment and exhibit good performances with accuracy that, in some conditions, can reach R2 values up to 0.99. However, such results strongly depend on whether the device is calibrated or not (using a reference method) in the operative environment; if not, R2 values lower than 0.5 are observed.

IoT-Enabled Particulate Matter Monitoring and Forecasting Method Based on Cluster Analysis

In recent years, particulate matter (PM) having a diameter smaller than 2.5 μm has become a significant issue due to its severe impact on human health. With the advent of IoT-enabling technologies, a ubiquitous IoT sensing infrastructure is now used to constantly monitor aspects of our surrounding environment, such as ambient air pollution. In this article, we introduce a PM-sensing system composed of off-the-shelf LoRa-based wireless hardware boards and low-cost PM sensors. By leveraging software platforms that are compliant with an IoT standard called oneM2M, PM data sets can be collected and accessed in a standardized manner, i.e., via oneM2M-defined representational state transfer application programmable interfaces. Also, for reliable PM monitoring, a short-term (i.e., within 2 h) PM forecasting method based on autoregressive integrated moving average and vector autoregressive moving average (VARMA) models is proposed and evaluated with a 30-day PM data set collected from 15 LoRa-based PM sensor nodes installed at a university campus. The experimental results show that the overall root-mean square error and correlation coefficient of the VARMA models integrated with hierarchical clustering are improved by 7.77% and 3.7%, respectively, compared with the single node-based forecast model.

Simulation and Experimental Research on the Charged Characteristics of Particulate Matter in the Sensor under Different Exhaust States.

The particulate matter sensor needs to be used in order to detect the concentration of particulate matter in diesel engine exhaust, monitor the working condition of diesel particulate filter (DPF) in real time, and ensure the reliable operation of DPF. The flow field and electric field of the sensor are studied and their distribution in the sensor is analyzed. At the same time, the particle tracking model was used to simulate the charged characteristics of particles in the sensor under different exhaust states. It is found that the exhaust gas flow rate maintains stability after entering the outer protection zone and concentration test zone. The electric field is a non-uniform electric field and the direction of electric field intensity is from the high voltage electrode to the grounding electrode. The electric charge per particle will decrease with the increase of exhaust flow rate, but the electric charge shows a slow growth trend. The charge of particles increases with the increase of exhaust temperature, exhaust gas concentration and particle size. The study of the charged characteristics under different environmental conditions provides a theoretical basis for further improving the prototype mechanism of a leakage flow particle sensor.

A Smart Mask for Active Defense Against Coronaviruses and Other Airborne Pathogens

Face masks provide effective, easy-to-use, and low-cost protection against airborne pathogens or infectious agents, including SARS-CoV-2. Existing masks are all passive in nature, i.e., simply act as air filters for the nasal passage and/or mouth. This article presents a new “active mask” paradigm, in which the wearable device is equipped with smart sensors and actuators to both detect the presence of airborne pathogens in real time and take appropriate action to mitigate the threat. The proposed approach is based on a closed-loop control system that senses airborne particles of different sizes near the mask and then makes intelligent decisions to reduce their concentrations. In the current implementation, an on-board controller determines ambient air quality via a commercial particulate matter sensor, and if necessary activates a piezoelectric actuator that generates a mist spray to load these particles, thus causing them to fall to the ground. The system communicates with the user via a smart phone application that provides various alerts, including the need to recharge and/or decontaminate the mask prior to reuse. The application also enables a user to override the on-board control system and manually control the mist generator if necessary. Experimental results from a functional prototype demonstrate significant reduction in airborne PM counts near the mask when the active protection system is enabled.

Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network.

Low-cost air quality sensors can help increase spatial and temporal resolution of air pollution exposure measurements. These sensors, however, most often produce data of lower accuracy than higher-end instruments. In this study, we investigated linear and random forest models to correct PM2.5 measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.S. Environmental Protection Agency Federal Equivalence Method (FEM) monitors. Our training set included data from five DDPHE sensors from August 2018 through May 2019. Our testing set included data from two newly deployed DDPHE sensors from September 2019 through mid-December 2019. In addition to PM2.5, temperature, and relative humidity from the low-cost sensors, we explored using additional temporal and spatial variables to capture unexplained variability in sensor measurements. We evaluated results using spatial and temporal cross-validation techniques. For the long-term dataset, a random forest model with all time-varying covariates and length of arterial roads within 500m was the most accurate (testing RMSE=2.9μg/m3 and R2=0.75; leave-one-location-out (LOLO)-validation metrics on the training set: RMSE=2.2μg/m3 and R2=0.93). For on-the-fly correction, we found that a multiple linear regression model using the past eight weeks of low-cost sensor PM2.5, temperature, and humidity data plus a near-highway indicator predicted each new week of data best (testing RMSE=3.1μg/m3 and R2=0.78; LOLO-validation metrics on the training set: RMSE=2.3μg/m3 and R2=0.90). The statistical methods detailed here will be used to correct low-cost sensor measurements to better understand PM2.5 pollution within the city of Denver. This work can also guide similar implementations in other municipalities by highlighting the improved accuracy from inclusion of variables other than temperature and relative humidity to improve accuracy of low-cost sensor PM2.5 data.

Numerical and experimental investigation on the performance of a ventilated chamber for low-cost PM sensor calibration

Particulate matter (PM) sensors are popular for distributed monitoring due to their portability, low-cost and networking capabilities. To provide reliable data, these sensors have to be calibrated periodically in the laboratory. This is often done in chambers with several of them co-located, alongside several research grade instruments. It is important to confirm that the aerosol is well distributed to ensure reliability and accuracy of the calibration tests. This paper describes a modeling and experimental study of these chambers used for calibration of PM sensors. The experimental setup is described and calibration results for the low-cost PM sensors (MAXIMA sensor, Applied Particle Technology, Inc.) using research-grade instruments (GRIMM 11-C, GRIMM Technologies, Inc.) are presented. A computational fluid dynamics (CFD) model including effects of turbulence and Brownian motion is used to predict particle trajectories and distribution in the chamber. The simulated particle number concentration agrees well with the measured values in the chamber. Having the CFD model, the suitability of three proposed chamber designs (fan off; fan on-parallel; fan on-diagonal) are explored for a variety of conditions. Operating conditions that result in uniform concentrations are established using the model. The recommended design for the PM sensor calibration in a rectangular ventilated chamber is: parallel fan layout, fan pressure of 12 Pa, and inlet air flow rate between 10 and 15 L/min. This study shows that the numerical modeling is available to understand the performance of a calibration chamber as well as optimizing the chamber design. A broader perspective is that the results validate the use of low-cost PM sensors for checking the distribution of aerosols in chambers.

A lightweight low-cost and multipollutant sensor package for aerial observations of air pollutants in atmospheric boundary layer

A lightweight low-cost multipollutant sensor package is particularly portable to aerial measurements and field campaign due to its light weight (2.0 kg), compact size (L × W × H, 22 × 15 × 10 cm), and low energy-consumption (20 Wa). The sensor system consists of electrochemical sensors measuring O3, NO2, NO, SO2, CO, an optical counting (OPC) particulate matter (PM) sensor for PM1.0, PM2.5, and PM10, a miniature photoionization detector (PID) for total volatile organic compounds (TVOCs), and metrological sensors for air temperature, relative humidity (RH) and air pressure. All sensor signals were collected and transferred by a data acquirement (DAQ) logger. The sensor data were saved in a microcomputer and transmitted wireless by a GSM/GPS module. All sensors and the accessories were integrated and installed in a thermal insulation foam package, of which temperature was stable at 25 °C to avoid the temperature effect. A silicone desiccant tube was connected to the inlet of air sample to minimize the RH influence. The sensor performances were compared with on-ground reference instruments for a 21-day field campaign and commercial portable instruments from Thermal Scientific and Vaisala in aerial observations. All gas sensors and PM sensor showed good correlations with the reference instruments with R2 varying from 0.81 to 0.93 and slope from 0.89 to 1.35. The O3 sensor performed best with R2 = 0.93 and slope = 1.02. In aerial measurements PM and O3 sensors obtained similar vertical profiles of PM and O3 with those obtained by commercial Thermal PTR PM monitor and Vaisala O3 sounding sensor, respectively. The sensor package was successfully deployed to observe the vertical profiles of air pollutants on a tethered balloon and to exactly locate exhaust sources in an industrial park with an unmanned aerial vehicle (UAV). Our sensor package was substantiated to be a reliable and accurate device for aerial measurements of air pollutants in atmospheric boundary layer (ABL).

Usage of Low-Cost Particulate Matter Sensors for Characterization of Emission Dynamics at a Traffic Intersection and for the Description of Pollution in Urban Areas

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Despite the increase in public attention to particulate matter emissions since the millennium and the known effects on the human organism, representative predictions on the dispersion of emissions in both urban and rural areas are hardly possible so far. Especially the validation of simulations with measurement results is not possible due to the limited number and high price of certified measuring instruments. Rather, the temporally high-resolution measuring instruments are only sporadically distributed within the city, so that emission dynamics at traffic hotspots, such as traffic jams or intersections, cannot be depicted. Furthermore, due to the small number of certified measuring instruments, fine dust pollution can only be interpolated over long distances.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;In this paper, first results from a study about the transport characteristics of particles at a road intersection will be made by using a variety of so-called low-cost particulate matter sensors. Furthermore, the portable low-cost sensors will be used for the mobile measurement of fine dust concentrations in large areas. 20 sensors of the type Plantower PMS7003 were distributed along a traffic intersection. The sensors, previously calibrated on a test setup, determine the particle classes PM10, PM2.5 and PM1 in real time and additionally the temperature and humidity. This alloes the analysis of the fine dust concentrations in great detail, also with regard to the prevailing weather conditions. Es konnte gezeigt werden, dass lokale Unterschiede mit dem Low-Cost Sensoren festzustellen sind, Zusätzlich können zeitliche Änderungen in der Partikelkonzentration entlang der Kreuzung beobachtet werdn. In the further course of the project, the mobile sensors will be to map an urban area with regard to the fine dust distribution.&lt;/div&gt;&lt;/div&gt;

Investigation of Low-Cost and Optical Particulate Matter Sensors for Ambient Monitoring

This article presents a long-term evaluation of low-cost particulate matter (PM) sensors in a field measurements campaign. Evaluation was performed in two phases. During the first five months of the campaign, two PM sensors were simultaneously compared with the results from the reference air quality monitoring station in various atmospheric conditions—from the days with freezing cold (minimum temperature below −10 °C) and high relative humidity (up to 95%) to the days with the maximum temperature above 30 °C and low relative humidity (at the level of 25%). Based on the PM10 measurements, the correlation coefficients for both devices in relation to the reference station were determined (r = 0.91 and r = 0.94, respectively), as well as the impact of temperature and relative humidity on measurements from the low-cost sensors in relation to the reference values. The correction function was formulated based on this large set of low-cost PM10 measurements and referential values. The effectiveness of the corrective function was verified during the second measurement campaign carried out in the city of Nowy Sącz (located in southern Poland) for the same five months in the following year. The absolute values of the long-term percentage errors obtained after adjustment were reduced to a maximum of about 20%, and the average percentage errors were usually around 10%.