A Wide Dynamic Range Optical Particulate Matter Sensor With On-Chip Machine Learning Calibration

There has been a steady increase in applications that rely on crowdsensing to gather data for analysis purposes. Crowdsensing enables the use of dynamic sensors to collect data on static objects of interest. However, using dynamic sensors in this way causes a problem. The focus of the collected data is on the position of the sensor, not on the object of interest. This results in difficulties in tracking the object of interest in terms of what part of the data from the dynamic sensor describes the object of interest. To shift the focus from the dynamic sensors to a static object, the virtual sensor is introduced. A virtual sensor enables the grouping of data from different dynamic sensors into a single virtual sensor based on measurement positions. The data from the multiple dynamic sensors can be analyzed to provide information per virtual sensor. The data structure of a visual sensor is close to the SensorThings API data structure, which can be expanded to support virtual sensors by adding an additional entity.

The overall objective of this project was to develop exhaust gas recirculation (EGR) control strategies using fast-response Particulate Matter (PM) sensors and NOx sensors to improve the quality of particulate and gaseous emissions from diesel engines. This project initially comprised three phases: (1) Phase IA - sensor requirements to meet PM sensor specifications, NOx sensor assessment, and initial model development for EGR control; (2) Phase IB - continue development on PM and NOx sensors, integrate the sensor signals into the control simulations, and finalize model development for control strategies; and (3) Phase II - validation testing of the control strategies. Only Phase 1A was funded by DOE and executed by Honeywell. The major objectives of Phase 1A of the project included: (1) Sensor validation and operation of fast-response PM and NOx sensors; (2) Control system modeling of low-pressure EGR controls, development of control strategies, and initial evaluation of these models and strategies for EGR control in diesel engines; (3) Sensor testing to understand applicability of fast-response PM sensors in determining loading rates of the particle trap; and (4) Model validation and sensor testing under steady-state and transient operational conditions of actual engines. In particular, specific objectives included demonstration of: (1)more » A PM sensor response time constant (T10 - T90) of better than 100 milliseconds (msec); (2) The ability to detect PM at concentrations from 0.2 to 2 Bosch smoke number (BSN) or equivalent; (3) PM sensor accuracy to within 20% BSN over the entire range of operation; and (4) PM sensor repeatability to within 10% over the PM entire sensor range equivalent to a BSN of 0.2 to 2.« less

PurposeThis paper aims to design and optimize the threaded fastener of leakage current particulate matter (PM) sensor. The corresponding air-tight test is conducted to ensure the reliability of the installation strategy with screw connection.Design/methodology/approachResearch on the pressure-deformation curve of seal gasket was conducted and the vibration load of engine was considered for the calculation of the minimum installation pre-tightening force. Simultaneously, the danger threaded section area was calculated, and the carrying capacity was verified. The height of the welding line was studied to ensure the reliability of the application. FEA was carried out to acquire the relationship between local structure size and local stress for continuous improvement of thread connection. The installation torque range was acquired from the torque control principle for the pre-tightening force. The sealing reliability of the connector was proved with leakage.FindingsThe air tightness of the thread connector is proved to be fine. When the pre-tightening force is over 8,000 N, and its length reaches 2 mm, the connector has good reliability at ambient temperature. The tightening torque of 60-74 Nm can guarantee the reliable fixing ability of thread connector, and its plastic non-deformation ability in the process of repeated tearing down.Originality/valueThis paper provides an installation strategy and an optimization of PM sensor, which has a positive effect on the study and the manufacture of PM sensor. It is helpful to further develop PM sensor and after-treatment technology. This kind of real-time monitoring PM sensor needs to be studied further to achieve its commercial application.

This paper proposes a development of the low-cost and capacitance measurement particulate matter (PM) sensor to monitor diesel particulate filter (DPF) failure. Compared to the resistance measurement PM sensor that is affected by ash accumulated between electrodes due to periodic PM regeneration, capacitance measurement method has the advantage of compensating with capacitance measurement even though the initial ash is present. To reduce the manufacturing cost, we fabricated the proposed sensor with alumina instead of zirconia as the substrate and reduced the sensing area by 26 % compared to the conventional PM sensor. As a result of evaluation, the response time of 5 min required to reach a threshold 50pF at the exhaust gas flow rate of 70 kg/h and PM concentration of 10 mg/m3. It is similar to the conventional PM sensor which takes 343 sec. Durability of the alumina-based PM sensor investigated through thermal endurance test, and the sensor features (C0, R0heater and response time) indicated no significant difference before and after the test.

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

This paper presents a dynamic range expansion technique of CMOS image sensors with dual charge storage in a pixel and multiple exposures. Each pixel contains two photodiodes, PD1 and PD2 whose sensitivity can be set independently by the accumulation time. The difference of charge accumulation time in both photodiode can be manipulated to expand the dynamic range of the sensor. It allows flexible control of the dynamic range since the accumulation time in PD2 is adjustable. The multiple exposure technique used in the sensor reduces the motion blur in the synthesized wide dynamic range image when capturing fast-moving objects. It also reduces the signal-to-nose ratio dip at the switching point of the PD1 signal to the PD2 signals in the synthesized wide dynamic range image. A wide dynamic range camera with 320x240 pixels image sensor has been tested. It is found that the sampling of 4 times for the short accumulation time signals is sufficient for the reduction of motion blur in the synthesized wide dynamic range image, and the signal-to-noise ratio dip at the switching point of the PD1 signal to the PD2 signal is improved by 6 dB using 4 short-time exposures.

This review summarizes current evidence on low-cost particulate matter (PM) sensors for indoor and occupational environments and proposes a framework that links performance evaluation, calibration, and uncertainty to decision-making. Results from laboratory and field co-locations are synthesized to define reporting standards-accuracy, precision, dynamic range, detection capability, and temporal response-and to compare calibration strategies. Optical sensors consistently capture temporal dynamics of indoor sources but show mass bias that depends on concentration range, aerosol composition, and humidity. Context-specific reporting, with conditioning on environmental state and source regime, is therefore essential. Calibration practices range from simple linear corrections, often adequate at low to moderate concentrations, to multivariate or nonlinear models incorporating humidity, temperature, or volatile organic compounds, which reduce residual bias under high or mixed-source loadings. A staged quality assurance and quality control workflow-including procurement screening, bench checks, co-location with blocked validation, external validation for transportability, and rotating "gold-node" drift checks-ensures reproducibility and decision-relevant uncertainty. Deployment studies demonstrate that event-aware sensor networks can support targeted ventilation and filtration, reducing exceedance time and cumulative exposure without unnecessary energy use. Standardized reporting tables, model versioning, applicability limits, and anomaly-handling rules further enhance reliability and governance. Overall, low-cost PM sensors can provide decision-relevant data when embedded in calibrated, uncertainty-aware pipelines with explicit scope statements. While reference-grade methods remain necessary for compliance, calibrated networks are well suited to hotspot detection, intervention design, and operational optimization in buildings and workplaces.

This paper presents an investigation and a correction technique for fixed pattern noise in wide dynamic range image sensor with on the focal plane Reinhard tone mapping. The Human eye is capable of capturing images over a wide dynamic range of illumination; however, typical CMOS image sensors have limited ability to capture dynamic range available in nature. Even when wide dynamic range images are captured, they need to be displayed on conventional media with limited ability to display WDR scenes. Hence, the image has algorithmically transformed by mathematical operators called tone mapping operators to fit the limited dynamic range. In this paper, we report our research on a pixel, which captures a scene with inbuilt tone mapping operator, particularly, the Reinhard photographic mapping operator, which is a monotonically increasing function. However, the inherent Fixed pattern noise(FPN) limits the performance of image sensors. This is mainly due to the variations between the responses of individual pixels within an array of pixels. A simple procedure has been adapted to reduce FPN in which parametric response of the pixel is used, with FPN modeled as variations in the individual parameters. The parameters of each individual pixel are measured, recorded and then used to correct their response.

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.

<div class="section abstract"><div class="htmlview paragraph">EmiSense Technologies, LLC (<a href="http://www.emisense.com" target="_blank">www.emisense.com</a>) is commercializing its electronic particulate matter (PM) sensor that is based on technology developed at the University of Texas at Austin (UT). To demonstrate the capability of this sensor for real-time PM measurements and on board diagnostics (OBD) for failure detection of diesel particle filters (DPF), independent measurements were performed to characterize the engine PM emissions and to compare with the PM sensor response.</div><div class="htmlview paragraph">Computational fluid dynamics (CFD) modeling was performed to characterize the hydrodynamics of the sensor's housing and to develop an improved PM sensor housing with reproducible hydrodynamics and an internal baffle to minimize orientation effects. PM sensors with the improved housing were evaluated in the truck exhaust of a heavy duty (HD) diesel engine tested on-road and on a chassis dynamometer at the University of California, Riverside (UCR) using their Mobile Emissions Laboratory (MEL). This consisted of a HD diesel tractor/trailer unit with PEMS for characterizing exhaust. Time-resolved PM mass and/or number concentrations were measured with an AVL MSS-483, Dekati DMM-230, TSI Dustrak, and UCR's fast scanning mobility particle sizer (fSMPS) and compared to the outputs of 12 EmiSense PMTrac sensors.</div><div class="htmlview paragraph">In situ PM measurements from EmiSense's PM sensors correlated well with gravimetric measurements, and results from the real-time laboratory PM monitoring instruments in UCR's mobile emissions laboratory. In addition, particle size distribution data from UCR's fSMPS are presented and discussed. One characteristic of the PM sensor that is desirable for post-DPF OBD measurements is that it directly measures PM in the exhaust with no additional requirements for sampling and/or dilution systems.</div></div>

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

Calibration of low-cost particulate matter sensors for coal dust 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%.

Recent advances in wireless communication technology and the Internet of Things (IoT) have provided an opportunity for mass deployment of low cost sensor nodes to measure air pollution in real-time over a large geographical area. This article presents the design of a low cost, innovative Air Pollution Monitoring Device (APMD) along with the evaluation of its advanced features. An on-board Particulate Matter (PM) sensor is designed to measure PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> and PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> . APMD additionally has electrochemical sensors to measure carbon monoxide, sulphur dioxide, nitrogen dioxide, ozone, besides temperature and humidity sensors. The node is equipped with a solar energy harvesting unit and a rechargeable battery as a backup to power up the module. By utilizing an on-board GPS subsystem, APMD packs all these gathered air quality data in a frame with physical location, time, and date, and sends them to a cloud server. The node can communicate through WiFi and NB-IoT connectivity. For validating the quality of sensing, the developed APMD was co-located with an accurate reference sensor node and a series of field data were collected over seven days. In a fully ON state, the on-board PM sensor saves up to 94% energy while maintaining root mean square error (RMSE) of 0.58 for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> and 2.5 for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> . A power control mechanism is also applied on the PM sensor to control the speed of the fan by applying a pulse width modulated (PWM) signal at the switch connected to the power supply of fan. At 100 ms switching period with 30% duty cycle, the on-board PM sensor is 97% energy efficient compared to the commercial sensor, while maintaining sensing error (RMSE) as low as 0.7 for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> and 2.7 for PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub> . Our outdoor deployment studies demonstrate that the designed APMD is 90.8% more power efficient than the reference setup with significantly higher coverage range, while maintaining an acceptable range of sensing error.

These data were collected during a study on the performance of low-cost particulate matter (PM) sensors. All data were collected in an indoor laboratory at Colorado State University in Fort Collins, Colorado, USA between 2019-07-02 and 2019-10-06. The files associated with this dataset include: (1) time-averaged PM mass concentrations reported by the low-cost sensors during each steady-state test point included in the study, (2) time-averaged particle number concentrations reported by the low-cost sensors during each steady-state test point included in the study, (3) time-averaged particle size distribution data measured using an Scanning Mobility Particle Sizer (SMPS) during each steady-state test point included in the study, (4) time-averaged particle size distribution data measured using an Aerodynamic Particle Sizer (APS) Spectrometer during each steady-state test point included in the study, (5) real-time particle size distribution data measured using an APS during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (6) PM2.5 concentrations recorded at one-minute intervals by a Tapered Element Oscillating Microbalance (TEOM) during all experiments conducted during the study, (7) PM concentrations recorded at one-minute intervals by a DustTrak during an experiment in which the low-cost sensors were exposed to very high Arizona road dust concentrations for 18 hours, (8) data associated with all gravimetric filter samples of PM collected during the study, (9) real-time data recorded by the low-cost PM sensors during an experiment in which the sensors were exposed to very high Arizona road dust concentrations for 18 hours, (10) all of the raw data recorded by the low-cost PM sensors during the study, and (11) all of the raw data recorded by a DustTrak DRX 8533 during the study.