scholarly journals Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4796
Author(s):  
Amara L. Holder ◽  
Anna K. Mebust ◽  
Lauren A. Maghran ◽  
Michael R. McGown ◽  
Kathleen E. Stewart ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Field Evaluation ◽  
Eastern United States ◽  
Monitoring Networks ◽  
Wildfire Smoke ◽  
Sensor Package ◽  
The Impact ◽  
Mean Square Errors

Until recently, air quality impacts from wildfires were predominantly determined based on data from permanent stationary regulatory air pollution monitors. However, low-cost particulate matter (PM) sensors are now widely used by the public as a source of air quality information during wildfires, although their performance during smoke impacted conditions has not been thoroughly evaluated. We collocated three types of low-cost fine PM (PM2.5) sensors with reference instruments near multiple fires in the western and eastern United States (maximum hourly PM2.5 = 295 µg/m3). Sensors were moderately to strongly correlated with reference instruments (hourly averaged r2 = 0.52–0.95), but overpredicted PM2.5 concentrations (normalized root mean square errors, NRMSE = 80–167%). We developed a correction equation for wildfire smoke that reduced the NRMSE to less than 27%. Correction equations were specific to each sensor package, demonstrating the impact of the physical configuration and the algorithm used to translate the size and count information into PM2.5 concentrations. These results suggest the low-cost sensors can fill in the large spatial gaps in monitoring networks near wildfires with mean absolute errors of less than 10 µg/m3 in the hourly PM2.5 concentrations when using a sensor-specific smoke correction equation.

scholarly journals Measure particulate matter by yourself: data-quality monitoring in a citizen science project

2019 ◽  
Vol 8 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Aboubakr Benabbas ◽  
Martin Geißelbrecht ◽  
Gabriel Martin Nikol ◽  
Lukas Mahr ◽  
Daniel Nähr ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Data ◽  
And Control ◽  
Future Work ◽  
The Impact

Abstract. The concern about air quality in urban areas and the impact of particulate matter (PM) on public health is turning into a big debate. A good solution to sensitize people to this issue is to involve them in the process of air quality monitoring. This paper presents contributions in the field of PM measurements using low-cost sensors. We show how a low-cost PM sensor can be extended to transfer data not only over Wi-Fi but also over the LoRa protocol. Then, we identify some of the correlations existing in the data through data analysis. Afterwards, we show how semantic technologies can help model and control sensor data quality in an increasing PM sensor network. We finally wrap up with a conclusion and plans for future work.

scholarly journals Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 926 ◽  
Author(s):  
Iasonas Stavroulas ◽  
Georgios Grivas ◽  
Panagiotis Michalopoulos ◽  
Eleni Liakakou ◽  
Aikaterini Bougiatioti ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Forest ◽  
Low Cost ◽  
Field Evaluation ◽  
Urban Environments ◽  
Spatiotemporal Variability ◽  
Monitoring Networks ◽  
Fire Event ◽  
Ambient Relative Humidity ◽  
Reference Measurements

Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

scholarly journals Using Low-Cost Sensors to Assess Fine Particulate Matter Infiltration (PM2.5) during a Wildfire Smoke Episode at a Large Inpatient Healthcare Facility

2021 ◽  
Vol 18 (18) ◽  
pp. 9811
Author(s):  
Phuong D. M. Nguyen ◽  
Nika Martinussen ◽  
Gary Mallach ◽  
Ghazal Ebrahimi ◽  
Kori Jones ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Healthcare Facility ◽  
Dew Point ◽  
Limited Information ◽  
Healthcare Facilities ◽  
Wildfire Smoke ◽  
Fine Particulate

Wildfire smoke exposure is associated with a range of acute health outcomes, which can be more severe in individuals with underlying health conditions. Currently, there is limited information on the susceptibility of healthcare facilities to smoke infiltration. As part of a larger study to address this gap, a rehabilitation facility in Vancouver, Canada was outfitted with one outdoor and seven indoor low-cost fine particulate matter (PM2.5) sensors in Air Quality Eggs (EGG) during the summer of 2020. Raw measurements were calibrated using temperature, relative humidity, and dew point derived from the EGG data. The infiltration coefficient was quantified using a distributed lag model. Indoor concentrations during the smoke episode were elevated throughout the building, though non-uniformly. After censoring indoor-only peaks, the average infiltration coefficient (range) during typical days was 0.32 (0.22–0.39), compared with 0.37 (0.31–0.47) during the smoke episode, a 19% increase on average. Indoor PM2.5 concentrations quickly reflected outdoor conditions during and after the smoke episode. It is unclear whether these results will be generalizable to other years due to COVID-related changes to building operations, but some of the safety protocols may offer valuable lessons for future wildfire seasons. For example, points of building entry and exit were reduced from eight to two during the pandemic, which likely helped to protect the building from wildfire smoke infiltration. Overall, these results demonstrate the utility of indoor low-cost sensors in understanding the impacts of extreme smoke events on facilities where highly susceptible individuals are present. Furthermore, they highlight the need to employ interventions that enhance indoor air quality in such facilities during smoke events.

scholarly journals The impact of measures to reduce ambient air PM<sub>10</sub> concentrations originating from road dust, evaluated for a street canyon in Helsinki

2019 ◽  
Vol 19 (17) ◽  
pp. 11199-11212 ◽  
Author(s):  
Ana Stojiljkovic ◽  
Mari Kauhaniemi ◽  
Jaakko Kukkonen ◽  
Kaarle Kupiainen ◽  
Ari Karppinen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Street Canyon ◽  
Dust Emission ◽  
Road Dust ◽  
Ambient Air ◽  
Air Quality Measurements ◽  
Emission Models ◽  
The Impact ◽  
Quality Measurements

Abstract. We have numerically evaluated how effective selected potential measures would be for reducing the impact of road dust on ambient air particulate matter (PM10). The selected measures included a reduction of the use of studded tyres on light-duty vehicles and a reduction of the use of salt or sand for traction control. We have evaluated these measures for a street canyon located in central Helsinki for four years (2007–2009 and 2014). Air quality measurements were conducted in the street canyon for two years, 2009 and 2014. Two road dust emission models, NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and FORE (Forecasting Of Road dust Emissions), were applied in combination with the Operational Street Pollution Model (OSPM), a street canyon dispersion model, to compute the street increments of PM10 (i.e. the fraction of PM10 concentration originating from traffic emissions at the street level) within the street canyon. The predicted concentrations were compared with the air quality measurements. Both road dust emission models reproduced the seasonal variability of the PM10 concentrations fairly well but under-predicted the annual mean values. It was found that the largest reductions of concentrations could potentially be achieved by reducing the fraction of vehicles that use studded tyres. For instance, a 30 % decrease in the number of vehicles using studded tyres would result in an average decrease in the non-exhaust street increment of PM10 from 10 % to 22 %, depending on the model used and the year considered. Modelled contributions of traction sand and salt to the annual mean non-exhaust street increment of PM10 ranged from 4 % to 20 % for the traction sand and from 0.1 % to 4 % for the traction salt. The results presented here can be used to support the development of optimal strategies for reducing high springtime particulate matter concentrations originating from road dust.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

scholarly journals Eta-CMAQ air quality forecasts for O<sub>3</sub> and related species using three different photochemical mechanisms (CB4, CB05, SAPRC-99): comparisons with measurements during the 2004 ICARTT study

2010 ◽  
Vol 10 (6) ◽  
pp. 3001-3025 ◽  
Author(s):  
S. Yu ◽  
R. Mathur ◽  
G. Sarwar ◽  
D. Kang ◽  
D. Tong ◽  
...  
Keyword(s):  
Air Quality ◽  
Production Efficiency ◽  
Gulf Of Maine ◽  
Ozone Production ◽  
Eastern United States ◽  
Forecast Performance ◽  
Transport And Transformation ◽  
Mixing Ratios ◽  
Upper Limits ◽  
The Impact

Abstract. A critical module of air quality models is the photochemical mechanism. In this study, the impact of the three photochemical mechanisms (CB4, CB05, SAPRC-99) on the Eta-Community Multiscale Air Quality (CMAQ) model's forecast performance for O3, and its related precursors has been assessed over the eastern United States with observations obtained by aircraft (NOAA P-3 and NASA DC-8) flights, ship and two surface networks (AIRNow and AIRMAP) during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) study. The results show that overall none of the mechanisms performs systematically better than the others. On the other hand, at the AIRNow surface sites, CB05 has the best performance with the normalized mean bias (NMB) of 3.9%, followed by CB4 (NMB=−5.7%) and SAPRC-99 (NMB=10.6%) for observed O3≥75 ppb, whereas CB4 has the best performance with the least overestimation for observed O3<75 ppb. On the basis of comparisons with aircraft P-3 measurements, there were consistent overestimations of O3, NOz, PAN and NOy and consistent underestimations of CO, HNO3, NO2, NO, SO2 and terpenes for all three mechanisms although the NMB values for each species and mechanisms were different. The results of aircraft DC-8 show that CB05 predicts the H2O2 mixing ratios most closely to the observations (NMB=10.8%), whereas CB4 and SAPRC-99 overestimated (NMB=74.7%) and underestimated (NMB=−25.5%) H2O2 mixing ratios significantly, respectively. For different air mass flows over the Gulf of Maine on the basis of the ship data, the three mechanisms have relatively better performance for O3, isoprene and SO2 for the clean marine or continental flows but relatively better performance for CO, NO2 and NO for southwesterly/westerly offshore flows. The results of the O3-NOz slopes over the ocean indicate that SAPRC-99 has the highest upper limits of the ozone production efficiency (εN) (5.8), followed by CB05 (4.5) and CB4 (4.0) although they are much lower than that inferred from the observation (11.8), being consistent with the fact that on average, SAPRC-99 produces the highest O3, followed by CB05 and CB4, across all O3 mixing ratio ranges

Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors

2019 ◽  
Vol 245 ◽  
pp. 932-940 ◽  
Author(s):  
T. Sayahi ◽  
A. Butterfield ◽  
K.E. Kelly
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Field Evaluation ◽  
Term Field
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