Author Correction: Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment

In agro-industrial complex in most agricultural enterprises the expenses connected with production often exceed indicators of revenues of branch. Our researches have shown that there are some opportunities of change of this situation and, first of all, this introduction in production of effective, low-cost ways of processing of the soil. they significantly reduce potential contamination of an arable layer of earth, negatively don’t influence processes of formation of efficiency of culture, provide increases in level of profitability and net income against the background of saving of diesel fuel on one third. As the proof stated by us long-term field experiments in the conditions of a steppe zone of Kabardino-Balkaria are made (the item. Skilled the Tersky Region of KBR) 2009-2011 in these researches it is shown influence of ways of the main processing on potential contamination of the soil, productivity and economic efficiency of production of grain of corn.

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Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing

Sensors ◽

10.3390/s20164381 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4381 ◽

Cited By ~ 2

Author(s):

Han Mei ◽

Pengfei Han ◽

Yinan Wang ◽

Ning Zeng ◽

Di Liu ◽

...

Keyword(s):

Particulate Matter ◽

Low Cost ◽

Field Evaluation ◽

Dust Storms ◽

Relative Bias ◽

Miniaturized Sensors ◽

Beijing China ◽

Pm2.5 Concentration

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached −24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary.

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Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors

Heliyon ◽

10.1016/j.heliyon.2020.e04207 ◽

2020 ◽

Vol 6 (6) ◽

pp. e04207 ◽

Cited By ~ 2

Author(s):

Opeyemi R. Omokungbe ◽

Olusegun G. Fawole ◽

Oyediran K. Owoade ◽

Olalekan A.M. Popoola ◽

Roderic L. Jones ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Urban Environment ◽

Low Cost ◽

Airborne Particulate Matter ◽

Meteorological Conditions ◽

Airborne Particulate

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

Atmosphere ◽

10.3390/atmos11101040 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1040

Author(s):

Mariusz Rogulski ◽

Artur Badyda

Keyword(s):

Particulate Matter ◽

Relative Humidity ◽

Low Cost ◽

Reference Station ◽

Correction Function ◽

Maximum Temperature ◽

Large Set ◽

Campaign Evaluation ◽

The Impact

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%.

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APPLICATION OF LOW-COST OPTICAL PM SENSOR FOR MONITORING OF PARTICULATE MATTER AIR POLLUTION IN THE URBAN ENVIRONMENT: A CASE STUDY IN ESPLANĀDE HOUSING ESTATE, DAUGAVPILS CITY

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2021vol1.6595 ◽

2021 ◽

Vol 1 ◽

pp. 223-229

Author(s):

Juris Soms ◽

Haralds Soms

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Urban Environment ◽

Meteorological Data ◽

Low Cost ◽

Average Concentration ◽

Environmental Data ◽

Housing Estate ◽

Spatial Coverage ◽

Pm2.5 And Pm10

The harmful health effects of airborne particulate matter (PM) pollutants are well-known. However, the spatial coverage of automated air quality observation stations of Latvian Environment, Geology and Meteorology Centre (LEGMC) is sparse. Therefore the capability for PM concentration detection was examined by using the low-cost optical PM sensor to improve the spatial resolution of environmental data. The aim of the study was to perform 24h/7d measurements of PM2.5 and PM10 concentrations during a period of one year and to identify air quality in Esplanāde housing estate, Daugavpils city. For data obtaining on the concentration of PM2.5 and PM10 particles measurements have been performed by optical sensor Nova SDS011; meteorological data were obtained using the database of LEGMC; for processing, analysis and visualization of obtained data statistical methods were applied. Evaluation of PM2.5 and PM10 daily average concentration variability in 2020 indicates that air quality in the urban environment could be assessed as good. A well-expressed statistical correlation between meteorological factors (t°C, relative humidity) and the average concentration of PM particles was not found. It highlights the necessity of further research.

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Preliminary research for low-cost particulate matter sensor network

E3S Web of Conferences ◽

10.1051/e3sconf/201910000004 ◽

2019 ◽

Vol 100 ◽

pp. 00004 ◽

Cited By ~ 1

Author(s):

Csongor Báthory ◽

Márton L. Kiss ◽

Attila Trohák ◽

Zsolt Dobó ◽

Árpád Bence Palotás

Keyword(s):

Particulate Matter ◽

Calculation Method ◽

High Spatial Resolution ◽

Low Cost ◽

Pearson Correlation ◽

Load Test ◽

Air Quality Measurements ◽

Protective Cover ◽

Preliminary Research

Low-cost particulate matter (PM) sensors may be suitable for indicative air quality measurements thanks to their small dimensions and high spatial resolution. Three different sensor types were selected for investigation in this study with specific focus on a Honeywell HPMA115S0 sensor to find out its usability at outdoors, perform load and long-term tests. The load test showed that the sensor calculates PM10 based on measured PM2.5 values. The analysis shows a break in calculation method at 25 μg/m3 PM2.5, and the calculation method for PM10 varies from 25 μg/m3 by around 81 μg/m3. Parallel test performed with different sensor types has shown that the protective cover formed by lamellar exterior does not affect the accuracy of the sensors, no accumulation or loss of sensitivity occurs. Long-term measurements have shown that the concentration values measured by the Honeywell sensor during outdoor measurements require humidity compensation, over 90% relative humidity (RH) the Pearson correlation coefficient (R) between the reference and sensor PM2.5 concentrations decreased by 0.3.

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POTENTIAL OF NON-CALIBRATED UAV-BASED RGB IMAGERY FOR FORAGE MONITORING: CASE STUDY AT THE RENGEN LONG-TERM GRASSLAND EXPERIMENT (RGE), GERMANY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-203-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 203-206 ◽

Cited By ~ 1

Author(s):

G. Bareth ◽

U. Lussem ◽

J. Menne ◽

J. Hollberg ◽

J. Schellberg

Keyword(s):

Spatial Data ◽

Vegetation Index ◽

Low Cost ◽

Floristic Composition ◽

Growing Period ◽

Spatio Temporal ◽

Non Destructive ◽

Term Field

Abstract. Forage monitoring in grassland is an important task to support management decisions. Spatial data on (i) yield,(ii) quality, and (iii) floristic composition are of interest. The spatio-temporal variability in grasslands is significant and requires fast and low-cost methods for data delivery. Therefore, the overarching aim of this contribution is the investigation of low-cost and non-calibrated UAV-derived RGB imagery for forage monitoring. Study area is the Rengen Grassland Experiment (RGE) in Germany which is a long-term field experiment since 1941. Due to the experiment layout, destructive biomass sampling during the growing period was not possible. Hence, non-destructive Rising Plate Meter (RPM) measurements, which are a common method to estimate biomass in grasslands, were carried out. UAV campaigns with a Canon Powershot 110 mounted on a DJI Phantom 2 were conducted in the first growing season in 2014. From the RGB imagery, the RGB vegetation index (RGBVI) and the Grassland Index (GrassI) introduced by Bendig et al. (2015) and Bareth et al. (2015), respectively, were computed. The RGBVI and the GrassI perform very well against the RPM measurements resulting in R2 of 0.84 and 0.9, respectively. These results indicate the potential of low-cost UAV methods for grassland monitoring and correspond well to the studies of Viljanen et al. (2018) and Näsi et al. (2018).

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