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

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 256
Author(s):  
Pengfei Han ◽  
Han Mei ◽  
Di Liu ◽  
Ning Zeng ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Short Term Memory ◽  
Hot Spot ◽  
Low Cost ◽  
Field Evaluation ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
National Monitoring

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.

scholarly journals Development of a low-cost outdoor carbon monoxide analyzer applied to the city of Oran, Algeria.

2020 ◽  
Author(s):  
Farid RAHAL ◽  
Noureddine BENABADJI ◽  
Mohamed BENCHERIF ◽  
Mohamed Menaouer BENCHERIF
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Electrochemical Sensor ◽  
Low Cost ◽  
Atmospheric Pollutants ◽  
Pollution Monitoring ◽  
Coefficient Of Determination ◽  
Air Pollution Monitoring ◽  
The City

Abstract In Algeria, air pollution is classified as a major risk by the law. However, this risk is underestimated because there is no operational network for measuring air quality on a continuous basis.Despite the heavy investments made to equip several cities with these measurement systems, they are out of order due to a lack of continuous financial support.The alternative to the absence of these air pollution measurement networks can come from the recent development of electrochemical sensor technologies for air quality monitoring which arouses a certain interest because of their miniaturization, low energy consumption and low cost.We developed a low-cost outdoor carbon monoxide analyzer called APOMOS (Air pollution Monitoring System) based on electrochemical sensor managed by microcontroller. An application developed with the Python language makes it possible to manage process and analyze the collected data.In order to validate the APOMOS system, the recorded measurements are compared with measurements taken by a conventional analyzer.Comparison of the measurements resulting from conventional analyzer and those resulting from the APOMOS system gives a coefficient of determination of 98.39 %.Two versions of this system have been designed. A fixed version and another embedded, equipped with a GPS sensor. These 2 variants were used in the city of Oran in Algeria to measure the concentration of carbon monoxide continuously.The targeted pollutant is carbon monoxide. However, the design of the APOMOS system allows its evolution in an easy way in order to integrate other sensors concerning the various atmospheric pollutants.

scholarly journals H2020 project CAPTOR dataset: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network.

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll
Keyword(s):  
Air Pollution ◽  
Low Cost ◽  
Monitoring Network ◽  
Pollution Monitoring ◽  
Raw Data ◽  
Air Pollution Monitoring

scholarly journals Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

2018 ◽  
Vol 11 (6) ◽  
pp. 3717-3735 ◽  
Author(s):  
Alessandro Bigi ◽  
Michael Mueller ◽  
Stuart K. Grange ◽  
Grazia Ghermandi ◽  
Christoph Hueglin
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Low Cost ◽  
Multivariate Linear Regression ◽  
Pollution Level ◽  
Support Vector ◽  
Non Linear ◽  
Linear Algorithms ◽  
Urban Sites

Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE  <  5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10 ppb for Random Forest and 15 ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

scholarly journals Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

2017 ◽  
Vol 41 (6) ◽  
pp. 648-664 ◽  
Author(s):  
Sérgio Henrique Godinho Silva ◽  
Anita Fernanda dos Santos Teixeira ◽  
Michele Duarte de Menezes ◽  
Luiz Roberto Guimarães Guilherme ◽  
Fatima Maria de Souza Moreira ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Soil Properties ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
High Accuracy ◽  
Important Variable ◽  
X Ray ◽  
Element Contents ◽  
Fluorescence Spectrometer

ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.

National Trace-Element Air Pollution Monitoring Survey Using Epiphytic Lichens

1991 ◽  
Vol 23 (2) ◽  
pp. 139-165 ◽  
Author(s):  
J. E. Sloof ◽  
H. Th. Wolterbeek
Keyword(s):  
Air Pollution ◽  
Trace Element ◽  
Dispersion Model ◽  
Pollution Monitoring ◽  
Deciduous Tree ◽  
Data Sets ◽  
Air Pollution Monitoring ◽  
Geographical Concentration ◽  
National Monitoring ◽  
Concentration Patterns

AbstractTwo national monitoring surveys were carried out within 5 years, using Parmelia sulcata as a biomonitor of trace-element air pollution. The method of sampling was standardized. The lichen samples were analysed by neutron activation analysis. Local variations in element concentrations in lichens from various deciduous tree species from several sampling sites were established. The geographical concentration patterns obtained from the lichen data sets agreed with the element concentration gradients obtained from one dispersion model and measured data of atmospheric concentrations and deposition. Comparison of the two lichen data sets showed the relationship of the geographical concentration patterns with time. Combination of the available data gave insight into the possibility of localization of pollution sources.

scholarly journals Wind direction sensor based on thermal anemometer for olfactory mobile robot

2019 ◽  
Vol 13 (2) ◽  
pp. 475 ◽  
Author(s):  
Helmy Widyantara ◽  
Muhammad Rivai ◽  
Djoko Purwanto
Keyword(s):  
Mobile Robot ◽  
Success Rate ◽  
Wind Direction ◽  
Tracking System ◽  
Low Cost ◽  
Pollution Monitoring ◽  
Sensor System ◽  
Positive Temperature ◽  
Air Pollution Monitoring ◽  
Thermal Anemometer

A wind direction sensor has been implemented for many applications, such as navigation, weather, and air pollution monitoring. In an odor tracking system, the wind plays the important role to carry gas from its source. Therefore, the precise, low-cost, and effective wind direction sensor is required to trace the gas source. In this study, a new design of wind direction sensor has been developed using thermal anemometer principle with the main component of the positive temperature coefficient thermistor. Three anemometers each of which has different directions are used as inputs for the neural network to determine the direction of the wind automatically.The experimental results show that the wind sensor system is able to detect twelve wind directions. A mobile robot equipped with this sensor system can navigate to a wind source in the open air with a success rate of 80%.This system is expected to increase the success rate of the mobile robot in searching for dangerous leaking gas in the open air.

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