[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Topalović et al., 2019 - Google Patents

In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network …

Topalović et al., 2019

View PDF
Document ID
13510383701464007481
Author
Topalović D
Davidović M
Jovanović M
Bartonova A
Ristovski Z
Jovašević-Stojanović M
Publication year
Publication venue
Atmospheric Environment

External Links

Snippet

The current compliance networks of automatic air-quality monitoring stations in large urban environments are not sufficient to provide spatial and temporal measurement resolution for realistic assessment of personal exposure to pollutants. Small low-cost sensor platforms with …
Continue reading at eprints.qut.edu.au (PDF) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0009General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0036Specially adapted to detect a particular component
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0009General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0073Control unit therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection circuits for computing concentration
    • G01N21/274Calibration, base line adjustment, drift correction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0006Calibrating gas analysers
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01JMEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J1/00Photometry, e.g. photographic exposure meter
    • G01J1/42Photometry, e.g. photographic exposure meter using electric radiation detectors
    • G01J1/4228Photometry, e.g. photographic exposure meter using electric radiation detectors arrangements with two or more detectors, e.g. for sensitivity compensation

Similar Documents

Publication Publication Date Title
Topalović et al. In search of an optimal in-field calibration method of low-cost gas sensors for ambient air pollutants: Comparison of linear, multilinear and artificial neural network approaches
Kang et al. Performance evaluation of low-cost air quality sensors: A review
Li et al. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2. 5
Feenstra et al. Performance evaluation of twelve low-cost PM2. 5 sensors at an ambient air monitoring site
Borrego et al. Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise
Cordero et al. Using statistical methods to carry out in field calibrations of low cost air quality sensors
Brauer et al. Examination of monitoring approaches for ambient air pollution: A case study for India
Bigi et al. Performance of NO, NO 2 low cost sensors and three calibration approaches within a real world application
Morawska et al. Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?
Casey et al. Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors
van Zoest et al. Calibration of low-cost NO2 sensors in an urban air quality network
Schneider et al. Mapping urban air quality in near real-time using observations from low-cost sensors and model information
Jerrett et al. A review and evaluation of intraurban air pollution exposure models
Li et al. Predicting ground-level PM2. 5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach
Engel-Cox et al. Toward the next generation of air quality monitoring: particulate matter
Liang et al. Field comparison of electrochemical gas sensor data correction algorithms for ambient air measurements
Kloog et al. A new hybrid spatio-temporal model for estimating daily multi-year PM2. 5 concentrations across northeastern USA using high resolution aerosol optical depth data
Broday et al. Wireless distributed environmental sensor networks for air pollution measurement—The promise and the current reality
Considine et al. Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network
Miskell et al. Solution to the problem of calibration of low-cost air quality measurement sensors in networks
McFarlane et al. Application of Gaussian mixture regression for the correction of low cost PM2. 5 monitoring data in Accra, Ghana
Malings et al. Application of low-cost fine particulate mass monitors to convert satellite aerosol optical depth to surface concentrations in North America and Africa
Li et al. Characterizing the aging of alphasense NO2 sensors in long-term field deployments
Wu et al. A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation
Li et al. From air quality sensors to sensor networks: Things we need to learn