Reference health
From air quality sensors to sensor networks: Things we need to learn
43/43 checkable references clean · checked 2026-07-23
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 43 checked references that resolve
resolves10.1016/j.atmosenv.2019.117066
A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018
resolves10.1080/02786820121582
Federal Reference and Equivalent Methods for Measuring Fine Particulate Matter
resolves10.1016/j.atmosenv.2019.06.026
Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors
resolves10.1016/j.envint.2017.05.005
Mapping urban air quality in near real-time using observations from low-cost sensors and model information
resolves10.1016/j.envint.2014.11.019
The rise of low-cost sensing for managing air pollution in cities
resolves10.1016/j.envint.2018.04.018
Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?
resolves10.1016/j.envpol.2018.11.065
Long-term field evaluation of the Plantower PMS low-cost particulate matter sensors
resolves10.3390/s17112478
Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)
resolves10.1089/env.2016.0044
A Citizen Science and Government Collaboration: Developing Tools to Facilitate Community Air Monitoring
resolves10.1007/s11869-020-00815-9
Schoolchildren’s exposure to PM2.5: a student club–based air quality monitoring campaign using low-cost sensors
resolves10.1016/j.snb.2015.03.031
Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide
resolves10.3390/atmos10090506
Review of the Performance of Low-Cost Sensors for Air Quality Monitoring
resolves10.1016/j.envpol.2016.12.039
Ambient and laboratory evaluation of a low-cost particulate matter sensor
resolves10.1021/acssensors.8b00074
Solution to the Problem of Calibration of Low-Cost Air Quality Measurement Sensors in Networks
resolves10.5194/amt-11-4883-2018
The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog
resolves10.1002/essoar.10500022.1
Correction and Long-Term Performance Evaluation of Fine Particulate Mass Monitoring with Low-Cost Sensors
resolves10.5194/amt-11-291-2018
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
resolves10.5194/amt-12-903-2019
Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring
resolves10.1016/j.atmosenv.2019.06.028
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
resolves10.1016/j.envint.2019.105022
Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea
resolves10.3390/s19030691
Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring
resolves10.1016/j.scitotenv.2020.141813
Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration
resolves10.1016/j.atmosenv.2019.116946
Performance evaluation of twelve low-cost PM2.5 sensors at an ambient air monitoring site
resolves10.1016/j.atmosenv.2019.117092
Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program
resolves10.1016/j.envpol.2016.12.045
Characteristics and applications of small, portable gaseous air pollution monitors
resolves10.5194/amt-11-4823-2018
Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments
resolves10.1016/j.scitotenv.2019.134123
Spatial homogeneity and heterogeneity of ambient air pollutants in Tehran
resolves10.1029/2005JD006457
PM<sub>2.5</sub> chemical composition and spatiotemporal variability during the California Regional PM<sub>10</sub>/PM<sub>2.5</sub> Air Quality Study (CRPAQS)
resolves10.1016/j.scitotenv.2004.07.021
PM2.5 chemical composition in Hong Kong: urban and regional variations
resolves10.1002/2017JD027913
Spatial Representativeness of PM<sub>2.5</sub> Concentrations Obtained Using Observations From Network Stations
resolves10.1029/2018JD028888
Estimating the Contribution of Local Primary Emissions to Particulate Pollution Using High‐Density Station Observations
resolves10.1080/10473289.2004.10470919
Spatial Variability of PM<sub>2.5</sub> in Urban Areas in the United States
resolves10.1016/j.envpol.2015.01.013
A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi'an, China
resolves10.1109/JIOT.2018.2853660
A Survey on Sensor Calibration in Air Pollution Monitoring Deployments
resolves10.1016/j.apr.2021.03.012
A new calibration system for low-cost Sensor Network in air pollution monitoring
resolves10.1109/INDIN41052.2019.8971963
MegaSense: Feasibility of Low-Cost Sensors for Pollution Hot-spot Detection
resolves10.3390/app9091947
Evaluation of Performance of Inexpensive Laser Based PM2.5 Sensor Monitors for Typical Indoor and Outdoor Hotspots of South Korea
resolves10.1016/j.snb.2018.12.049
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
resolves10.1016/j.envpol.2017.09.042
Node-to-node field calibration of wireless distributed air pollution sensor network
resolves10.3390/s21093190
From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development
The 14 references without a DOI — listed, not checked
no DOI — not checkedWHO, Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide: World Health Organization; 2006.
no DOI — not checked10.1016/j.snb.2021.130958_bib4
no DOI — not checkedJ. Gilliam, E. Hall, Reference and Equivalent Methods Used to Measure National Ambient Air Quality Standards (NAAQS) Criteria Air Pollutants-Volume IUS Environmental Protection Agency, Washington, DC, US Environmental Protection Agency, Washington, DC, EPA/600/R-16/139, (2016).
no DOI — not checkedR. Williams, V. Kilaru, E. Snyder, A. Kaufman, T. Dye, A. Rutter, et al., Air sensor guidebook, US Environmental Protection Agency, (2014).
no DOI — not checked10.1016/j.snb.2021.130958_bib23
no DOI — not checkedClosing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance
no DOI — not checkedR. Duvall, A. Clements, G. Hagler, A. Kamal, V. Kilaru, L. Goodman, et al., Performance Testing Protocols, Metrics, and Target Values for Fine Particulate Matter Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory Supplemental and Informational Monitoring Applications, US Environmental Protection Agency2021.
no DOI — not checkedR. Duvall, A. Clements, G. Hagler, A. Kamal, V. Kilaru, L. Goodman, et al., Performance Testing Protocols, Metrics, and Target Values for Ozone Air Sensors: Use in Ambient, Outdoor, Fixed Site, Non-Regulatory and Informational Monitoring Applications., US Environmental Protection Agency2021.
no DOI — not checkedA. Lewis, W.R. Peltier, E. von Schneidemesser, Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications, (2018).
no DOI — not checkedV.P. Andrea Polidori, Ashley Collier-Oxandale,Hilary Hafner, and Timothy Blakey, Community in Action: A Comprehensive Guidebook on Air Quality Sensors, (2021).
no DOI — not checkedStationary and portable multipollutant monitors for high spatiotemporal resolution air quality studies including online calibration
no DOI — not checkedA. Polidori, V. Papapostolou, B. Feenstra, H. Zhang, Field evaluation of low-cost air quality sensors, South Coast Air Quality Management District (SCAQMD), (2017).
no DOI — not checkedA. Polidori, V. Papapostolou, H. Zhang, Laboratory Evaluation of Low-Cost Air Quality Sensors—Laboratory Setup and Testing Protocol, Diamond Bar, CA: South Coast AQMD, (2016).
no DOI — not checkedF. Concas, J. Mineraud, E. Lagerspetz, S. Varjonen, X. Liu, K. Puolamäki, et al., Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis, arXiv preprint arXiv:191206384, (2019).
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-07-23 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.1016/j.snb.2021.130958"><img src="https://citestamp.com/citestamped/10.1016/j.snb.2021.130958/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.1016/j.snb.2021.130958)