PMID- 30671683
OWN - NLM
STAT- MEDLINE
DCOM- 20190228
LR  - 20200225
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Print)
IS  - 0167-6369 (Linking)
VI  - 191
IP  - 2
DP  - 2019 Jan 22
TI  - Analysing the performance of low-cost air quality sensors, their drivers, 
      relative benefits and calibration in cities-a case study in Sheffield.
PG  - 94
LID - 10.1007/s10661-019-7231-8 [doi]
LID - 94
AB  - Traditional real-time air quality monitoring instruments are expensive to install 
      and maintain; therefore, such existing air quality monitoring networks are 
      sparsely deployed and lack the measurement density to develop high-resolution 
      spatiotemporal air pollutant maps. More recently, low-cost sensors have been used 
      to collect high-resolution spatial and temporal air pollution data in real-time. 
      In this paper, for the first time, Envirowatch E-MOTEs are employed for air 
      quality monitoring as a case study in Sheffield. Ten E-MOTEs were deployed for a 
      year (October 2016 to September 2017) monitoring several air pollutants (NO, 
      NO(2), CO) and meteorological parameters. Their performance was compared to each 
      other and to a reference instrument installed nearby. E-MOTEs were able to 
      successfully capture the temporal variability such as diurnal, weekly and annual 
      cycles in air pollutant concentrations and demonstrated significant similarity 
      with reference instruments. NO(2) concentrations showed very strong positive 
      correlation between various sensors. Mostly, correlation coefficients (r values) 
      were greater than 0.92. CO from different sensors also had r values mostly 
      greater than 0.92; however, NO showed r value less than 0.5. Furthermore, several 
      multiple linear regression models (MLRM) and generalised additive models (GAM) 
      were developed to calibrate the E-MOTE data and reproduce NO and NO(2) 
      concentrations measured by the reference instruments. GAMs demonstrated 
      significantly better performance than linear models by capturing the non-linear 
      association between the response and explanatory variables. The best GAM 
      developed for reproducing NO(2) concentrations returned values of 0.95, 3.91, 
      0.81, 0.005 and 0.61 for factor of two (FAC2), root mean square error (RMSE), 
      coefficient of determination (R(2)), normalised mean biased (NMB) and coefficient 
      of efficiency (COE), respectively. The low-cost sensors offer a more affordable 
      alternative for providing real-time high-resolution spatiotemporal air quality 
      and meteorological parameter data with acceptable performance.
FAU - Munir, Said
AU  - Munir S
AUID- ORCID: 0000-0002-7163-2107
AD  - Department of Civil and Structural Engineering, The University of Sheffield, 
      Sheffield, S1 3JD, UK. smunir2@sheffield.ac.uk.
FAU - Mayfield, Martin
AU  - Mayfield M
AD  - Department of Civil and Structural Engineering, The University of Sheffield, 
      Sheffield, S1 3JD, UK.
FAU - Coca, Daniel
AU  - Coca D
AD  - Department of Automatic Control and Systems Engineering, University of Sheffield, 
      Sheffield, S1 3JD, UK.
FAU - Jubb, Stephen A
AU  - Jubb SA
AD  - Department of Civil and Structural Engineering, The University of Sheffield, 
      Sheffield, S1 3JD, UK.
FAU - Osammor, Ogo
AU  - Osammor O
AD  - Air Quality Monitoring & Modelling, Sheffield City Council, Howden House, 1 Union 
      Street,, Sheffield, S1 2SH, UK.
LA  - eng
GR  - NA/Engineering and Physical Sciences Research Council/
PT  - Evaluation Study
PT  - Journal Article
DEP - 20190122
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
RN  - 0 (Air Pollutants)
RN  - 0 (Particulate Matter)
RN  - 31C4KY9ESH (Nitric Oxide)
RN  - 7U1EE4V452 (Carbon Monoxide)
RN  - S7G510RUBH (Nitrogen Dioxide)
SB  - IM
MH  - Air Pollutants/*analysis
MH  - Air Pollution/*analysis
MH  - Calibration
MH  - Carbon Monoxide/analysis
MH  - Cities
MH  - Environmental Monitoring/*instrumentation/methods
MH  - Linear Models
MH  - Nitric Oxide/analysis
MH  - Nitrogen Dioxide/analysis
MH  - Particulate Matter/analysis
MH  - Time Factors
MH  - United Kingdom
PMC - PMC6343017
OTO - NOTNLM
OT  - Air pollution monitoring
OT  - Envirowatch E-MOTEs
OT  - Generalised additive model
OT  - Sensor cost
OT  - Sensor networks
EDAT- 2019/01/24 06:00
MHDA- 2019/03/01 06:00
PMCR- 2019/01/22
CRDT- 2019/01/24 06:00
PHST- 2018/09/23 00:00 [received]
PHST- 2019/01/10 00:00 [accepted]
PHST- 2019/01/24 06:00 [entrez]
PHST- 2019/01/24 06:00 [pubmed]
PHST- 2019/03/01 06:00 [medline]
PHST- 2019/01/22 00:00 [pmc-release]
AID - 10.1007/s10661-019-7231-8 [pii]
AID - 7231 [pii]
AID - 10.1007/s10661-019-7231-8 [doi]
PST - epublish
SO  - Environ Monit Assess. 2019 Jan 22;191(2):94. doi: 10.1007/s10661-019-7231-8.