AMTDAtmospheric Measurement Techniques DiscussionsAMTDAtmos. Meas. Tech. Discuss.1867-8610Copernicus GmbHGöttingen, Germany10.5194/amtd-8-7511-2015The Outdoor Dust Information Node (ODIN) – development and performance assessment of a low cost ambient dust sensorOlivaresG.gustavo.olivares@niwa.co.nzEdwardsS.National Institute of Water and Atmospheric Research (NIWA), 41 Market Place, Auckland Central 1010, Auckland, New ZealandG. Olivares (gustavo.olivares@niwa.co.nz)22July2015877511753315June201530June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/preprints/8/7511/2015/amtd-8-7511-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/preprints/8/7511/2015/amtd-8-7511-2015.pdf
The large gradients in air quality expected in urban areas present
a significant challenge to standard measurement technologies. Small,
low-cost devices have been developing rapidly in recent years and have
the potential to improve the spatial coverage of traditional air
quality measurements. Here we present the first version of the Outdoor
Dust Information Node (ODIN) as well as the results of the first
real-world measurements. The lab tests indicate that the Sharp dust
sensor used in the ODIN presents a stable baseline response only
slightly affected by ambient temperature. The field tests indicate
that ODIN data can be used to estimate hourly and daily PM2.5
concentrations after appropriate temperature and baseline corrections
are applied. The ODIN seems suitable for campaign deployments
complementing more traditional measurements.
Introduction
Global estimates indicate that between 70–90 % of the population
is exposed to average annual PM2.5 concentrations that
exceed the World Health Organization Air Quality Guideline of
10 µgm-3 (annual average). This translates into
between 2.2 and 7 millionprematuredeathsyear-1
worldwide .
A significant source of uncertainty in these estimates is the spatial
representativeness of the measurements used to generate them. The cost
of installing and operating air quality monitoring stations means that
a small number of measurements are used to represent extensive
geographical areas
. This
is particularly relevant in urban areas where large gradients are
expected in the concentration of pollutants especially in areas with
large density of sources. These gradients can not be resolved if the
spatial coverage of the measurements is inadequate
.
Also, capturing accurate data to assess personal exposure to air
pollution is critical when dealing with human health effects
. Determining
exposure in both time and space is challenging as individual behaviour
affects daily and long term exposure to air pollution
. Therefore, health effect studies require
accurate, spatially and temporally resolved air quality
data. Epidemiological studies often use ambient concentrations at
a city level as a proxy for exposure. The spatial and temporal
resolution of reported data sets means they are unlikely to adequately
reflect individual pollution exposure
. Modelling can partially compensate for
the lack of spatial resolution, but in regions with complex topography
and meteorology or unevenly distributed and poorly characterised
emission sources, model accuracy is constrained
.
The spatial and temporal resolution of air pollution data sets could
be improved by deploying additional sensors to supplement existing
monitoring networks. Where previously the cost of doing so was
prohibitive, rapid advances in technology in recent years have seen
increasing numbers of low cost air pollution sensors
available. Individuals and non-regulatory groups have seized the
opportunity and developed citizen science initiatives to monitor their
local air quality
.
Whilst low-cost sensors have increased the accessibility of air
pollution data to the general public, the sensors are not without
limitations. Instruments used for regulatory monitoring must meet high
standards of precision, accuracy, comparability and traceability
whereas low cost sensors are often provided
without calibration information and have either not been characterised
under ambient conditions or tested only for a limited time leaving
questions on their long term reliability
. This is not necessarily a problem if the
purpose of the measurements is commensurate with the capabilities of
the sensors. For example, in some cases qualitative information
indicating an increase or decrease of pollutant levels might be
sufficient. However, in order to derive quantitative information from
each sensor response, some level of evaluation against more robust
monitors is required. This is likely to result in site specific
calibrations, given the variable nature of aerosol composition. To
date very few low-cost pollution sensors have been evaluated against
compliance monitoring instruments
.
We have made some progress in the use of low cost sensors for the
assessment of personal pollution exposure with the development of the
Particles, Activity and Context Monitoring Autonomous Node (PACMAN)
for indoor exposure studies . In this
work we describe the development of the outdoor counterpart of PACMAN
the Outdoor Dust Information Node (ODIN), a new, low-cost, battery
powered sensor package to monitor outdoor particulate
concentrations. We also explore the performance of the dust sensor at
the heart of ODIN and PACMAN in terms of baseline stability. Finally,
we present results of the first field tests of the ODIN co-located
with traditional standard instrumentation at a regulatory monitoring
site in Christchurch, New Zealand. These tests indicate that the ODIN
is able to capture PM2.5 concentrations once suitable
corrections are applied which make it a viable instrument to measure
PM in combustion dominated areas complementing traditional
measurements.
MethodsODIN
The Outdoor Dust Information Node (ODIN) was developed leveraging on
the extensive open source hardware and software communities using
readily available components. In very broad terms, the ODIN is a set
of sensors that are integrated by a microcontroller that logs the
data. Figure shows a diagram of the
operation of ODIN and a view of the internal layout of the
device. Power is drawn from either the battery or the solar panel
depending on the sunshine. To maximize the battery life, the ODIN uses
its microcontroller's sleep mode and only wakes up to take
a single measurement every minute.The sensors feed information to the
microcontroller which in turn saves it to the memory card. The
electric design files are available from
and the detail of the components is as follow.
Dust sensor: the Sharp Optical Dust Sensor GP2Y1010AU0F
was selected because of its low power consumption
.The basic measurement principle of
this sensor is infra-red light scattering. An infra-red light emitting
diode (IRLED) and a photodetector (PD) are arranged 90∘ to
each other around the sensing volume. The particles in the sensing
volume scatter the light from the IRLED which is measured by the
PD. This sensor requires 5–7 Vdc to operate and outputs
a 0–3 Vdc signal proportional to the total dust mass
measured. The operation of this sensor is as a 10 ms sampling
cycle consisting of a high phase of 0.32 ms and a low phase of
9.68 ms. A single measurement is taken 0.28 ms into
the high phase . To smooth the output
of this sensor, the microcontroller takes 100 samples of 10 ms
each and records their average.
Note that the dust sensor does not have a defined measurement size
range but that the upper and lower cut off sizes are controlled by the
sensitivity of the internal amplifying circuit. It is expected that
this circuit has significant inter-instrument variability as well as
its response to depend on the type of aerosol sampled.
Temperature and relative humidity: the AM2302
temperature-humidity sensor from AOSONG was selected for its size,
power consumption and ease of interface. According to the
manufacturers, the AM2302 is accurate to within 2 % RH and
0.5 ∘C and it is able to operate in a wide range of
conditions .
Microcontroller: Sparkfun's Arduino Pro Mini
based on the ATMega328 microcontroller
was selected primarily because of its ease of use and the programming
libraries available for the Arduino system. The firmware used on this
microcontroller is available in the project's GitHub repository
.
Memory: a 2 GB micro SD card is used to log the
data. Adafruit's micro SD card adapter board
was used to interface with the
microcontroller. The files created by the units are labelled as
YYYYMMDD.TXT, e.g. “20120520.TXT” corresponds to the file for the 20 May 2012.
Clock: the DS3231 based temperature compensated RTC
Chronodot v2.0 was used
to keep track of time. This component is documented to have a drift
smaller than one minute per year. A specific library was used to
communicate with this component
.
Power: a lithium ion polymer battery pack
(3.7 V) delivers the power required for the
unit. A 6 Ah battery is capable of powering the system for six
weeks. A solar panel was added to the design which under ideal
conditions of sunshine, is capable of powering the system as well as
recharging the battery for long term deployments. Unfortunately the
chosen combination of solar panel and charging circuit was unable to
recover the battery so in future revisions a new solution will be
tested.
Figure also shows the internal layout of
the ODIN. As the ODIN is a passive instrument, i.e. there is no forced
air sample, the dust sensor is located against the wall of the
enclosure with its opening directly towards the outside of the
instrument, protected by a rain shield and a coarse metal mesh.
The cost of the materials for the prototype ODIN is less than USD 300
(June 2015) and each unit takes about three hours to put together.
Deployments
Several short deployments were used to capture the data presented
here. The first one was aimed at characterise the zero response of the
dust sensor at the heart of the ODIN. Eight Sharp dust sensors were
set up in a custom made, sealed and uninsulated enclosure and placed
in a controlled environment for five days at a temperature of 22±1∘C and a relative humidity of 50±5 %. Following this deployment, the same eight units were placed
outdoors in the same sealed container but exposed to sunshine. The
sensors were subject to temperatures ranging from 6 to
26 ∘C. More than 4000 records (66 h) were obtained.
To evaluate the response of the ODIN to woodsmoke and assess its
performance against standard air quality instrumentation, one unit
(ODIN_01) was located at Environment
Canterbury's
http://ecan.govt.nz
air quality
monitoring site at Coles Place (-43.511236∘ S;
172.633687∘ W) between the 24 July and the 14 August
2014. The unit was attached to the meteorological mast at the same
height as the inlets for the PM10 and PM2.5
instruments at the site (Fig. ). The data
were manually downloaded from the internal memory at the end of the
test period.
Data from the air quality monitoring station were obtained from
Environment Canterbury. These data were provided before the normal QA
process as 60 min moving average, every 10 min of, PM10,
PM2.5 measured by a TEOM-FDMS instrument, wind speed and
direction and air temperature. The details of these measurements are
described by .
Data analysis
All the data analysis was done using and the Openair R package . The raw data are
available from and the analysis scripts
are available from .
The baseline of each instrument was obtained by averaging all the
measurements taken during the temperature controlled baseline
deployment described above. This baseline was then subtracted from the
raw measurements to obtain the baseline corrected signal. The
temperature response of the sensors was estimated using data from the
variable temperature baseline deployment. A linear regression between
the baseline corrected signal and the ambient temperature in
the enclosure was performed to describe the effect of ambient
temperature on the response of the Sharp dust sensors.
For the co-location deployment the data analysis included the
following: we first removed baseline drift, then corrected for
temperature effects and then found the calibration coefficients to
approximate PM2.5.
More in detail, the baseline drift was estimated from the linear trend
of the raw response from ODIN. This baseline was subtracted from the
raw ODIN output.
Then, a temperature correction was applied by first finding the linear
regression between the de-trended ODIN signal and the instrument
temperature. This was done by considering only data above
10 ∘C. Figure shows
that above 10 ∘C the temperature effect dominates the
ODIN response. The data were then corrected by subtracting the linear
regression with the temperature from the de-trended ODIN output.
To obtain the correction coefficients to estimate PM2.5,
this de-trended and temperature corrected ODIN signal was used in
a linear regression with PM2.5 as the dependent variable and
the ODIN signal. To explore the stability of this correction, the
linear regression was performed on the first 1/3 of the data while the
error estimate (RMSE) was calculated using the whole dataset.
ResultsDust sensor behaviour
Table shows the results of the baseline test for
the eight sensors tested and highlights the inter-instrument
variability for the Sharp dust sensor with the range of baselines
between 0.24 to 28 mV. For each sensor, the standard deviation
of the baseline estimate suggests a relatively stable baseline for the
sensors tested.
In our previous work with similar sensors we found a linear
relationship between the baseline response of the dust sensor and
ambient temperature in field deployments
. Our original hypothesis was that
this interference by temperature was a property of the sensor so, as
described above, we placed several units in a clean environment
exposed to a range of temperatures.
If the effect of ambient temperature on the Sharp dust sensor readings
was related to the sensor itself we would expect that there would be
a significant increase in their baseline response in warmer
temperatures even when placed in a clean environment.
As shown in Fig. ,
ambient temperature appears to change the response of the sensors by
around 0.3 mV for every 1 ∘C. This, however is
a much weaker response to temperature than what is found when using
the sensor as part of the ODIN. Table shows that
the temperature correction applied to the ambient data is of
4.4 mV for every 1 ∘C. This suggests that the
impact of temperature on the response from the Sharp dust sensor is
dominated by impacts on the aerosol instead of being a property of the
devices. However, future tests will further explore this behaviour.
ODIN's performance evaluation
Figure shows that the raw ODIN data show
a significant baseline drift and they only capture a small fraction of
the features observed in the PM2.5 time series. The baseline
correction showed a significant drift of almost -30 % during the
deployment (Table ). After removing the baseline
drift, the temperature correction was estimated as
4.4 mV∘C-1 (Table ) which, as
noted above, is significantly higher than what was obtained in clean
conditions but similar to what was observed by our group in a previous
work .
Table shows that, after applying the baseline and
temperature corrections, a calibration factor of 0.6 and an offset of
19 µgm-3 is sufficient for the ODIN data to capture
most of the variability of
PM2.5. Figure shows that the
corrected ODIN data captured both the timing and intensity of all the
high concentration events observed in the PM2.5
data. However, for PM2.5 concentrations below
25 µgm-3 the scatter plot shows a less defined
relationship between ODIN and PM2.5.
Figure also shows that the correction
calculated for the beginning of the deployment significantly
overestimates the low concentrations observed towards the end of the
dataset. This could be due to a residual baseline drift not accounted
for by the method used but it does not seem to affect the fit for the
higher concentrations.
Finally, and to evaluate the behaviour of the ODIN's inlet, we
compared the error in the ODIN estimate (PM2.5- ODIN)
with the observed wind speed and
direction. Figure shows that the error is uniform
with wind direction but has a small negative trend with wind
speed. This difference in higher wind speeds could be related to
a different source mix than in low wind speeds which typically occur
during night time (). However,
PM2.5 concentrations are generally lower in high wind speeds
which, as indicated before, is closer to the detection limit of the
Sharp dust sensor.
Conclusions
The small, low-cost dust monitor ODIN, based on an optical dust sensor
has been shown to be able to capture most of the features of the
PM2.5 time series in a wood-smoke impacted area after
suitable baseline and temperature corrections are applied.
In controlled conditions, the optical dust sensor used in ODIN was
shown to have a stable baseline with a small dependence with ambient
temperature. However, field data indicates a significant baseline
drift and temperature interference. The baseline drift of nearly
30 % in three weeks indicates that regular checks will be required
if the ODIN is to be used for extended periods of time.
The fact that the temperature interference observed in the field tests
was more significant than that observed in lab conditions suggests
that the changes in the response of the ODIN to ambient temperature
are related to the nature of the aerosol sampled and not only to the
sensor themselves. This has implications for the transferability of
the correction factors to other locations.
Also, the performance of the ODIN is worst for PM2.5
concentrations below 25 µgm-3. This is to be expected
as the datasheet of the Sharp dust sensor has a response curve
starting at 100 µgm-3. This is a common
characteristic of low-cost sensors and one
should be cautious when using these sensors in low-concentration
environments as their response may reflect more their noise than
a real measurement.
A simple test of the performance of ODIN against wind speed and
direction found that the inlet performed as expected with no wind
direction bias and only a small negative wind speed trend. This
negative trend with wind speed may also be related to the fact that
high wind speeds relate to low concentrations, where the Sharp dust
sensor perform worst, and that a different source mix may be dominant
in those conditions.
Nevertheless, the ODIN is shown to be a useful complement to
regulatory measurements for campaigns and its calibration parameters
are stable for deployments of around one month.
The next steps in understanding the response of the ODIN to urban
aerosols are to explore more in detail the performance of the Sharp
dust sensor to specific aerosol populations (size and composition),
explore the inter-instrument variability and the drivers for the
baseline drift and temperature interference found here. We also expect
to explore the transferability of correction coefficients with data
currently being captured in Auckland and Christchurch and it is
expected to generate results by September 2015.
Future versions of the ODIN are expected to include distributed
telemetry for high density deployments at a city scale as well as
improved energy efficiency for long term deployments.
Acknowledgements
The development of ODIN was funded through NIWA's Atmosphere and
Health 2013-14 programme (ATHS1301). The ODIN deployments and data
analysis were funded through NIWA's Impacts of Air Pollutants 2014-15
programme (CAAP1504). Also, we gratefully acknowledge the support from
Environment Canterbury through Teresa Aberkane in giving us access to
Christchurch's air quality monitoring sites and data.
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Results from the dust sensor baseline experiment where eight Sharp dust sensors were placed in a clean, temperature controlled environment.
Details of the corrections applied to the data. The error estimates correspond to the 95 % confidence interval of the coefficients. The RMSE and R2 correspond to the whole dataset estimates.
Internal layout and simplified diagram of ODIN showing the main components and the flow of power (red arrows) and information (black arrows). The picture on the right shows how the components fit inside the enclosure with the dust sensor attached to the lid of the unit and exposed through a hole directly over its sensing area. See for the full electrical schematics.
Deployment of ODIN_01 at ECan's air quality monitoring site. The circles show the location of the unit attached to the meteorological mast.
Scatter plot of the baseline corrected ODIN data against instrument temperature. The full line corresponds to the estimated temperature correction based on these data.
Scatter plot of one minute dust sensor data (mV) against temperature (∘C). Colour indicates the different units.
Hourly time series of PM2.5 (top plot) and raw output from ODIN (bottom plot) highlighting the significant drift in the ODIN's baseline. Note the different units of the ODIN raw response and the different scales on the plots.
Comparison between the standard measurements. Panel (a) shows the hourly time series of PM2.5 and the calibrated ODIN data. Panel (b) shows the equivalent daily time series. Panels (c and d) show the scatter plots of for the hourly and daily data with the black lines indicating the 1:1 line.
Comparison between the error in the ODIN PM2.5 estimate as a function of wind direction (a) and wind speed (b). The error estimate is calculated as hourly values of PM2.5- ODIN.