Technical ReportPDF Available

PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003


Abstract and Figures

A short review of available low-cost sensors for PM2.5 monitoring including Plantower PMS7003, Nova Fitness SDS011, Honeywell HPMA115S0. Correlation of PM2.5 data from PMS7003 and SDS011 with a BAM monitor from the US. Embassy, Hanoi, Vietnam was reported with the urbane ambient condition.
Content may be subject to copyright.
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 1/6
PM2.5 low-cost sensors and calibration data for SDS011 and
Binh Nguyen, Independent Researcher, Hanoi, Vietnam
1. What is laser-scattering method
Three popular models of low-cost sensors for PM2.5 in 2019 are Plantower PMS7003, Nova Fitness SDS011
and Honeywell HPMA115S0. Plantower has other older models such as PMS5003, and PMS3003. The fourth
version is not low-cost by a developing country standard, Dylos DC1100 Pro.
Laser-scattering method is mentioned in low-cost sensors with dierent nuances such as based on the
principle of laser scattering (SDS011, PMS7003), laser-based sensor (HPM), and true laser particle
counter (DC1100). The distinction is needed because laser emits a narrow region of wavelengths, created a
perception of a higher accurate device. Light scattering is used interchangeably in this article. In a true
technical term, light scattering referred to a lower accuracy, less expensive LED as the light source. The
laser is used in laboratory-grade equipment such as Met One E-Sampler or GRIMM EDM 180.
What I learned from the Internet about laser-scattering method is shining a laser beam onto a particle,
light can be scattered, diracted, absorped or extincted. Measuring size of a particle and how many
particle in that size is based Mie Theory with inelastic scattering. To read more about laser-scattering, refer
to this 7-page write-up.
Table 1: Basic technical specication of lowcost sensors
# PMS7003 SDS011 HPMA115S0 DC1100 Pro
Price ~13$ ~$19 ~$19 ~$290
Range (µg/m 0-500 (1000)* 0-999 0-1000 N/A
Error 10% and ±10µg/m 15% and
15% and
Lifespan (h) 8,000 8,000 20,000 several years
Communication Serial with headers, baudrate 9600
Mode Continuous or passive query by Serial input 1-minute or 1-hour
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 2/6
# PMS7003 SDS011 HPMA115S0 DC1100 Pro
Output PM ,PM ,PM , counts with size
of 0.3, 0.5, 10, 2.5, 5, 10 µm
PM ,
PM in
in integer
small (> 0.5µm) and large
(>2.5µm) for Pro model
Software Arduino, Python libraries Data logger (Windows),
customized Python script
*: eective and maximum ranges
If we only look at the specications, PMS7003 is very promising with the lowest cost, smallest footprint,
available third-party library for DIY. Particle counting with 0.3 µm at 50% and 0.5µm at 98% eciency
making this sensor stands out for its wealth of outputs. The SDS011 has a sampling hose that can draw
sample upto 1m. This is convenient for a setup with no fan installed. HPMA115S0's datasheet is pleasant
compared to the rst and the brand name brings some ease as well. DC1100 has been used as a middle-
device and has shown a good correlation to lab-grade equipment.
2. Comparative monitoring
The rst use for the low-cost sensors is comparative monitoring, in which values of PM and PM (PMs)
are compared with the time event. This analysis provides patterns of the concentration with the change of
experimental conditions such as road vs. home, cooking vs. none, day vs. night, construction site vs.
residential home. The comparative analysis is suitable for high-school and undergrad students to have
some peek into the change of PMs with the condition.
I myself conducted three studies by the time writing this post using the comparative analysis. The rst
one is to evaluate the PMs inside a closed room and the balcony during a 5-day vacation. The next two are
PMs emitted by a rewood cook stove and removal eciencies of face masks to PMs. The details of these
studies posted here. For each study, I compared PMs concentration from a sensor measuring the
background or ambient concentration and the other for the experimental conditions. When one sensor is
available, I have to move between experimental conditions and the one for the background.
3. Summary of studies on observed value and calibration
Comparative studies are simple and useful for hobyist and personal uses. When communicating results to
the public or for a research level, the accuracy of the low cost devices to the standard method is a must.
Table 1 listed the error of PMS7003, SDS011, HPMA115S0 is 10% and ±15µg/m which is a good start, but to
be sure with local conditions, the so-called "co-location" study is needed. In a co-location study, all
devices are placed in proximity and expose to the same ambient condition. The output of sensors is cross-
checked with additional to a calibration curve based a reliable device using the reference method.
Available testing on optical sensors measuring PMs including the low-cost class are carried out by the US.
EPA and the Air Quality Sensor Performance Evaluation Center (AQ-SPEC). The study done by the US. EPA
included mid-range devices in term of price, from $500-$2500. Only DC1100 Pro was included in this
study in North Carolina, US. The AQ-SPEC carried extensive testing on commerical devices ranging from
$150-$300. Testing bare sensors is not included rather a package with possible customized calibration by
the device's maker. The reference device is Federal Equipment Monitor (FEM) approval such as GRIMM
(EDM 180) or Met One (BAM-1020).
Table 2: Correlation value (R )** and linear regression from collocation eld study
# PMS7003 SDS011 HPMA115S0 DC1100 Pro
US .EPA N/A N/A N/A 0.5-0.6
AQ-SPEC (R 0.85 (Edimax PMS5003)*,
0.93-0.97 (PurpleAir,
N/A N/A 0.81
1 2.5 10 2.5
2.5 10
2.5 10
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 3/6
# PMS7003 SDS011 HPMA115S0 DC1100 Pro
-8E12x +5E-
05x+3.97 (x:
Johnston (2019), UK,
ρ as Pearson
0.88 N/A 0.85 N/A
Badura (2018),
R =0.73-0.75, FEM=0.413x R =0.66-
Liu (2018), Norway N/A R =0.71-
*PMS5003 and PMS7003 has similar observed values **R , another name: Goodness-of-Fit, a value = 1 shows a perfect t to the
regression line. A value = 1 shows no correlation between two axes
Over 10 reports and journal articles I skimmed through, a conscensus summary as follow:
SDS011, PMS7003, HPMA115S0 overestimated PMs concentration by FEM device
The R values is in the region of 0.6-0.8 for the eld test
4. Personal experience on observed value and calibration
During the rst 6-month of 2019, I collaborated with SPARC lab, Hanoi University of Science and
Technology, Vietnam to evaluate the PMS7003 sensors. The PMS7003 is the heart of AirSENSE kit that has
been used as a demo for high-school and students for STEM education. This kit could also use as low-cost
PM2.5 monitor with additional calibration. In addition to the sensors from SPARC's lab, I bought one
SDS011 sensor and collected PM2.5 concentration measured by the US. Embassy at Vietnam (Hanoi) as the
reference station. The reference station was using MetOne BAM 1020. The station stopped working from
the end of April 2019 for "technical diculty". The location of PMS and SDS011 is 5.3 km to the South-
West of the reference station. During winter and spring seasons, the dominant wind direction in Hanoi is
The data is collected using Raspberry Pi with Python script or available Arduino libraries for ESP8266 or
ESP32. I also coded up a Python library for PMS7003 to run simultanously 4 sensors in one script. The code
also works with other x003 sensors from Plantower since they use the same bitstream format.
2 2
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 4/6
Fig. 1: Location and distance between the PMS7003 and SDS011 site to the reference station (MetOne BAM 1020).
The graph below shows over 60-day collecting data. The data was cleaned up for peaks that is larger than
300 (µg/m ). PMS7003 produced more abnormal peaks than SDS011. Finally, a total of 1451 rows,
equipvalents to 1451 hours, was used for further analysis.
Fig. 2: One-hour averaged data from PMS7003 and SDS011 and from the reference station (MetOne BAM 1020).
Using Seaborn library, the relational plots between each sensor to the reference stations are shown below.
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 5/6
Fig. 3: Correlation of 1-hour average PM2.5 from SDS011 and PMS7003 with MetOne BAM 1020.
The graph shows at low concentration, the correlation between PMS7003 and SDS011 to the reference
station is high. At a higher PM , concentration, a cluster of points above the tting lines suggested some
systematic changes relative to the reference.
Correlation between SDS011 and PMS7003 is surprisingly well which could interpret that either sensor is
suitable for a low-cost device for PM monitor.
Fig. 4: Correlation of 1-hour average PM2.5 from SDS011 with PMS7003.
The Seaborn library provides nice visualization but does not have available tting statistics. Nevertheless,
using library such as SkLearn, we can t the data with linear regression with an option to intercept to the
origin. The results are summarized in Table 3.
Table 3: Linear regression from 60-day data collection, Hanoi Vietnam.
# PMS7003* SDS011* SDS011/PMS7003**
R0.66 0.51 0.84
Slope 0.66 0.77 0.81
*: x axis for SDS011 or PMS7003, y axis for BAM (FEM) **: x axis for PMS7003, y axis for SDS011
The results in Table 3 are inline with the literature review in Table 2, in which SDS011, PMS7003
overestimated the PM by a reference method or a FEM device. The PMS7003 displayed a higher
overestimation than SDS011 and a higher R as well.
6/19/2019 b-io | PM2.5 low-cost sensors and calibration data for SDS011 and PMS7003 6/6
Fig. 5: PM concentration by SDS011, PMS7003 after adjustment with the coecien factors in Table 3 along with
MetOne BAM 1020 .
Other sensors such as HPMA115S0, one Dylos DC1100 Pro and a second one SDS011 has been in operation
recently. The data is not sucient to included for an analysis at this time of writing.
5. Summary
Located 5.3km away from a reference station is less than ideal. Variation of wind and PM sources
introduces more uncertainty. Nevertheless, this analysis and data show resonable ttings of PM
monintoring from low-cost sensors.PM by Nova Fitness SDS011 overestimated 30% than by BAM
monitor. The Plantower PMS7003 overestimated 52% than the BAM monitor. The goodness-of-tting
(R ) of PMS7003 is 0.66 and SDS011's is 0.51 to BAM monitor. The R found in this study is lower than the
other studies. Collorating data between SDS011 and PMS7003 hows a R =0.84 which supports the distance
between BAM monintor and the low-cost sensors contributed to a lower R . In addition, the report will be
updated with additional data from Honeywell HPMA115S0 and Dylos DC1100 Pro devices.
6. Additional information
For more information about technical of each sensor mentioned above, checkout its datasheet:
Plantower PMS7003
Nova Fitness SDS011
Honeywell HPM
Dylos DC1100 Pro
2 2
Copyright © 2019 All Rights Reserved.
... This averaging is very important to check if the low-cost laser sensor does not get dirty over time, which can lead to a measurement deterioration. More accurate calibration analyses are in line with other authors (Nguyen 2019). ...
Full-text available
The article presents the study of Particulate Matter air pollution with PM 1 , PM 2,5 and PM 10 by means of a low-cost sensors mounted on Unmanned Aerial Vehicles. The article is divided into two parts. In first part pollution measurement system is described. In second part expert system for optimization of flight parameters is described. The research was conducted over a municipal cemetery area in Poland. The obtained results were analyzed through an inductive knowledge management system (decision tree method) for classification analysis of air pollution. The decision tree mechanism would be used to optimize flight parameters taking into account the air pollution parameters. The analysis was made from the influence of PM concentration point of view, depending on the altitude. The decision tree method was used, which allowed to determine, among other aspects, which PM indicator should be measured and which altitude plays a greater role in the optimization of air pollution measurements by means of cheap sensors mounted on drones. As a result of the analysis, the optimum flight altitude of the measurement drone in the specified area was determined.
Full-text available
This article presents the capabilities and selected measurement results from the newly developed low-cost air pollution measurement system mounted on an unmanned aerial vehicle (UAV). The system is designed and manufactured by the authors and is intended to facilitate, accelerate, and ensure the safety of operators when measuring air pollutants. It allows the creation of three-dimensional models and measurement visualizations, thanks to which it is possible to observe the location of leakage of substances and the direction of air pollution spread by various types of substances. Based on these models, it is possible to create area audits and strategies for the elimination of pollution sources. Thanks to the usage of a multi-socket microprocessor system, the combination of nine different air quality sensors can be installed in a very small device. The possibility of simultaneously measuring several different substances has been achieved at a very low cost for building the sensor unit: 70 EUR. The very small size of this device makes it easy and safe to mount it on a small drone (UAV). Because of this device, many harmful chemical compounds such as ammonia, hexane, benzene, carbon monoxide, and carbon dioxide, as well as flammable substances such as hydrogen and methane, can be detected. Additionally, a very important function is the ability to perform measurements of PM2.5 and PM10 suspended particulates. Thanks to the use of UAV, the measurement is carried out remotely by the operator, which allows us to avoid the direct exposure of humans to harmful factors. A big advantage is the quick measurement of large spaces, at different heights above the ground, in different weather conditions. Because of the three-dimensional positioning from GPS receiver, users can plot points and use colors reflecting a concentration of measured features to better visualize the air pollution. A human-friendly data output can be used to determine the mostly hazardous regions of the sampled area.
ResearchGate has not been able to resolve any references for this publication.