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Citation: Hofman, J.; Peters, J.;
Stroobants, C.; Elst, E.; Baeyens, B.;
Van Laer, J.; Spruyt, M.; Van Essche,
W.; Delbare, E.; Roels, B.; et al. Air
Quality Sensor Networks for
Evidence-Based Policy Making: Best
Practices for Actionable Insights.
Atmosphere 2022,13, 944. https://
doi.org/10.3390/atmos13060944
Academic Editors: Jai Prakash and
Ravi Kant Pathak
Received: 6 May 2022
Accepted: 6 June 2022
Published: 9 June 2022
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atmosphere
Article
Air Quality Sensor Networks for Evidence-Based Policy
Making: Best Practices for Actionable Insights
Jelle Hofman 1, * , Jan Peters 1, Christophe Stroobants 2, Evelyne Elst 2, Bart Baeyens 1, Jo Van Laer 1,
Maarten Spruyt 1, Wim Van Essche 3, Elke Delbare 3, Bart Roels 4, Ann Cochez 5, Evy Gillijns 6
and Martine Van Poppel 1
1Unit Health, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium;
jan.peters@vito.be (J.P.); bart.baeyens@vito.be (B.B.); jo.vanlaer@vito.be (J.V.L.);
maarten.spruyt@vito.be (M.S.); martine.vanpoppel@vito.be (M.V.P.)
2Department Air, Environment and Communication, Flanders Environment Agency (VMM),
Kronenburgstraat 45, 2000 Antwerp, Belgium; c.stroobants@vmm.be (C.S.); e.elst@vmm.be (E.E.)
3Environmental Officer, Municipal Council, 1910 Kampenhout, Belgium;
wim.vanessche@kampenhout.vera.be (W.V.E.); elke.delbare@kampenhout.vera.be (E.D.)
4Sustainability Officer, City Council, 9100 Sint-Niklaas, Belgium; bart.roels@sint-niklaas.be
5Environmental Officer, Municipal Council, 1700 Dilbeek, Belgium; ann.cochez@dilbeek.be
6Environmental Officer, City Council, 9700 Oudenaarde, Belgium; evy.gillijns@oudenaarde.be
*Correspondence: jelle.hofman@vito.be; Tel.: +32-14-33-57-58
Abstract:
(1) Background: This work evaluated the usability of commercial “low-cost” air quality
sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially
available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring
station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address
real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and
at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts?
(3) Results: The considered sensor systems performed well in terms of data capture, correlation and
intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality
levels for indicative measurements as set in the Air Quality Directive (U
exp
< 50% for PM and <25%
for NO
2
). A methodological setup was proposed using local background and source locations,
allowing for quantification of the (3.1) maximum potential impact of local policy interventions and
(3.2) air quality impacts from different traffic interventions with local contribution reductions of up to
89% for NO
2
and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions:
Our results indicate that commercial air quality sensor systems are able to accurately quantify air
quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy
making under the condition of a proper methodological setup (background normalization) and data
quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a
blueprint for air quality sensor networks for replication actions in other cities.
Keywords: urban; air quality; sensors; smart city; calibration; traffic; policy measures
1. Introduction
Despite overall improvements in air quality, air pollution still presents major health
concerns [
1
]. This is evidenced by the recent tightening of the health-based air quality
guidelines set by the World Health Organization (WHO) [
2
]. In the European Union, it is
estimated that 96% of the urban population is exposed to levels of fine particulate matter
above these new WHO guidelines. Especially in urban environments, pollution hotspots
are generated and tend to vary greatly in both time and space, which requires dedicated
policy measures in certain targeted areas. As road traffic represents an important source
of, e.g., NO
x
, PM, UFP and BC [
3
–
15
], many air quality policies (e.g., low emission zones,
Atmosphere 2022,13, 944. https://doi.org/10.3390/atmos13060944 https://www.mdpi.com/journal/atmosphere
Atmosphere 2022,13, 944 2 of 25
speed reductions, school streets) are targeted at reducing local traffic density to improve
the local air quality, safety and overall wellbeing of city dwellers [
16
–
22
]. Quantifying
the resulting air quality impacts from local policy interventions is often hard, as there are
few (or none) regulatory air quality monitoring stations (AQMS) available, and dedicated
monitoring campaigns are difficult and costly to set up using traditional monitoring equip-
ment. Moreover, fixed air quality monitoring stations (AQMS) are mostly not installed at
representative locations to assess the impact of traffic interventions.
Recent advances in sensor and Internet of Things (IoT) technologies have resulted in a
wide range of commercially available “lower-cost” sensor systems that allow for quantifica-
tion of relevant urban pollutants, e.g., particulate matter (PM
x
), nitrogen dioxide (NO
2
) and
ozone (O
3
), at a much higher spatiotemporal resolution [
23
–
25
]. These tools allow regional
or local authorities to set up dedicated monitoring campaigns more easily. However, these
lower-cost sensor technologies typically suffer from a lower accuracy when compared to
the regulatory equivalent or reference methods. Moreover, they can be sensitive towards en-
vironmental conditions and other pollutants and experience sensor drift over time
[25–29]
.
In the literature, a wealth of studies is available on the lab- or field-based evaluation of
sensors [
30
–
41
], co-location or network calibration approaches
[25,26,28,42–49],
personal
exposure [
3
,
50
–
52
] and mapping [
53
–
58
] proof of concept applications. These studies often
conclude that air quality sensors have the potential for policy support, but fall short in
addressing real-life policy concerns or in indicating how such a monitoring campaign could
be set up methodologically or controlled in terms of data quality. This work presents a
collaborative effort of a research institute (VITO), local authorities from three municipalities
and two cities and the regional environmental agency (VMM) to evaluate whether contem-
porary sensor solutions can be used to quantify air quality impacts from real-world traffic
interventions. Air quality impacts from traffic management scenarios have been shown be-
fore, using expensive high-end equipment [
22
,
59
–
61
] or modeling [
16
,
62
,
63
], both of which
require substantial user experience. We wondered to what extent cities or municipalities
can apply more affordable sensor solutions themselves as tools for evidence-based policy
making. In a prior co-creational research trajectory, an inventory of available air quality
sensor systems was created following a literature and market search. Contemporary air
quality concerns and network requirements were identified in a series of workshops and
questionaries with involved municipalities and cities, and finally two pilot sensor networks
were deployed in Kampenhout and Sint-Niklaas. This paper explores the usefulness of air
quality sensors for evidence-based policy making by (1) evaluating sensor and intra-sensor
performance and stability over time and (2) testing two real-life use cases to quantify
the local pollution contribution from road traffic and potential air quality impacts from
different traffic management scenarios.
Within the following paragraphs, the selection of the considered sensor systems is
described in Section 2, followed by the setup of the co-location campaigns to evaluate the
data quality of the sensor systems (Sections 2.1.1 and 2.1.2) and subsequent local calibration
(Section 2.1.3). The aim and setup of the pilot deployments in Kampenhout and Sint-Niklaas
is described in Sections 2.2 and 2.3, respectively. Results of the co-location campaigns
(Section 3.1) and subsequent Kampenhout (Section 3.2) and Sint-Niklaas (Section 3.3) pilots
are provided in Section 3and discussed in Section 4.
2. Materials and Methods
This study considers a field benchmarking study of two commercially available sensor
systems and subsequent application in two pilot deployments. In Kampenhout (BE), the
municipality was interested in the air quality impact from road traffic restrictions (“school
street”) near an elementary school. In Sint-Niklaas (BE), the city wanted to gain insight in
the air pollution contribution from road traffic at the central city square (Grote Markt), as a
baseline assessment before implementing a new traffic management plan. Requirements
of both sensor networks, therefore, focused on traffic-related pollutants (PM, NO
2
) in an
Atmosphere 2022,13, 944 3 of 25
outdoor environment. A set of minimal functional requirements for the sensor networks is
provided in Table 1.
Table 1. Functional requirements of the air quality sensor network.
Pollutants NO2, PM1, PM2.5, PM10
Time period 3 months/pilot
Additional metrics Temperature (◦C), Relative humidity (%)
Number of sensors/locations Minimum 3 (preferably 5)
Housing Outdoor, weather resistant
Power via solar panel
Communication No WiFi available →via SIM/GPRS
Data quality High quality needed: Good comparability
(precision) between sensor boxes
After conducting a literature search and market analysis on commercially available
sensor systems [
64
] and comparing sensor system specifications to the defined functional
requirements (Supplementary Table S1), we decided to proceed with Kunak Air A10 (Kunak
Technologies, Navarra, Spain; ~
€
7000/unit; purchase) and Airly PM + Gas sensor (Airly,
Kraków, Poland; ~
€
300/unit; rental). The Kunak Air A10 holds Alphasense (Essex, UK)
electrochemical sensors for NO
2
, NO and O
3
, holds an OPC-N3 (Alphasense, Essex, UK)
for PM
1
, PM
2.5
and PM
10
, and provides meteorological data on external temperature,
relative humidity and pressure. The Airly PM + Gas sensor holds Alphasense (Essex, UK)
electrochemical sensors for NO
2
and O
3
, holds a PMS5003 (Plantower, Beijing, China) for
PM
1
, PM
2.5
and PM
10
, and provides meteorological data on external temperature and
relative humidity. Further sensor specifications are provided in Supplementary Table S2.
Both sensor systems fulfilled our minimal requirements but were significantly different in
terms of pricing (commonly observed in the air quality sensor market [
64
]) and target users
(more scientific targeted versus community groups). In consultation with the municipalities,
we decided to select one sensor system at each side of the (cost) spectrum to evaluate both
qualitative (user-friendliness, configuration options, transparency, etc.) and quantitative
sensor performance (accuracy and precision).
The study consisted of an initial co-location campaign of 2 weeks, co-locating all
6 sensor systems (3 of each brand/type) at an urban background air quality monitoring
station (AQMS) of the Flemish Environmental Agency. After this co-location, the first pilot
was conducted in Kampenhout (12 February 2021–17 May 2021). This pilot was immediately
followed by a second 2-week co-location campaign (co-location 2) at the same AQMS and
the second pilot study in Sint-Niklaas (10 June 2021–15August 2021). After finishing both
pilot studies, we performed a final 2-week co-location campaign (co-location 3) at the
AQMS (Figure 1). The recurrent co-location campaigns at a regulatory AQMS allowed for
(1) performance (accuracy and precision) evaluation over time, (2) application of linear
correction functions to improve sensor performance before each pilot and (3) evaluation of
sensor drift and sensitivity to meteorological conditions over time.
2.1. Co-Location Campaigns
During each co-location campaign, the 6 sensor units (3 Kunaks, 3 Airlys) were co-
located for two weeks on the roof of a regulatory AQMS in Antwerp, Belgium (51
◦
12
0
34.82
00
N,
4
◦
25
0
54.51
00
E), near the inlets of the reference monitor for NO and NO
2
(42C NO-NO
2
-NO
x
analyzer, Thermo) and equivalent (automatic) monitor for PM (FIDAS 200, Palas). In total,
we considered three co-location campaigns in order to be able to compare the sensor and
intra-sensor performance in different seasons and under varying environmental conditions.
Moreover, potential drift or aging were considered by evaluating the accuracy over the
considered time period (11 months in total).
Atmosphere 2022,13, 944 4 of 25
Figure 1.
Timing of subsequent benchmarking (co-location) and pilot (Kampenhout and Sint-Niklaas)
campaigns during our study. Each co-location campaign evaluated the comparability between
the sensors and equivalent measurements (sensor vs. REF) and intra-sensor comparability (sensor
vs. sensor).
2.1.1. Sensor Performance
We evaluated the comparability between the sensor systems and the reference (NO
x
)
or equivalent (PM) method by calculating the following performance metrics on an hourly
and daily averaging time:
•
Coefficient of determination (R
2
): value between 0 and 1, where 1 is the best possible
value. When the R
2
is equal to 1, the measurements are on a perfect line, and this
means that all variance in the senor data can be explained by the measured refer-
ence concentrations.
•
Pearson correlation (COR): value between
−
1 and 1 that represents the degree of corre-
lation between sensor and reference measurements.
•
Root Mean Squared Error (RMSE): Root of the mean squared error of the measurement
error between sensor and reference data; it is a frequently applied accuracy statistic
and is very dependent on peaks/outliers.
•
Mean Absolute Error (MAE): The mean absolute error between sensor and reference
data; can be interpreted as the mean deviation between sensor and reference.
•
Mean Bias Error (MBE): Mean error between sensor and reference data. This metric
can be both positive and negative and represents respectively the degree of over- or
underestimation of the sensor.
•
Expanded Uncertainty: Measure for the uncertainty (%) around the limit or target value,
as defined by the EU [
65
]. We calculate the non-parametric approach (Uexp), proposed
by the Flanders Environmental Agency, to calculate the uncertainty near 50
µ
g m
−3
for PM
10
, 30
µ
g m
−3
for PM
2.5
and 40
µ
g m
−3
for NO
2
. Although both approaches
aim at quantifying the sensor uncertainty near the limit value, the EU approach used
in the Demonstration of Equivalence (DOE), the relative expanded uncertainty for
the candidate method (Wcm) is derived from the logistic regression between sensor
and reference data (model derivation), while the non-parametric approach (Uexp) is
quantified experimentally (95 percentile MAE of measured concentrations within 10%
range of the regulatory limit/target concentration).
In addition to the calculated performance metrics, we explored the temporal variability
of the sensor and reference data using TimeVariation graphs (openair package in R [
66
,
67
])
and the sensor sensitivity to environmental conditions (temperature, relative humidity and
ozone), as these variables are known to impact the performance of low-cost optical PM and
electrochemical NO
2
sensors [
24
,
25
,
27
,
29
,
32
,
33
,
68
–
70
] and need proper compensation in
order to provide reliable results [25,26,28,42,45,71].
Atmosphere 2022,13, 944 5 of 25
2.1.2. Intra-Sensor Performance
As the comparability between the sensor units is key to compare concentrations from
multiple sensor locations against each other, the precision was evaluated by calculating
the following:
•Min–Max correlation between sensor units of the same brand (Kunak, Airly);
•Min–Max MAE between sensor units of the same brand (Kunak, Airly).
2.1.3. Local Sensor Calibration
Besides evaluating the sensor performance, we explored whether a local re-calibration
increased the sensor and intra-sensor performance. As the sensor systems already included
calibration algorithms, we didn’t consider complex multivariate calibration models (as
applied in Hofman et al. [
25
]), but relied on linear rescaling factors, derived from the slope
of the linear regression function (intercept = 0) between the reference and sensor data, as
shown in Equations (1) and (2):
CSEN SOR_R AW =a∗CREF (1)
CSEN SOR_C AL =CSENSOR_RAW
a(2)
where
CSEN SOR_R AW
and
CREF
represent the measured concentrations of the sensor and
reference equipment (AQMS), respectively, and
a
is the derived slope from the linear
regression function between the raw sensor and reference data (Equation (1)). The calibrated
sensor data (
CSEN SOR_C AL
) can subsequently be calculated by dividing the raw sensor data
by the derived slope (a) from Equation (1) (Equation (2)). Former studies already showed
improved performance after local re-calibration [
45
,
46
,
72
–
74
], as the sensor measurements
are adjusted for local aerosol properties (size distribution, composition and reflectance) and
environmental conditions.
2.2. Kampenhout Pilot
In Kampenhout, three monitoring locations were considered (Figure 2):
•
School: Roadside location in front of the school (50
◦
56
0
43.90
00
N, 4
◦
35
0
11.80
00
E) where
air quality impacts from implemented traffic measures are expected.
•
Environment: Roadside location on adjacent street (50
◦
56
0
41.34
00
N, 4
◦
34
0
51.32
00
E) with
similar traffic as school location and not impacted by implemented traffic measures.
•
Background: Quiet location behind the school (50
◦
56
0
46.16
00
N, 4
◦
35
0
10.46
00
E) where
no road traffic was present and, therefore, regarded as representative for background
pollution and local sources other than road traffic.
At each monitoring location, both a Kunak and Airly unit were installed on 12 February
2021. PM, NO and NO
2
concentrations were monitored for three months, until 17 May
2021. Different traffic scenarios (baseline, knip, schoolstreet) were implemented during
school opening (8 h 10–8 h 40) and closing (15 h 00–15 h 45 and 11 h 00–11 h 45 on
Wednesday) hours:
•Baseline scenario: No traffic restrictions, resulting in unaltered traffic flows.
•
Knip scenario: one-way traffic cut in the street where the school was located (green in
Figure 2), alternately in the western driving direction during school start times and
the eastern direction during school end times.
•
Schoolstreet scenario: 2-way traffic restriction in the street of the school and the perpen-
dicular street (blue in Figure 2).
Atmosphere 2022,13, 944 6 of 25
Figure 2.
Monitoring locations (school, environment (car), background (tree)) of the Kampenhout
pilot, with associated visualizations of the Kunak and Airly deployments.
Each traffic scenario was implemented for about 3 weeks, while the monitoring period
also included holidays (3 weeks school holidays + 1 additional week of school closure due
to the COVID-19 pandemic). The timing of the implemented traffic scenarios is detailed in
Supplementary Table S3. Operational performance of the sensor network (QA/QC) during
the pilot was evaluated by checking the connectivity and potential operational alerts of the
sensors twice a week in the online Kunak and Airly cloud dashboards.
By comparing the measured pollutant concentrations at the school location against
the background location for each of the tested traffic scenarios, we were able to quantify
the resulting air quality impact and identify the most optimal scenario resulting in lowest
pollutant levels at the school entrance.
2.3. Sint-Niklaas Pilot
In Sint-Niklaas, three monitoring locations were considered, with one Kunak and
Airly sensor unit deployed at each location (Figure 3):
•
Background: Quiet location near the main city square (51
◦
10
0
1.27
00
N, 4
◦
8
0
21.70
00
E) in a
car-free street (Paul Snoekstraat), therefore, regarded as representative for background
pollution and local sources other than road traffic. Nearest (quiet) traffic was at 50 m
(Collegestraat) and 40 m (Boonhemstraat).
•
Grote Markt 1: Roadside location at the eastern side (Apostelstraat) of the central city
square (51◦9050.4900 N, 4◦8028.3300 E).
•
Grote Markt 2: Roadside location at the western side (Nieuwstraat) of the central city
square (51◦9050.4100 N, 4◦8022.4600 E).
At each monitoring location, both a Kunak and Airly unit were deployed on 10 June
2021. PM, NO and NO
2
concentrations were monitored for three months, until 15 August
2021. From the resulting difference between the roadside locations at the central city
square (Grote Markt 1 and 2) and the background location, the local traffic-related pollution
contribution was quantified for each pollutant.
Operational performance of the sensor network (QA/QC) during the pilot was eval-
uated by checking the connectivity and potential operational alerts of the sensors twice
a week in the online Kunak and Airly cloud dashboards. By comparing the measured
concentrations at the central city square locations against the background location, we were
able to quantify the road traffic contribution at the central city square and potential air
quality impact from future traffic restrictions.
Atmosphere 2022,13, 944 7 of 25
Figure 3.
Monitoring locations (central city square (car) and background (tree)) of the Sint-Niklaas
pilot, with associated visualizations of the Kunak and Airly deployments.
3. Results
3.1. Co-Location Campaigns
3.1.1. Sensor Performance
Co-location measurements (before rescaling) are visualized in timeseries graphs for
the main traffic-related pollutants NO
2
, NO and PM
1
for each of the Kunak (Figure 4)
and Airly (Supplementary Table S3) sensors. From these time series, it becomes clear that
the sensor measurements follow the reference measurements reasonably well, resulting
in good correlations between sensor and reference data for NO
2
(r = 0.96–0.97) and NO
(r = 0.94–0.99) from Kunak and for PM
1
(r = 0.91–0.94), PM
2.5
(r = 0.89–0.92) and PM
10
(
r = 0.72–0.75
) from Airly (Table 2), with adequate compensation for potential temperature
and relative humidity effects (tested but not shown). Nevertheless, considerable under- or
overestimation can be observed for some pollutants/sensors, resulting in low accuracies,
e.g., for PM1from Kunak (MAE ~9 µg/m3).
An overview of associated data quality metrics (R
2
, COR, RMSE, MAE, MBE, Uexp)
for co-location campaign 1 based on hourly values is presented for NO
2
, NO, PM
1
, PM
2.5
and PM
10
in Table 2. The same statistics were also calculated on daily-aggregated data
and are provided in Supplementary Table S4. Table 2shows data quality metrics
(R
2
, COR, RMSE, MAE, MBE, Uexp) calculated on the raw and locally re-calibrated
(_cal) hourly-aggregated NO
2
, NO, PM
1
, PM
2.5
and PM
10
data for each of the sen-
sor units (
3×Kunak
and
3×Airly
). Local re-calibration is based on slope correction
(
sensor = a ∗REF
;
Equation (1)
), except for the NO
2
of Airly (NO
2
_cal*), where a multilin-
ear model was applied (
sensor = a ∗REF + b ∗Temp + c
). Uexp was only calculated for
pollutants with existing limit values (NO
2
, PM
2.5
and PM
10
) and therefore not for PM
1
and
NO (NA).
As both the Airly and Kunak unit hold the same electrochemical NO
2
sensor (Al-
phasense NO2-B43F), it was quite surprising to see the difference in sensor performance
(similar over the multiple co-location campaigns), probably related to differences in the
applied property algorithms to calibrate the sensors and compensate for environmental
impacts. The better PM performance observed for the Airly units can be explained by
either the difference in PM sensors (PPMS5003 (Plantower, Beijing, China) vs. OPC-N3
(Alphasense, Essex, UK)) or the associated calibration and compensation algorithms.
Atmosphere 2022,13, 944 8 of 25
Figure 4.
Hourly-aggregated time series graphs of NO
2
(upper), NO (middle) and PM
1
(lower) sensor
(Kunak 1–3) and reference measurements from the AQMS (42R801; REF) during the first co-location
campaign (22 January 2021–10 February 2021). Similar graphs for the Airly sensors are provided in
Supplementary Figure S1.
Atmosphere 2022,13, 944 9 of 25
Table 2.
Data quality metrics (R
2
, COR, RMSE, MAE, MBE, Uexp) calculated on the raw and locally
re-calibrated (_cal) hourly-aggregated NO
2
, NO, PM
1
, PM
2.5
and PM
10
data for each of the sensor
units (3
×
Kunak and 3
×
Airly). Local re-calibration is based on slope correction (
sensor = a ∗REF
;
Equation (1)), except for the NO
2
of Airly (NO
2
_cal*) where a multilinear model was applied
(
sensor = a ∗REF + b ∗Temp + c
). Uexp was only calculated for pollutants with existing limit values
(NO2, PM2.5 and PM10) and, therefore, not for PM1and NO (NA).
NO2NO2_cal NO2NO2_cal*
Kunak_1 Kunak_2 Kunak_3 Kunak_1 Kunak_2 Kunak_3 Airly_8463 Airly_12744 Airly_12798 Airly_8463 Airly_12744 Airly_12798
R20.92 0.94 0.95 0.92 0.94 0.95 0.00 0.69 0.02 0.84
COR 0.96 0.97 0.97 0.96 0.97 0.97 NA 0.83 0.14 0.92
RMSE 10.30 7.81 10.26 5.42 4.37 5.89 37.17 123.73 37.16 7.08
MAE 9.04 6.42 9.50 4.01 3.38 4.67 33.44 123.30 33.44 5.29
MBE −9.04 −6.38 −9.50 −0.74 1.04 −1.61 −33.44 123.30 −33.44 0.00
Uexp 42.95 33.00 39.66 26.20 25.11 26.77 110.07 364.54 110.07 36.82
NO NO_cal
Kunak_1 Kunak_2 Kunak_3 Kunak_1 Kunak_2 Kunak_3
R20.98 0.90 0.88 0.98 0.90 0.88
COR 0.99 0.95 0.94 0.99 0.95 0.94
RMSE 3.14 3.13 3.39 1.53 3.15 3.52
MAE 2.21 2.52 2.66 1.20 2.54 2.77
MBE −2.16 0.58 0.64 −0.91 0.88 1.12
Uexp 27.76 15.71 15.71 8.87 12.27 12.27
PM1PM1_cal PM1PM1_cal
Kunak_1 Kunak_2 Kunak_3 Kunak_1 Kunak_2 Kunak_3 Airly_8463 Airly_12744 Airly_12798 Airly_8463 Airly_12744 Airly_12798
R20.73 0.73 0.75 0.73 0.73 0.75 0.85 0.87 0.82 0.85 0.87 0.82
COR 0.86 0.85 0.87 0.86 0.85 0.87 0.92 0.94 0.91 0.92 0.94 0.91
RMSE 10.67 10.83 11.01 4.75 4.87 4.83 4.85 3.14 5.31 3.01 2.75 3.35
MAE 9.05 9.23 9.47 3.59 3.71 3.65 3.91 2.50 3.95 2.33 2.16 2.30
MBE −9.05 −9.23 −9.47 0.24 0.03 −0.17 3.30 1.39 3.35 0.49 0.44 0.40
Uexp NA NA NA NA NA NA NA NA NA NA NA NA
PM2.5 PM2.5_cal PM2.5 PM2.5 _cal
Kunak_1 Kunak_2 Kunak_3 Kunak_1 Kunak_2 Kunak_3 Airly_8463 Airly_12744 Airly_12798 Airly_8463 Airly_12744 Airly_12798
R20.28 0.22 0.25 0.28 0.22 0.25 0.81 0.84 0.79 0.81 0.92 0.79
COR 0.53 0.47 0.50 0.53 0.47 0.50 0.90 0.92 0.89 0.90 0.96 0.89
RMSE 9.17 9.62 9.42 9.94 10.84 10.14 16.37 11.72 16.45 3.69 1.58 3.93
MAE 7.51 8.04 7.79 7.73 8.24 7.91 13.58 9.63 13.45 3.00 1.11 3.10
MBE −5.65 −5.61 −5.84 1.14 1.23 1.06 13.06 8.63 12.92 −0.01 0.43 −0.02
Uexp 68.31 70.53 69.85 91.33 93.94 84.63 97.75 71.08 96.67 18.67 NA 16.54
PM10 PM10_cal PM10 PM10 _cal
Kunak_1 Kunak_2 Kunak_3 Kunak_1 Kunak_2 Kunak_3 Airly_8463 Airly_12744 Airly_12798 Airly_8463 Airly_12744 Airly_12798
R20.28 0.22 0.25 0.15 0.11 0.12 0.55 0.56 0.52 0.55 0.56 0.52
COR 0.53 0.47 0.50 0.38 0.33 0.34 0.74 0.75 0.72 0.74 0.75 0.72
RMSE 9.17 9.62 9.42 13.02 14.11 13.63 20.37 13.71 19.99 8.26 8.94 8.71
MAE 7.51 8.04 7.79 11.47 12.52 12.10 16.28 11.28 15.85 6.83 7.36 7.20
MBE −5.65 −5.61 −5.84 −6.11 −7.10 −6.65 13.55 5.81 12.70 −0.31 −0.62 −0.31
Uexp 68.31 70.53 69.85 39.91 47.14 43.75 47.00 47.00 47.00 24.24 24.24 24.24
3.1.2. Intra-Sensor Performance
From Figure 4, low intra-sensorvariability (precision of the sensor units) can be ob-
served, which is very important for the intended purpose of our pilots, since we want to
compare different sensor units (locations) against each other. Good intra-sensor perfor-
mance was confirmed when calculating the Min–Max correlation and MAE between the
considered sensor units (Table 3), showing that the agreement between the sensor units
is better than the agreement between the sensor and reference measurements. Moreover,
correlations and MAEs were also similar between the three co-location campaigns (
Table 3
),
meaning that intra-sensor performance does not deteriorate over the considered time
period (11 months).
Atmosphere 2022,13, 944 10 of 25
Table 3.
Minimum and Maximum Pearson correlations (COR) and Mean Absolute Errors (MAEs)
between the considered sensor units for NO
2
, NO, PM
1
, PM
2.5
and PM
10
before (RAW) and after
(CAL) local re-calibration.* During the final co-location, only two Airly sensors were available.
Kunak Airly
NO2NO PM1PM2.5 PM10
RAW CAL RAW CAL RAW CAL RAW CAL RAW CAL
CO-LOCATION 1
22 January 2021–10
February 2021
COR MIN 0.978 0.978 0.963 0.963 0.964 0.964 0.986 0.986 0.984 0.984
MAX
0.997 0.997 0.983 0.983 0.984 0.984 0.989 0.989 0.987 0.987
MAE MIN 2.597 2.067 0.795 0.758 1.336 0.850 1.424 0.950 2.029 1.860
MAX
3.631 4.244 2.953 2.491 2.121 2.996 4.445 13.167 7.742
13.327
NO2NO PM1PM2.5 PM10
RAW CAL RAW CAL RAW CAL RAW CAL RAW CAL
CO-LOCATION 2
18 May 2021–8 June 2021
COR MIN 0.986 0.986 0.837 0.837 0.837 0.837 0.921 0.921 0.920 0.920
MAX
0.993 0.993 0.978 0.978 0.915 0.915 0.959 0.959 0.959 0.959
MAE MIN 1.931 2.227 0.741 0.597 0.741 1.479 1.004 1.456 1.387 2.639
MAX
3.542 2.606 2.197 1.823 1.013 4.923 1.364 3.377 2.162 8.207
NO2NO PM1* PM2.5 * PM10 *
RAW CAL RAW CAL RAW CAL RAW CAL RAW CAL
CO-LOCATION 3
21 September 2021–14
November 2021
COR MIN 0.983 0.983 0.982 0.982 0.814 0.814 0.911 0.911 0.926 0.926
MAX
0.994 0.994 0.990 0.990
MAE MIN 4.753 2.365 0.629 0.859 1.452 2.242 1.690 1.924 2.767 3.152
MAX
10.536
4.634 2.244 2.422
3.1.3. Local Re-Calibration
As the linear correlations between the sensor and reference data were generally good
(mostly r > 0.9) and did not exhibit temperature and/or relative humidity effects (not
shown) for NO
2
and NO (Kunak) and PM
1
, PM
2.5
and PM
10
(Airly), and a significant
amount of residual variation can be explained by the slope, slope calibration factors were
implemented as explained in Section 2.1.3 and shown in Figure 5. We derived slope
calibration factors for each pollutant and sensor and calculated the sensor performance
again based on the re-calibrated sensor data (“_cal” in Table 2). This exercise resulted in an
improved accuracy (RMSE, MAE, MBE, Uexp) for all pollutants and sensors and similar
correlations (R
2
and COR), as can be seen from Table 2and Supplementary Figure S2.
While the expanded uncertainty of the original sensor data did not reach the data quality
objectives (DQOs) for indicative measurements (50% for PM, 25% for NO
2
) as defined at
the EU level [
65
], the DQOs were met after slope re-calibration for Airly PM
2.5
and PM
10
and Kunak PM
10
(<50%) and approximated for Kunak NO
2
(~25%), as can be seen from
Table 2.
This re-calibration approach based on slope factors improved the sensor performance
for all pollutants/sensors, except for the Airly NO
2
, where an additional sensitivity towards
atmospheric temperature (
◦
C) was observed (Supplementary Figure S3). By considering a
multilinear re-calibration model compensating for both slope and temperature (covariates),
we were able to explain the residual variability and improve the sensor performance
(Supplementary Figure S3). This was, however, out of scope for this study, as we wanted to
evaluate out-of-the-box performance and added value of easily-derived calibration factors
for the local user communities.
Atmosphere 2022,13, 944 11 of 25
Figure 5.
Effect of the local slope re-calibration on NO
2
(upper), NO (middle) and PM
1
(lower) data
from Kunak 1 during co-location campaign 1. The recalibrated data (right) approximates the 1:1 line
(dashed) much better when compared to the raw sensor data (left). Regression plots for all considered
pollutants of both Konak and Airly are provided in Figure S2.
In conclusion, the considered sensor systems generally performed quite well and
followed the pollutant dynamics of the reference measurements. Nevertheless, a local cali-
bration (rescaling) is advisable to improve the sensor accuracy. While Airly performs best
for PM, Kunak gives the best results for NO
2
and NO. Note that the evaluation is based on
sensor system versions available at the time of the study, and improvements of newer ver-
sions can be expected. After implementing the local re-calibration factors, we reached over-
all good performance for Kunak NO
2
(R
2
= 0.92–0.95,
MAE = 3.38–4.67 µg m−3
) and NO
(
R2= 0.88–0.98
, MAE = 1.2–2.77
µ
g m
−3
) and Airly PM
1
(R
2
= 0.82–0.87,
MAE = 2.16–2.33 µg m−3
)
and PM
2.5
(R
2
= 0.79–0.92, MAE = 1.11–3.1
µ
g m
−3
). Taking into account the observed
data quality and the implemented policy measures (traffic restrictions) in Kampenhout and
Atmosphere 2022,13, 944 12 of 25
Sint-Niklaas, further data analysis of the pilots will focus on the best performing sensor
systems for traffic-related pollutants NO (Kunak), NO2(Kunak) and PM1(Airly).
When comparing the sensor performance (Supplementary Table S6) and derived slope
factors (Supplementary Table S5) between the recurrent co-location campaigns, it becomes
clear that both the raw sensor performance and derived slopes tend to vary throughout the
year. The raw NO
2
sensor performance reduces in the summer period (second co-location),
possibly due to impacts from temperature and ozone, while PM performance decreases
during the winter first and third co-location), probably related to a higher relative humidity.
While the raw PM sensor readings underestimated PM concentrations during the summer
(co-location 2), they overestimated PM concentrations during the winter (co-locations 1 and
3), as can be seen from Figure 6.
Figure 6.
Regression plots of Airly PM
2.5
raw sensor readings against reference data during a winter
(1; left) and summer (2; right) co-location campaign. Associated performance metrics (R
2
, RMSE,
MAE, MBE and Uexp) are provided above the plots.
This seasonal variation of environmental parameters, therefore, tends to impact the
sensor performance. However, after applying the slope re-calibration, similar sensor per-
formances are achieved to the first co-location period (Supplementary Table S6), indicating
that (1) recurrent co-location calibrations can cope with changing environmental impacts
and (2) no significant sensor aging or drift is observed in the considered study period
(11 months).
3.2. Kampenhout Pilot
3.2.1. Descriptive Statistics and Temporal Variability
As described in Section 2.2, PM, NO and NO
2
concentrations were monitored at three
locations (background, school and environment) between 12 February 2021 and 17 May
2021. Slope re-calibration factors from the preceding co-location campaign (co-location 1 in
Supplementary Table S5) were applied to the sensor data, and descriptive statistics of the
exhibited pollutant concentrations and meteorological conditions (temperature, relative
humidity and pressure) are provided in Supplementary Table S7. Data coverage during
the pilot was good (>95%), and meteorological conditions were very similar between
the considered locations. This is not surprising, as all locations are located within a
400 m radius.
Time series graphs in Supplementary Figure S4 show realistic concentration dynam-
ics throughout the monitoring campaign, and diurnal pollutant variability with distinct
morning and evening rush hour peaks were observed for NO
2
, NO and PM
1
(Figure 7).
From the diurnal pattern of the monitoring locations and the implemented traffic scenarios,
it becomes clear that similar pollutant dynamics are observed at all monitoring locations,
with highest concentrations at the environment location (roadside, not impacted), followed
Atmosphere 2022,13, 944 13 of 25
by the school location (roadside, impacted) and the background location (no traffic). During
school start times, concentration differences between the background and school location
are highest for NO (16
µ
g m
−3
), followed by NO
2
(4
µ
g m
−3
) and are only moderate for
PM
1
(<1
µ
g m
−3
). This can be explained by the fact that NO is emitted by traffic and rapidly
oxidizes to NO
2
. NO can, therefore, be regarded as a short-lived pollutant, resulting in
steep concentration gradients near roads (difference in background vs. school/environment
location in Figure 7). The NO
2
concentrations result from primary emissions and converted
NO. NO
2
can be transported over longer distances, and the ratio of NO/NO
2
is the result
of a complex photochemistry, resulting in a less direct relation of NO2to traffic compared
to NO. PM is predominantly influenced by background pollution and can be generated by
a wide range of direct and indirect emission sources.
Figure 7. Cont.
Atmosphere 2022,13, 944 14 of 25
Figure 7.
Average hourly NO
2
(upper), NO (middle) and PM
1
(lower) concentration (
µ
g m
−3
)
variability throughout the day (0–23 h), plotted for each monitoring location (school, environment
and background) and implemented traffic scenario (baseline, knip, schoolstreet and school holi-
days (verlof)).
During school start (8–9 h) and end (15–16 h) times, increased NO and NO
2
pollutant
concentrations at the school location are observed in Figure 7, most clearly during the
baseline (no traffic restriction), to a lesser extent during the knip scenario (one-way traffic
restriction) and to a negligible extent during the schoolstreet scenario and holiday periods.
For PM
1
, it is hard to observe any difference in concentrations between the scenarios based
on the diurnal graphs (Figure 7). Lowest overall NO
2
, NO and PM
1
concentrations are
observed during the school holiday periods. Although these observations already suggest
an air quality impact from the implemented traffic scenarios, actual scenario differences are
calculated in Section 3.2.2.
3.2.2. Traffic Scenario Differences
As the traffic scenarios were only implemented on weekdays (n = 1776), during school
start and end times (n = 161), we extracted this data from the overall hourly-aggregated
dataset. When comparing the observed pollutant variability of these data subsets between
the traffic scenarios (baseline, knip, schoolstreet) and holiday periods at the school location
using boxplots and Wilcoxon Rank significance tests (Supplementary Figure S5), signifi-
cant concentration differences were only observed for NO. However, scenario differences
were mimicked due to the varying background concentration. Higher background con-
centrations were observed for NO
2
(+28%), NO (+44–66%) and PM
1
(+123–185%) during
the implemented traffic scenarios, when compared to the baseline scenario (Supplemen-
tary Figure S6). In order to take into account background concentration variability, we
normalized the school location concentrations using the concentrations observed at the
background location, both absolute (
µ
g m
−3
; Equation (3)) and relative (%; Equation (4)),
for comparison:
Cnorm =Cschool −Cb ackground (3)
Cnorm (%) = Cschool −Cbackgro und
Cback ground
×100 (4)
where
Cnorm
is the normalized concentration (
µ
g m
−3
),
Cschool
is the hourly-averaged
pollutant concentration measured at the school location (
µ
g m
−3
) and
Cback ground
is the
hourly-aggregated concentration simultaneously measured at the background location
(
µ
g m
−3
).
Cnorm
can, therefore, be regarded as the local pollution contribution at the
school location.
Atmosphere 2022,13, 944 15 of 25
Based on the resulting boxplots and descriptive statistics of the normalized pollutant
concentrations (
Cnorm
) in Figure 8, we can state that the average local NO
2
contribution
(during the scenarios; 8–9 h and 12–13 h/15–16 h) decreases from 4.33
µ
g m
−3
(baseline) to
2.9
µ
g m
−3
(
−
33%) during the knip scenario (one-way traffic restriction) and to ~0.5
µ
g m
−3
(
−
89%) for the schoolstreet scenario (two-way traffic restriction). For NO, the air quality
impact is even more significant, from 16.83
µ
g m
−3
(baseline) to 10.91
µ
g m
−3
(
−
35%) for
the knip scenario and 6.72
µ
g m
−3
(
−
60%) for the schoolstreet scenario. Finally, for PM
1
,
we see no effects on the scenarios, with a negligible local PM
1
contribution at the school
location (0.06–0.31
µ
g m
−3
). The observed concentration differences between the traffic
scenarios exceed the intra-sensor uncertainties quantified in the co-location campaigns
(Table 3), and can therefore be explained by the implemented traffic scenarios.
Figure 8.
Boxplots with associated Wilcoxon Rank scores (*: p< 0.05, **: p< 0.01, ****: p< 0.001) for
the local contribution of NO
2
(upper), NO (middle) and PM
1
(lower) exhibited at the school location
during school opening/closing hours (n = 161). Associated descriptive statistics (n, mean, standard
deviation (sd), median and interquartile range (IQR)) are provided next to each plot.
Atmosphere 2022,13, 944 16 of 25
3.3. Sint-Niklaas Pilot
As described in Section 2.3, PM, NO and NO
2
concentrations were monitored at three
locations (background, Grote Markt 1 and 2) between 10 June 2021 and 15 August 2021.
Slope re-calibration factors derived from the preceding co-location campaign (co-location 2
in Supplementary Table S5) are applied to the sensor data. The resulting average pollutant
concentrations for NO
2
, NO, PM
1
, PM
2.5
and PM
10
are provided in Table 4. Data coverage
during the pilot was again very good (>95%), as can be seen from the time series graphs in
Supplementary Figure S7.
Table 4.
Average NO
2
, NO, PM
1
, PM
2.5
and PM
10
concentrations exhibited during the pilot campaign
in Sint-Niklaas.
Background Grote Markt 1 Grote Markt 2
µg m−3µg m−3µg m−3
NO212.8 16.4 19.0
NO 2.2 7.6 10.2
PM115.2 16.9 15.2
PM2.5 17.8 19.1 17.8
PM10 31.7 34.2 31.7
The average NO and NO
2
concentrations are clearly higher at the central city square
locations (Grote Markt 1 and 2), when compared to the background location (up to 6
µ
g/m
3
higher for NO
2
(+50%) and up to 8
µ
g/m
3
higher for NO (+360%)). For PM
1
, a modest
concentration increase of 2.5
µ
g m
−3
(+8%) compared to the background location is only
observed at one of the Grote Markt locations (Grote Markt 1). The location differences for
NO and NO
2
are larger than the observed uncertainty between the sensors (Table 3) and
should, therefore, be explained by effective location differences. Considering traffic as an
important source of NO and NO
2
(confirmed in the Kampenhout pilot) and the car-free
background location, our results indicate that increased traffic emissions at the Grote Markt
locations give rise to elevated atmospheric NO and NO2concentrations.
When evaluating the hourly pollutant variability at the central square (Figure 9), higher
morning and evening rush hour peaks are observed for NO
2
. From midday onwards, NO
2
reacts with O
2
(+UV and heat), forming NO and O
3
and decreasing NO
2
concentrations.
This photochemical balance is inversed in the late evening, when an excess of NO from the
evening rush hour peak (in absence of UV from solar radiation) reacts with O
3
, forming
NO
2
again. From 23 h onwards, NO
2
concentrations are reduced due to the absence of direct
emissions. As direct NO emissions from traffic are very short-lived (rapid oxidation towards
NO
2
) and NO is only formed under sunny conditions, nighttime NO concentrations are
very low. The diurnal pattern of NO
2
and NO is, therefore, impacted by both direct
emissions and photochemical reactions between NOx and ozone. Hourly PM variability at
the central square is very low, as can be seen from Figure 9. Hourly-averaged NO
2
and NO
concentrations at each monitoring location are provided in Supplementary Figure S8 and
show the most distinct location differences for NO, followed by NO2.
To focus on the actual traffic contributions at the central square (Grote Markt), we
calculated normalized pollutant contributions during the morning rush hour (6–9 h),
following Equation (3). Local pollutant contributions (difference between background and
central square locations) during the morning rush hour amounted to ~8.4
µ
g m
−3
for NO
2
(+30%), ~11.7
µ
g m
−3
(+180%) for NO and 1–2
µ
g m
−3
(+5%) for PM
1
. This effect was
pronounced for NO
2
(+43%) and NO (+216%) when only considering working days and
dropped to ~0% during the weekends, which confirms our traffic hypothesis. Observed
air quality impacts again exceeded between-sensor uncertainties for NO and NO
2
, which
confirms that observed air quality impacts are due to location differences.
Atmosphere 2022,13, 944 17 of 25
Figure 9.
Hourly NO
2
(Kunak; upper), NO (Kunak; middle) and PM
1
(Airly; lower) variability as
observed at the Grote Markt 1 location.
4. Discussion
4.1. Data Quality
The considered sensor systems generally performed quite well in terms of data
capture (>95%), correlation and intra-sensor uncertainty. However, good out-of-the-
box accuracy was not guaranteed, and a local calibration (rescaling) seems advisable
to improve the sensor accuracy to supplementary levels (<50% for PM and <25% for
NO
2
). Performance was related to both hardware setup (applied sensors) and applied
compensation and calibration property algorithms, with Airly performing best for PM
and Kunak giving the best results for NO
2
and NO. After implementing the local re-
calibration factors, we reached overall good performance for Kunak NO
2
(R
2
= 0.92–0.95,
MAE = 3.38–4.67 µg m−3
) and NO (R
2
= 0.88–0.98,
MAE = 1.2–2.77 µg m−3
) and Airly PM
1
(R
2
= 0.82–0.87,
MAE = 2.16–2.33 µg m−3
) and PM
2.5
(R
2
= 0.79–0.92,
MAE = 1.11–3.1 µg m−3
).
While the expanded uncertainty of the original sensor data did not reach the data quality
objectives (DQOs) for supplementary measurements (50% for PM, 25% for NO
2
), as defined
at the EU level [
65
], the DQOs were met after slope re-calibration for Airly PM
2.5
and
PM
10
and Kunak PM
10
(<50%) and were approximated for Kunak NO
2
(~25%). High
intra-sensor precision was observed for both Kunak and Airly. This is important, as many
sensor use cases (like the Kampenhout and Sint-Niklaas pilots) rely on comparisons of
multiple monitoring locations (between sensor units).
Atmosphere 2022,13, 944 18 of 25
Although the sensor price range was not necessarily indicative of the data quality
(Airly PM performed better than Kunak PM), the more costly Kunak sensors offered much
more flexibility, local calibration and configuration options, more data monitoring tools
with dedicated warnings (e.g., flow PM sensor), extended analytics based on the statistical
openair package [
67
] and availability of governmental air quality monitoring stations in
their cloud dashboard. In contrast, the Airly sensors and interface are more basic, but the
price of these sensors is ~10 times lower.
Field co-location campaigns seem vital to (1) evaluate and improve the performance
of contemporary sensor systems against actual reference equipment and (2) quantify intra-
sensor uncertainty for proper interpretation of the sensor data during their implementation
(locations differences > instrument noise). In addition, we noticed that environmental
impacts changed throughout the year, resulting in varying re-calibration factors for the three
co-location campaigns. Ideal calibration should, therefore, be repeated, e.g., by conducting
recurrent co-locations (e.g., every 3 months, at least at locations with large seasonality) [
26
],
as applied in our study. Other (re-)calibration approaches that are proposed in the literature
include (1) continuous scaling of co-location-derived calibration factors to a deployed
sensor network [
44
,
74
,
75
], (2) distant calibration of sensors based on the existing reference
network [25,54,76] or (3) a combination of field and mobile calibration approaches [42].
4.2. Pilots
When considering policy measures to improve local air quality (e.g., via traffic mea-
sures), it is important to think about the general build-up of air pollution in cities. Urban air
pollution consists of background contributions from cross-boundary, regional and urban
sources and local contributions from nearby sources, i.e., traffic (Figure 10). The maximum
achievable air quality improvement from local measures is therefore the local contribution,
which, at the roadside, showed to be the highest for NO, followed by NO
2
and PM. The
use of background monitoring locations proved to be very valuable, as we experienced
background concentration fluctuations mimicking the impacts from the implemented traf-
fic measures in one of the pilots (Kampenhout). By considering both a local source and
background monitoring location simultaneously, measured concentrations at the source
location can be normalized continuously for fluctuating background concentrations. This
allows for quantification of (1) the local source contribution (= maximum potential air
quality impact of local interventions), and (2) resulting air quality impacts from short-lived
traffic interventions (e.g., one-way cut and school street).
Figure 10.
Urban air pollution buildup with contributions from background and local sources for
NO2(blue), NO (orange) and PM1(green).
Although the sensor locations were fairly close to each other (<400 m), quantifiable
location differences were observed in both pilots, exceeding the intra-sensor uncertainty
Atmosphere 2022,13, 944 19 of 25
quantified in the co-location campaigns. We can therefore state that the considered sensor
systems were able to detect air quality impacts from local traffic in front of a school in
Kampenhout and at a central city square in Sint-Niklaas. The local traffic-related pollution
contribution (potentially impacted by local traffic measures) in Kampenhout was the highest
for NO (90%), followed by NO
2
(24%), and was only negligible for PM
1
(~7%) (Figure 11).
In Sint-Niklaas, similar findings were observed with the local rush hour contributions
of NO
2
and NO (Supplementary Figure S9). This is important information, as it shows
the maximal achievable air quality impacts from local policy measures. In Kampenhout,
implemented traffic measures (knip, school street) resulted in a reduction of this local
pollution contribution of up to 89% for NO
2
and 60% for NO (Figure 11). Both pilots thus
support the use of local traffic measures to improve local air quality in cities.
Figure 11.
(
Left
panel): local and background NO
2
, NO and PM
1
contribution during baseline (left),
knip (middle) and school street (right) scenario. (
Right
panel): Absolute and relative (%) impacts
from knip and school street scenarios on local NO2, NO and PM1contributions.
As we only considered a limited monitoring period in our pilots, 3 months (~3 weeks/traffic
scenario) in Kampenhout and 2 months in Sint-Niklaas, and seasonal dynamics in air quality
and modal split likely influenced the observed traffic scenario impacts, generalization of
the observed results is difficult.
Nevertheless, our results seem to confirm the few existing studies reporting air quality
impacts from traffic management scenarios near school environments and urban environ-
ments in general. A recent intervention study on the impact from traffic restrictions (school
streets) at three schools (high-end instruments) in Belgium reported decreased morning
rush hour peaks for NO
2
, BC and UFP by comparing concentrations during the traffic
intervention (school street) with concentrations from the hour before the intervention [
61
].
Results showed that the implemented traffic restrictions resulted in an average (morn-
ing drop-off) concentration decrease for NOx (from +32 to +77% (pre-) to
−
23% to +22%
(post-intervention)), BC (from +18 to +58% (pre-) to
−
27% to +38% (post-intervention))
and UFP (from +15 to +19% (pre-) to -9% to +8% (post-intervention)) [
61
]. Another study
with 30 AQMesh sensors at 16 schools in London (UK), comparing schools with traffic
restrictions (school street) against schools without traffic restrictions [
77
], reported average
reductions in nitric oxide (NO) concentrations of up to 8
µ
g/m
3
(34%) during the morning
intervention period, which equates to a reduction in daily average (school day) concen-
tration of approximately 5%. The resultant reduction in nitrogen dioxide (NO
2
) during
the school drop-off period was estimated as being up to 6
µ
g/m
3
(23%). The morning
intervention alone was thus expected to reduce daily average NO
2
by up to 0.4
µ
g/m
3
, or
2%. Moreover, 81% of parents and carers supported the measures at their children’s school,
and 18% of parents reported driving to school less as a result of school streets [
77
]. In terms
of the local pollution contribution from road traffic, a recent study with mobile sensors on
Atmosphere 2022,13, 944 20 of 25
buses in Hong Kong, determining local and background contributions for NO, NO
2
, CO
and PM
2.5
, showed that NO and NO
2
are locally dominated air pollutants, constituting
72%–84% and 58%–71%, respectively, whereas PM
2.5
and CO largely arise from background
sources, which contribute 55%–65% and 73%–79%, respectively [
78
]. Another recent study
showed the distinct traffic-related impacts from the COVID-19 lockdown in the United
Kingdom on resulting NO
2
and NO concentrations, with observed concentration reduc-
tions of 32 to 50% at urban traffic stations and 26 to 46% at urban background stations,
while no concentration reductions (or even increases) were observed for O3and PM [59].
Although these studies are all different in terms of implemented traffic interventions
(traffic restriction, cut, lockdown, etc.), applied instrumentation (sensors vs. monitors, fixed
vs. mobile deployments) and data analysis (normalized based on previous hour [
61
], nor-
malized based on simultaneous background concentration (our study), normalized based
on locations without traffic interventions [
77
]) and consider different time periods and/or
pollutants, there seems to be common evidence that road traffic results in local pollution
contributions (NO>NO
2
>PM), and local air quality improvements can be expected from
when implementing traffic interventions.
4.3. Blueprint for Urban Air Quality Sensor Networks
A total of 79 Belgian municipalities and cities and the Flanders Environmental Agency
(VMM) contributed in the research trajectory towards the usability of commercial air quality
sensor systems for evidence-based policy making. This trajectory included a literature
search and market analysis [
64
], workshops and survey on the needs and requirements,
problem statement, functional properties, and visualization and communication aspects
of air quality sensors. After a preparatory phase, the above pilot sensor networks were
deployed to address the real-world policy concerns of local authorities. Based on the
feedback from the involved cities, sensor data were deemed useful as an evidence base for
planned policy measures and were communicated to citizens. The involved municipalities
and cities indicated that organizing air quality sensor networks at the local municipal level
is still challenging, with sensor selection, data quality evaluation and data analysis as the
most challenging processes, requiring support from external experts or agencies.
To this end, we developed a hands-on tool (blueprint) for local governments aiming to
roll out air quality sensor networks in their municipality or city. The blueprint includes
scoping advice and practical tools (e.g., sensor selection tool) with regard to the entire
design process, including defining a research question, experimental setup, roll out, data
analysis and communication (Figure 12). This blueprint is available online [79].
Figure 12.
Blueprint for a municipal air quality sensor network. Scoping (orange) and best practices
(green) based on real-world experiences are given for each step in the design process.
Atmosphere 2022,13, 944 21 of 25
5. Conclusions
This work describes a collaborative effort of a research institute (VITO), local author-
ities and the regional environmental agency (VMM) in substantiating local air quality
policies with actionable sensor data from commercially available air quality sensor systems
(Airly, Kunak). The aim was to evaluate the usability of commercial “low-cost” air quality
sensor systems to substantiate evidence-based policy making by quantifying the local
pollution contribution from road traffic at a central city square and potential impacts from
different traffic measures in a school environment.
Both pilots demonstrate that contemporary air quality sensors are able to accurately
quantify air quality impacts from (even short-lived) local traffic measures and contribute
to evidence-based policy making, under the condition of a proper methodological setup
(background normalization) and data quality (recurrent calibration) procedure. When
considering commercially available sensor solutions, co-location campaigns remain vital
to benchmark the data quality of the sensors, evaluate the validity of observed effects
(sensor precision) and improve sensor performance through calibration. Including a
local background location in the sensor network has proven methodologically valuable to
normalize for background pollutant dynamics and to quantify local pollutant contributions
(i.e., potential impact of local policy measures). Based on the feedback from the involved
cities, sensor data were deemed useful and can empower local authorities to implement
new air quality management plans. To this end, a blueprint was developed to help local
authorities in setting up local air quality senor networks.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/atmos13060944/s1. Table S1. Comparison of defined functional
requirements (Table 1) against specifications from commercially available sensor systems. Table S2.
Sensor specifications of the considered Kunak Air A10 and Airly PM+Gas sensors, derived from
AQMD AQ-SPEC (http://www.aqmd.gov/aq-spec/home); Table S3. Timing of the implemented
traffic scenarios (baseline, knip and schoolstreet) and holiday periods during the pilot study in
Kampenhout; Figure S1. Hourly-aggregated time series graphs of NO2 (upper) and PM1 (lower)
from the different Airly units (8463, 12,744 and 12,798) and refer-ence measurements from the AQMS
(42R801; REF) during the first co-location campaign (22 January 2021–10 February 2021). The Airly
sensors do not contain a NO sensor; Table S4. Data quality metrics (R
2
, COR, RMSE, MAE, MBE,
Uexp) calculated on the raw and locally re-calibrated (_cal) 24h-aggregated NO2, NO, PM1, PM2.5
and PM10 data for each of the sensor units (3
×
Kunak and 3
×
Airly). Local re-calibration is based
on slope correction (sensor = a
∗
REF; Equation (1)), except for the NO2 of Airly (NO2_cal*) where a
multilinear model was applied (sen-sor = a
∗
REF + b
∗
Temp + c); Figure S2. Regression plots with
associated performance metrics (R
2
, RMSE, MAE, MBE and Uexp) after slope re-calibration for
NO2, NO, PM1, PM2.5 and PM10 from both the Kunak 1 and Airly 9463 sensor unit; Figure S3.
Upper graph: Sensitivity of the residual hourly NO2 variation (sensor-REF) towards temperature
(
◦
C), realtive humidity (%) and ozone (O3; derived from AQMS R801) with associated scatterplots,
historgrams and Pearson correlation tests. Lower graphs: Regression plots between raw (left), slope
re-calibrated (middle) and multilinear re-calibrated (right) sensor and reference NO2 data, with
associated performance metrics (R
2
, RMSE, MAE, MBE, Uexp); Table S5. Derived unit-specific slope
calibration factors for every pollutant (NO2, NO, PM1, PM2.5 and PM10) of each co-location (1–3)
campaigns. Slope factors of co-location campaign 1 are used in the Kampenhout pilot, while slope
factors derived from co-location 2 are applied in the Sint-Niklaas pilot; Table S6. Raw and slope
re-calibrated (CAL) sensor performance (R
2
, MAE, MBE, Uexp) calculated for NO2, NO and PM1, for
every sensor unit (Kunak 1–3 and Airly 1–3) in every co-location campaign; Table S7. Descriptive
statistics (Min, 25%, Median, Mean, 75%, Max, NA’s and Data capture) of meteorological and pollutant
data collected at the different monitoring locations (School, Environment and Background) during
the Kampenhout pilot; Figure S4. Time series graphs of NO2 (Kunak; upper), NO (Kunak; middle)
and PM1 (Airly; lower) concentrations during the Kampenhout pilot, exhibited at the background
(achtergrond), school and environment (omgeving) location; Figure S5. Boxplots with associated
Wilcoxon Rank scores for the NO2 (upper), NO (middle) and PM1 (lower) concentration differences,
observed at the school location, during the implemented traffic scenarios (and holidays) for both
weekdays (n = 1776; left) and school open-ing/closing hours (n = 161; right). Associated descriptive
Atmosphere 2022,13, 944 22 of 25
statistics (n, mean, standard deviation (sd), median and interquartile range (IQR)) are provided
below each plot; Figure S6. Boxplots with associated Wilcoxon Rank scores for the NO2 (upper), NO
(middle) and PM1 (lower) concentration differences, observed at the background location, during
the implemented traffic scenarios (and holidays) for both weekdays (n = 1776; left) and school
opening/closing hours (n = 161; right). Associated descriptive statistics (n, mean, standard devia-tion
(sd), median and interquartile range (IQR)) are provided below each plot; Figure S7. Time series
graphs of the collected NO2 (Kunak; upper), NO (Kunak; mid-dle) and PM1 (Airly; lower) sensor data
at the background (Stadsschouwburg) and Grote Markt (Apostelstraat and Nieuwstraat) locations
in Sint-Niklaas; Figure S8. Hourly-averaged concentrations (
µ
g m
−
3) for NO2 (left) and NO (right)
from the background (green), Grote Markt 1 (red) and Grote Markt 2 (blue) location; Figure S9.
Mean pollutant concentrations (
µ
g m
−
3) for NO2, NO and PM1 at the background and Grote Markt
locations (upper panel) and local NO2 and NO contribution at the Grote Markt during morning rush
hour (6–9 h) on working days (left) and Sundays (right). For PM1, no notable local contributions
(exceeding the background concentration) were observed.
Author Contributions:
Conceptualization, J.H., J.P. and M.V.P.; methodology, J.H., J.P., B.B., J.V.L.,
M.S., M.V.P., W.V.E., E.D., B.R., A.C. and E.G.; formal analysis, J.H., J.P. and M.V.P.; investigation, J.H.,
J.P., B.B., J.V.L., M.S. and M.V.P.; data curation, J.H. and J.P.; writing—original draft preparation, J.H.;
writing—review and editing, J.H., J.P., M.V.P., C.S. and E.E; visualization, J.H. and J.P.; supervision,
M.V.P.; funding acquisition, J.P., M.V.P., W.V.E., C.S. and E.E. All authors have read and agreed to the
published version of the manuscript.
Funding:
This research was funded by the Flanders Innovation and Entrepreneurship City of Things
program (COT.2018.006).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
Data retrieved during this study can be obtained by contacting the
corresponding author.
Acknowledgments:
In this section, you can acknowledge any support given which is not covered by
the author contribution or funding sections. This may include administrative and technical support,
or donations in kind (e.g., materials used for experiments).
Conflicts of Interest: The authors declare no conflict of interest.
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