The environments in which we live, work, and play are subject to
enormous variability in air pollutant concentrations. To adequately
characterize air quality (AQ), measurements must be fast (real time), scalable,
and reliable (with known accuracy, precision, and stability over time).
Lower-cost air-quality-sensor technologies offer new opportunities for
fast and distributed measurements, but a persistent characterization gap
remains when it comes to evaluating sensor performance under realistic
environmental sampling conditions. This limits our ability to inform the
public about pollution sources and inspire policy makers to address
environmental justice issues related to air quality. In this paper, initial
results obtained with a recently developed lower-cost air-quality-sensor
system are reported. In this project, data were acquired with the ARISense
integrated sensor package over a 4.5-month time interval during which the
sensor system was co-located with a state-operated (Massachusetts, USA) air
quality monitoring station equipped with reference instrumentation measuring
the same pollutant species. This paper focuses on validating electrochemical
(EC) sensor measurements of CO, NO, NO
Protecting populations from exposure to poor air quality (AQ) is one of the greatest public health challenges affecting all nations on earth (WHO, 2014). For the past half century, developed countries have made an effort to measure concentrations of major pollutants known to degrade health or damage plants and physical structures. Generally, the focus has been on the most populated areas, with an intent to assess daily, monthly, or annual concentrations on a regional basis. While greater spatial and temporal resolution has been desired, the costs of purchasing and operating instruments sufficiently robust, accurate, and free of interferences to generate reliable data has been prohibitive – an instrument to assess a single pollutant at ambient levels can cost many tens to hundreds of thousands of US dollars.
In this situation it is therefore easy to understand the motivation to develop inexpensive, rapid-response air quality monitoring devices that can be deployed in large numbers around point sources or throughout specific neighborhoods to create the desired high spatial and temporal resolution AQ data grid (Snyder et al., 2013; Kumar et al., 2015; McKercher et al., 2017). Indeed, within the past decade, many researchers, entrepreneurs, and manufacturers have pursued the development, deployment, and evaluation of lower-cost devices that measure air pollution (Mead et al., 2013; Williams et al., 2014b; Masson et al., 2015; Jiao et al., 2016; Lewis et al., 2016; Castell et al., 2017; Hagan et al., 2017; Mueller et al., 2017; Zimmerman et al., 2017).
While electrochemical (EC) sensors have formed the basis for workplace and
hazardous leak detection applications for many decades (Stetter and
Li, 2008), their transition from workplace to ambient air is accompanied by
much lower target concentration ranges over which the sensors must
accurately measure the analyte species of interest (Borrego et al.,
2016). Coincident with the need to resolve much lower concentrations is the
need to fully understand and model the influence of non-analyte
interferences resulting from changing temperature (
In order to calibrate and characterize interferences, laboratory and field-based co-location experiments must be carried out spanning the full range of pollutant concentrations and ambient sampling conditions that may be encountered in an actual stand-alone deployment. Deploying lower-cost AQ-sensor systems in the absence of such calibration significantly undermines the credibility of the data. Indeed, reports have appeared recently raising concerns about the reliability of data produced from inexpensive monitoring devices containing EC sensors (Lewis and Edwards, 2016).
This paper describes results obtained from a newly developed, integrated
lower-cost EC-sensor system, ARISense, which has been developed at Aerodyne
Research, Inc., for simultaneous, real-time measurement of a wide range of
ambient-level atmospheric pollutants and accompanying meteorological
metrics. Air quality monitoring systems can be roughly divided into three
cost tiers: (1) high-cost and high-accuracy systems costing
Mechanical drawings (wires excluded) showing the main
components of the ARISense system. Each system includes an anemometer (A) mounted
to the back bracket of the NEMA enclosure providing a description of
the wind fields in the immediate proximity to the gas and particle sampling
inlets of the system. Mounting brackets for wall or pole-mount
configurations attach at position (B). Expanded view of the internal
components reveals the optical particle counter (C), gas sampling manifold (D) with
embedded electrochemical and NDIR and RH/
The ARISense system used in the present study (version 1.0) measures ambient
levels of five gaseous pollutants (CO, NO, NO
ARISense v1.0 contained the following EC sensors (purchased from Alphasense
Ltd., UK): carbon monoxide (CO-B4), nitric oxide (NO-B4), nitrogen dioxide
(NO2-B43F), and total oxidants (Ox-B421). (More recent versions of ARISense
have been upgraded to model Ox-B431.) The integrated system also includes a
nondispersive infrared (NDIR) carbon dioxide (CO
ARISense electronics were designed to integrate all sensor measurements into
a unified data acquisition framework and provide user access and control over
the system' s configuration and operation. EC-sensor signals were collected
and processed by custom-built electronics designed to minimize noise and
amplify raw signals (i.e., potentiostat circuitry). Connectivity for v1.0
systems was enabled via hard-line CAT-5 ethernet connections (Lantronix
XPort Pro). Data were saved at user-defined sampling intervals (5–60 s) onto a
local USB drive and (if internet-connected) to the ARISense database
(
The ARISense system has two sampling inlets, one for measuring gas-phase
pollutants and the other dedicated to the measurement of particulate matter.
In both cases, the air flow is driven by small DC-powered fans embedded at
the downstream end of the sample flow path, minimizing the loss of sticky or
reactive gas molecules (NO
The particle inlet is on the bottom face of the NEMA enclosure (Fig. 1,
component C). Given the body of evidence implicating PM
This paper presents results for the four electrochemical sensors in a single ARISense system. Note that nominally identical electrochemical sensors can have widely different sensitivities and exhibit variable environmental interference effects. As a result, the specific calibration models described in this paper cannot be broadly applied to all ARISense systems. Until the reproducibility of electrochemical sensor manufacturing improves, system-specific HDMR models will need to be developed for each individual ARISense system to maintain robust sensor quantification metrics.
Map showing the locations of the two ARISense systems (yellow
circles) and the four Boston metropolitan area DEP monitoring stations (red
circles). The two ARISense systems were co-located with reference stations at
the Harrison Avenue site (in Dudley Square, Roxbury) and Von Hillern Street
site (
Two ARISense systems (indicated with yellow circles in Fig. 2) were deployed south of Boston, MA, from July to November 2016. This initial deployment of the ARISense systems was in conjunction with an existing four-node network (the Dorchester Air Quality Sensor System, DAQSS, project) established in January of 2016. The DAQSS node locations are indicated with green markers on the map. The neighborhoods of Roxbury and Dorchester are among Boston's largest and most economically diverse, including low-income residential areas interspersed with light and heavy industry, as well as the Interstate 93 (I-93) corridor which runs along the eastern edge of Dorchester. Given their location and activities therein, Dorchester and Roxbury experience a high frequency of automobile, commercial truck, and heavy duty diesel traffic, much of which is constrained to stop-and-go driving patterns on congested, narrow streets, in close proximity to housing and pedestrians. The original DAQSS deployment and initial ARISense proof-of-concept efforts were motivated by the need to assess the viability of lower-cost AQ-sensor systems in communities suffering from environmental health knowledge gaps, such as the unexplained doubling of the adult asthma rate in north Dorchester between 2001 and 2010 (Backus et al., 2012).
In order to validate our measurements, each ARISense system was co-located
with a Massachusetts Department of Environmental Protection (MassDEP) air
quality monitoring station (indicated with red circles on the map) for the
duration of the present study. This paper presents ARISense and MassDEP
reference data for the Roxbury site (left-hand yellow circle in Fig. 2)
located adjacent to Harrison Avenue in Dudley Square (latitude
The MassDEP Roxbury air monitoring site (ID: 25-025-0042), established in
December 1998, hosts continuous and semi-continuous gas and particle
measurements. The reference measurements used in this study include ozone
(O
Calibration is a critical issue for trusting the output of EC sensors.
Recent papers (Lewis et al., 2016; Castell et al., 2017) have highlighted
that the lack of rigorous calibration protocols for lower-cost AQ-sensors
results in significant potential error when the sensor system is deployed in
ambient conditions. For example, Mead et al. (2013) modeled the
temperature-dependent baseline drift of an Alphasense NO sensor using an exponential
curve fit through 24 h of ambient data. Their analysis revealed that
temperature-derived baseline drift could exceed a
Alphasense provides some guidance to customers regarding calibration and
temperature compensation of electrochemical sensor response (Alphasense
Application Note no. AAN 803-03, December 2014). This document highlights
the utility of including a fourth electrode in their B4-series
electrochemical sensors such as were used in this study. The purpose of this
fourth electrode (called the auxiliary electrode, AUX) is to provide a
real-time correction for environmentally derived interferences at the
working electrode. The AUX electrode is comprised of an identical
catalyst to that of the WE and is designed to mimic the WE's response to
environmental changes such as temperature, pressure, and humidity. Since the
AUX electrode is fully submerged in the electrolyte and directly below the
WE, the AUX signal is theoretically “blind” to the target analyte gas
species which readily oxidize or reduce at the WE surface (which is exposed
to the air on one side and the electrolyte layer on the other). In an ideal
world, a simple subtraction of the current generated at the AUX electrode
from the current generated by the WE would provide a signal that is linearly
proportional to the target analyte over the full concentration range of
interest. Unfortunately, we have found that in practice the AUX electrodes
in most sensors are not able to track the changes in the corresponding WE
over the nominal operational temperatures of the system. Specifically, at
sensor temperatures > 25
In practice, we have found that the manufacturer's recommended WE and AUX electrode corrections do not lead to pollutant concentration values of acceptable accuracy for ambient air analysis. In addition, the EC-sensor response is impacted by other environmental conditions besides temperature, such as relative humidity and the concentrations of other gas-phase species. At the low concentrations present in the atmosphere (10–1000 s ppb), characterizing the full (multidimensional) interference response is critical to achieving reliable measurements. In this work we demonstrate the use of a multidimensional mathematical modeling approach (HDMR) that has the ability to adequately identify and quantify the complex EC-sensor response to multiple environmental variables and interfering gas species simultaneously.
The ARISense system uses high-dimensional model representation techniques to convert the raw sensor outputs into units of concentration (ppb). HDMR is a numerical method consisting of a general set of quantitative model assessments and analyses for capturing input–output system behavior without reliance on a physics-based model or the sensor manufacturer's empirical correction procedure. When applied to a set of experimental data (with sufficient variability), it can produce a mathematical model relating user-defined input variables to output variables of interest; the resulting model can capture the interdependencies of the variables and provide a mathematical description of the system that is otherwise difficult or impossible to describe with a physics-based model. The HDMR model can be used to identify and quantify which variables and variable interactions have the most impact on data reduction, relative to an identified output (i.e., reference concentration). In collaboration with the research group of Herschel Rabitz of Princeton University, Aerodyne has implemented HDMR methods in a software tool called ExploreHD, providing graphical and command line user interfaces to HDMR algorithms.
The details of the HDMR algorithms used here are discussed in detail
elsewhere (Li and Rabitz, 2010; Sipilä et al., 2010; Li and Rabitz,
2012; Li et al., 2012). One of the key underlying tenets of the HDMR
framework is that many input–output relationships for complex physical
systems can be captured adequately by low-order combinations of input
variables, even in systems with high dimensionality in input variables. Each
component function provides an additive contribution to the overall model
prediction. The modeling process involves three steps. In the first step,
the user specifies a maximum variable interaction order (for example, a
second-order HDMR model would allow component functions involving
combinations of two input variables), and the HDMR algorithm considers
orthogonal component functions (in this case, cubic polynomials) involving
all possible variable combinations up to the maximum specified order. In the
second step, a statistical analysis (using
In the current study, the HDMR approach uses the raw EC-sensor output and
environmental variables to model the multidimensional relationship between
sensor output and the reference concentration. We used approximately 25 %
of the dataset to train the model. Sensor interference can be a product of
the combined influences of temperature, humidity, pressure, non-analyte gas
species, etc. The structure of the computational model accounts for both
absolute (i.e., highest to lowest concentrations) and transient (
The metrics used to evaluate the model are the slope and intercept of a
linear least squares regression of the model output with the reference
measurements, the coefficient of determination of the linear fit
(
An example of how the HDMR model is developed for the NO-B4 sensor is provided in the Supplement. The left column of Table S2 lists all available input parameters, and the other columns denote which parameters were included in the input matrix for each model run. The bottom rows list the RMSE, MAE, and MBE for each model run for both the training data (model generation) and test data (model evaluation).
The data presented in this paper were recorded over a 4.5-month sampling
interval (7 July 2016–23 November 2016). All four electrochemical sensors
used in this study were first removed from their packaging on 9 May 2016.
That means that from the date of unpackaging, the sensors had aged
Continuous 5 min average non-pollutant data acquired with the ARISense
system are shown in Fig. S2 in the Supplement, tracking ambient variability
in temperature, pressure, humidity, solar intensity, ambient noise, wind
speed, and wind direction at the Roxbury DEP monitoring site. The total
sampling time span covers the transition from mid-summer through late fall in
the northeastern United States (July through November), with meteorological
conditions changing from warmer and more humid to cooler and less humid. The
ARISense system ran continuously throughout the sampling interval with the
exception of a
Figure 3 shows the time series for a
Performance metrics for raw sensor output versus reference measurements.
Correlation plots for all electrochemical sensors versus
reference measurements for
The raw differential signals obtained from the CO-B4 sensor track reasonably
well with the CO concentrations measured by the co-located DEP monitor (Figs. 3b and 4a),
demonstrating the relatively small influence of ambient
temperature, humidity, or other chemical species on this EC sensor. The NO
sensor raw output also tracks reasonably well with the reference
measurements (Figs. 3c and 4b) except at temperatures over 25
The NO
As Fig. 3 shows, the magnitude of the interference signal due to temperature
alone (for NO, NO
Performance metrics for model output versus reference measurements for training data.
Time series of 5 min averages of the model output (sensor) and reference gas concentrations. Grey shaded areas indicate time periods over which the model was trained. A unique set of input parameters was used to train the HDMR model for each of the different electrochemical cells. Approximately 25 % of the data were used for training and the remaining 75 % was used to test the models.
The training data for the HDMR model were chosen to provide comprehensive coverage of environmental variability spanning the July–November sampling interval. It was important to include (1) sensor responses to the range of gas concentrations encountered in ambient air (near-zero to high concentration transient spikes), (2) the range of temperatures and various rates of change in temperature, and (3) the range of measured water content of the sample air in the flow cell. The goal was to include a wide enough range of training data to avoid extrapolation errors when applying the model to the test dataset (all ambient co-location data not included in the training dataset). Figure S3 shows the distributions of temperature, reference measurement, dew point temperature, and relative humidity for the training data for the CO-B4 HDMR model, overlaid with corresponding distributions of the test data. We did not attempt to minimize the amount of ambient data used for training or vary the timing of the training data with respect to the test data. Approximately 25 % of the full time series was used to generate the model (Table 2 and indicated with grey bars in Fig. 5). The exact fraction of data used for training was slightly different for each sensor due to differing calibration schedules for the reference measurements (which automatically excludes sensor data from the training or test datasets). For each sensor, the set of inputs included in the input data matrix was optimized as described in Sect. 2.5 and the Supplement.
Correlation plots of model-derived pollutant concentrations and reference
concentrations for the training data are shown in the middle panels (e–h)
of Fig. 4. The linear regression fit (solid red line) and a
Performance metrics for model output versus reference measurements for test data (5 min average temporal resolution).
The HDMR models were then used to analyze the remaining
The higher scatter in the O
Comparisons to published results utilizing integrated multi-pollutant systems comprised of Alphasense electrochemical sensors.
NR
It should be noted that the Ox-B421 sensor is not the latest version
released by Alphasense and improvements may be realized with the design of
their most recent model (Ox-B431). The results of Zimmerman et al. (2017)
for the Ox-B431 sensor have a much better
Examination of the model output for 72 h of the test data in Fig. 3
gives additional clues for improving the model. In Fig. 3d at
Table 4 presents a comparison of evaluation statistics for this work and
three other recent studies that evaluated Alphasense electrochemical sensors
through extended co-located field measurements. One important distinction
among the papers summarized in the table is that our work and the work of
Zimmerman et al. (2017) are obtained from an AQ system integrator
perspective (building and evaluating interference models), whereas the work
of Jiao et al. (2016) and Castell et al. (2017) are end-user evaluations,
reliant on the manufacturer's calibrated outputs. The duration of each
co-location, temporal resolution of the reported sensor measurements, and
variability in pollutant concentrations (and environmental conditions) at
each site strongly influence the performance metrics of the electrochemical
sensors. Results from the ARISense HDMR model at 5 min resolution show
significant improvement relative to Jiao et al. (2016) and Castell et al. (2017)
for all measured species and comparable metrics to Zimmerman et al. (2017) for
CO and NO
While Figures 4 and 5 illustrate that the system is capable of determining
valid gas-phase concentrations across a wide range of environmental
variability in temperature, RH, and absolute concentrations, it does not
speak to the longer-term stability of the sensors (e.g., how much does the
baseline and sensitivity of each electrochemical sensor change with time).
However, it should be noted that sensor aging cannot have had a major impact
on the data reported here or it would have been impossible for the HDMR
model to converge this well without including electrode age as one of the
input variables. For the models developed in this work, each data point for
each variable had equal weight, whether it was at the beginning, middle, or
end of the 4.5-month deployment. It is to be expected that aging of
EC sensors will change their sensitivities, due to electrolyte evaporation
or dilution, entrapment of contaminants, and repeated exposure to wide
swings in
This study demonstrates that lower-cost air-quality-sensor systems can
adequately characterize ambient urban pollution concentrations on rapid
(5 min) timescales, underscoring the potential of integrated sensor systems
to add a highly resolved local AQ data layer to existing pollution monitoring
infrastructure. The ARISense system is a first step toward understanding the
extent to which quantification efforts can yield useful results from such
systems. Training electrochemical sensor measurements of CO, NO, NO
Referring back to the map displayed in Fig. 2, it is striking to consider
that only four official monitoring stations exist within the Boston metropolitan area
(population
It cannot be overstated that EC-sensor systems such as ARISense can return reliable data only if calibrated over the full range of pollutant concentrations and meteorological parameters that will be encountered when they are deployed. In the present study, co-location of the ARISense system with the MassDEP reference monitors, coupled with variability of natural processes and anthropogenic activities, supplied the necessary range of conditions over the 4.5-month span of the study. In the future, we expect to compress that training period, using a controlled-environment laboratory chamber and mixes of calibration gases representative of the pollutants encountered under ambient conditions. This compression of the training period is especially important when addressing the challenges of sensor-to-sensor variability, finite (< 24–36 months) sensor lifetime, and premature damage or failures that will require rapid replacement and retraining of integrated systems.
The data are available upon request to Eben S. Cross (escross@aerodyne.com).
Eben S. Cross, Leah R. Williams, David K. Lewis, Gregory R. Magoon, Timothy B. Onasch, Douglas R. Worsnop, and John T. Jayne are employees of Aerodyne Research, Inc., which is developing and commercializing the ARISense system used in this study.
The authors would like to thank the staff at the Massachusetts Department of Environmental Protection for their support, including access to the DEP monitoring stations and raw 1 min data from the reference gas analyzers used in the current study. We are especially grateful to John Lane, Leslie Collyer, Emmy Andersen, Patrick Shea, and Thomas McGrath at MassDEP. ARISense builds upon the DAQSS network; Eben S. Cross acknowledges David Hagan for his support of the DAQSS project (electronics, backend development, and system integration) and for helpful discussions regarding advanced modeling approaches to sensor quantification. Eben S. Cross thanks Ann Backus and Gary Adamkiewicz for their collaboration on the DAQSS project (NIEHS grant P30 ES000002 via the Community Outreach and Engagement Core, COEC, of the HSPH NIEHS Center for Environmental Health). ESC also thanks Stephen Prescott for his assistance with the ARISense mechanical–electrical assembly, Xavier Cabral for his contributions to the initial electrical design, and Conor Mackinson and Wade Robinson for their mechanical design contributions to the ARISense package. The authors also thank the reviewers for their insightful comments which helped improve the final manuscript. Edited by: Gavin Phillips Reviewed by: three anonymous referees