Evaluation of Low-Cost CO2 Sensors Using Reference Instruments and Standard Gases for Indoor Use

CO2 monitoring is important for carbon emission evaluation. Low-cost and medium-precision sensors (LCSs) have become an exploratory direction for CO2 observation under complex emission conditions in cities. Here, we used a calibration method that improved the accuracy of SenseAir K30 CO2 sensors from ±30 ppm to 0.7–4.0 ppm for a CO2-monitoring instrument named the SENSE-IAP, which has been used in several cities, such as in Beijing, Jinan, Fuzhou, Hangzhou, and Wuhan, in China since 2017. We conducted monthly to yearly synchronous observations using the SENSE-IAP along with reference instruments (Picarro) and standard gas to evaluate the performance of the LCSs for indoor use with relatively stable environments. The results show that the precision and accuracy of the SENSE-IAP compared to the standard gases were rather good in relatively stable indoor environments, with the short-term (daily scale) biases ranging from −0.9 to 0.2 ppm, the root mean square errors (RMSE) ranging from 0.7 to 1.6 ppm, the long-term (monthly scale) bias ranging from −1.6 to 0.5 ppm, and the RMSE ranging from 1.3 to 3.2 ppm. The accuracy of the synchronous observations with Picarro was in the same magnitude, with an RMSE of 2.0–3.0 ppm. According to our evaluation, standard instruments or reliable standard gases can be used as a reference to improve the accuracy of the SENSE-IAP. If calibrated daily using standard gases, the bias of the SENSE-IAP can be maintained within 1.0 ppm. If the standard gases are hard to access frequently, we recommend a calibration frequency of at least three months to maintain an accuracy within 3 ppm.


Introduction
As more than 70% of global fossil fuel CO 2 emissions originate from cities [1], to achieve net-zero GHG emissions by 2050 and a 45% decrease in emissions by 2030 [2], Sensors 2024, 24, 2680 2 of 14 effective CO 2 monitoring in cities will be crucial for meeting emission reduction commitments.Several major cities in the USA and Europe have conducted carbon monitoring projects over the last decade [3][4][5][6][7][8].Since then, some studies have evaluated the accuracy of emission inventories by relying on a CO 2 observation network and inverse modeling approaches [7,9,10].
The emission sources of CO 2 in cities are diverse [11,12] and have complex spatial and temporal changes [13].Moreover, the observed concentrations are dominated by meteorological changes [14,15] and could be misinterpreted by biogenic fluxes [16].A strategy for resolving these challenges is to establish high-density, low-cost, and medium-precision sensor (LCS) networks [17][18][19][20].In recent decades, medium-precision carbon monitoring equipment has been used in the exploratory stage of application.Compared to highprecision instruments, such as Picarro and ABB-LGR, LCSs, which have accuracies of 1-10 ppm, have a price reduction of more than one order of magnitude (USD 5000-15,000) and can be deployed in large quantities.Such low-cost and high-density advantages make them competitive for urban carbon monitoring [21][22][23].
Currently, several cities, such as the Swiss network [22] and the California network in the USA [21,24] (Table 1), have constructed high-density CO 2 networks using LCSs.To reveal the site-specific CO 2 signals at most locations in Switzerland, the Carbosense CO 2 sensor network in Switzerland includes more than 300 nodes and was constructed using Swedish SenseAir LP8 sensors (Senseair AB, Delsbo, Sweden).With calibration in a laboratory chamber, ambient colocation with a nearby reference instrument, and regular drift correction during deployment, the observation accuracy can reach between 8 and 12 ppm [22].The Berkeley Environmental Air Quality and CO 2 Network (BEACO 2 N) in California, USA, has approximately 40 sites, where the Vaisala CarboCap GMP343 sensors measure atmospheric CO 2 .With an in situ method for correcting biases and time-dependent drift over time, the calibrated observation accuracy is approximately 1-4 ppm [21,25].Furthermore, the reported accuracy of BEACO 2 N reached 1.6-3.6 ppm when temperature corrections were used [24].The medium-precision CO 2 measuring instrument also included a LI-COR-830/850, with an accuracy between 6 and 12 ppm.China has committed to peaking its carbon emissions before 2030 and achieving carbon neutrality before 2060 (the dual carbon goals, DCGs) [26][27][28].To dynamically assess CO 2 emissions from cities, the Ministry of Ecology and Environment of China issued the "Carbon monitoring and assessment pilot work program" in 2021 [29].Since then, the carbon monitoring abilities of cities and provinces in China have greatly increased.
Since 2017, the Institute of Atmospheric Physics, Chinese Academy of Sciences, has established a network with more than 120 sites using LCSs for CO 2 monitoring, mostly in the city of Beijing and surrounding regions [30].We made efforts in the environmental calibration of the instrument to improve the accuracy of SenseAir K30 CO 2 sensors from ±30 ppm to 0.8-4 ppm [31,32].However, compared to the stability of high-precision instruments, LCSs are susceptible to time-dependent drift over time as well as to environmental variables [24,[33][34][35].Thus, LCSs are usually taken to a laboratory for regular calibration or for in situ field calibration for biases.
In this study, we conducted synchronous observations of our environment-calibrated LCSs for a few weeks to more than one year at four different sites under typical use condi-tions.The LCSs were compared to high-precision Picarro instruments, and standard gases were traceable to the WMO X2007 scale.Our comparison provides a basic understanding and evaluation of the performance of our LCSs.The use of high-precision instruments and standard gas tanks also helps us gain a basis for short-term and long-term time-dependent drift calibration methods that can improve the accuracy of urban CO 2 networks using LCSs.

Sensor Deployment
The LCSs were deployed in the following three cities (Figure 1a): Beijing (Figure 1b), Jinan in Shandong Province (Figure 1c), and Hangzhou in Zhejiang Province (Figure 1d).They were compared with a high-precision instrument or standard gas.The Beijing sites were located at the Institute of Atmospheric Physics, Chinese Academy of Sciences (Beijing-IAP, 39.39 N, 116.39 E, 62 m) and the China National Environment Monitoring Center (Beijing-CNEMC, 40.04 N, 116.42 E), in the central area of the city, which has a high population density and is greatly affected by traffic emissions.The Jinan site was in the suburbs of an urban area (36.83 N, 117.09E, 44 m) featuring a low population density and low emissions from transportation and industrial parks.The Hangzhou Mount Mantou station (30.23 N, 120.16 E, 43.2 m a.s.l.) is located at the convergence of the West Lake scenic area and residential areas in the south of city, where there is a high population density and influence of human activities.
In this study, we conducted synchronous observations of our environment-calibrated LCSs for a few weeks to more than one year at four different sites under typical use conditions.The LCSs were compared to high-precision Picarro instruments, and standard gases were traceable to the WMO X2007 scale.Our comparison provides a basic understanding and evaluation of the performance of our LCSs.The use of high-precision instruments and standard gas tanks also helps us gain a basis for short-term and long-term time-dependent drift calibration methods that can improve the accuracy of urban CO2 networks using LCSs.

Sensor Deployment
The LCSs were deployed in the following three cities (Figure 1a): Beijing (Figure 1b), Jinan in Shandong Province (Figure 1c), and Hangzhou in Zhejiang Province (Figure 1d).They were compared with a high-precision instrument or standard gas.The Beijing sites were located at the Institute of Atmospheric Physics, Chinese Academy of Sciences (Beijing-IAP, 39.39 N, 116.39 E, 62 m) and the China National Environment Monitoring Center (Beijing-CNEMC, 40.04 N, 116.42 E), in the central area of the city, which has a high population density and is greatly affected by traffic emissions.The Jinan site was in the suburbs of an urban area (36.83 N, 117.09E, 44 m) featuring a low population density and low emissions from transportation and industrial parks.The Hangzhou Mount Mantou station (30.23 N, 120.16 E, 43.2 m a.s.l.) is located at the convergence of the West Lake scenic area and residential areas in the south of city, where there is a high population density and influence of human activities.The precision and specificity of the reading accuracy of the sensors and instruments at the four sites are listed in Table 2.For our LCSs, we used a non-dispersive infrared (NDIR) CO 2 measurement (SenseAir K30, Senseair AB, Delsbo, Sweden), which has a ±30 ppm raw accuracy [36].Three K30s were put in one instrument with a Bosch BME680 sensor to record the temperature, humidity, and air pressure [31,32].This integrated instrument with environmental calibration is called the SENSE-IAP.We used a cavity ring-down spectrometer (Picarro G2301/G4301, Picarro, Santa Clara, CA, USA) as the high-precision instrument for the CO 2 measurements [37,38].The precision and accuracy of the Picarro instrument were better than 0.1 ppm [39].The standard gas tanks used at all four sites were obtained from the Meteorological Observation Center of the China Meteorological Administration (MOC/CMA) and traceable to the World Meteorological Organization (WMO) X2007 scale.To ensure the long-term synchronous observation of the LCSs and high-precision instruments, in their deployment, we made an effort to ensure that the two sets of instruments measured the same gas mass.That is, we ensured that the differences in the instrument observation values only came from the effects of temperature, humidity, air pressure, and the concentration span, which can be adjusted by calibration methods.What is more, to evaluate the gradual change in the systematic bias of low-cost sensors in longterm deployment, the synchronous observation instruments were all in a relatively stable indoor environment.Although the outdoor air CO 2 concentration extracted by the pump exhibited significant diurnal variations (Figure S1a,c), the temperature and humidity of the pre-processed air were basically stable (Figure S1b,d,f).
At the Beijing-IAP, Jinan, and Hangzhou sites, the ambient air was drawn by a pump from outside the window, and the intake was linked through a pipe with a particulate matter filter and a water dryer.As shown in Figure 2a,d, to assess the precision and accuracy of the SENSE-IAP, at the site of Beijing-IAP, excess gas was discharged from the outlet of the Picarro system and connected to sensors on the SENSE-IAP (number pi840).The ambient air at the Jinan site was diverted through a three-way valve to the SENSE-IAP (number pi925) and Picarro G4301 (Figure 2b,e).The long indoor pipe caused the temperature of the air gas to approach the indoor temperature controlled by the air conditioner.At the Beijing-IAP and Jinan sites, the Picarro analyzer was calibrated every month by high-pressure standard gases.
For the two SENSE-IAPs (numbers pi488 and pi674) deployed at Beijing-CNEMC, the standard gas from the gas tank directly flowed into the sensor of the SENSE-IAP through pipes (Figure 2c,f).We also deployed the SENSE-IAP (number pi642) at Hangzhou using a deployment method similar to that used for Beijing-IAP.The difference is that we only analyzed the CO 2 concentration observed by the SENSE-IAP when the standard gas was introduced every six hours.

Sensor Calibration and Evaluation Parameters
We developed a calibration system to substantially improve the CO2 accuracy of the K30 sensor.To reduce the background or white noise, we excluded anomalous observation values based on the 3-σ principle every minute.After calibrating for the sensitivity of temperature, humidity, and pressure effects in the laboratory [35], the accuracy increased to 1-4 ppm in comparison to that of Picarro [31].We adjusted the calibration of the span and system bias before observation and adjusted the gradual time-dependent drift (in ppm/day) using the following formula: where ΔCdrift is the bias between the concentration C measured by the instrument and the standard concentration C0 at the end of the time-dependent drift.The end time of drift is the time when the slope of the drift tended toward stability or any time when the sensors needed to be corrected.  is the long-term drift-calibrated CO2 concentration.
The following two parameters were used to evaluate the precision and accuracy of the SENSE-IAP.The root mean square error (RMSE) (2) was used to evaluate the precision, and the bias (3) was used to determine the measurement accuracy of the SENSE-IAP compared with that of the Picarro standard gas concentration.
where C is the CO2 concentration measured by the SENSE-IAP, and C0 is the standard concentration measured by Picarro or the concentration of standard gas.

Sensor Calibration and Evaluation Parameters
We developed a calibration system to substantially improve the CO 2 accuracy of the K30 sensor.To reduce the background or white noise, we excluded anomalous observation values based on the 3-σ principle every minute.After calibrating for the sensitivity of temperature, humidity, and pressure effects in the laboratory [35], the accuracy increased to 1-4 ppm in comparison to that of Picarro [31].We adjusted the calibration of the span and system bias before observation and adjusted the gradual time-dependent drift (in ppm/day) using the following formula: where ∆C drift is the bias between the concentration C measured by the instrument and the standard concentration C 0 at the end of the time-dependent drift.The end time of drift is the time when the slope of the drift tended toward stability or any time when the sensors needed to be corrected.C Cor is the long-term drift-calibrated CO 2 concentration.The following two parameters were used to evaluate the precision and accuracy of the SENSE-IAP.The root mean square error (RMSE) (2) was used to evaluate the precision, and the bias (3) was used to determine the measurement accuracy of the SENSE-IAP compared with that of the Picarro standard gas concentration.

RMSE
where C is the CO 2 concentration measured by the SENSE-IAP, and C 0 is the standard concentration measured by Picarro or the concentration of standard gas.

Daily to Monthly Comparisons with Standard Gases
At the Beijing-CNEMC site, we conducted observations of the SENSE-IAPs for both short-term tests (six hours) and long-term tests (seven weeks) in the months of January and February 2022.Throughout the experiment, the temperature in the laboratory was controlled at 23-25 • C by air conditioning.And the gas from the standard gas tank was similar to dry gas with very low water content.For short-term observations, the SENSE-IAP monitored the CO 2 concentration from the gas tank for seven continuous hours, and the data from the first hour were not calculated due to the need for stable ventilation.During the continuous short-term observation of the standard gas, the SENSE-IAPs exhibited a bias of −0.56 ± 0.38 ppm compared to the standard gas, with an RMSE of 1.16 ± 0.28 ppm (Figure 3 and Table 3).

Daily to Monthly Comparisons with Standard Gases
At the Beijing-CNEMC site, we conducted observations of the SENSE-IAPs for both short-term tests (six hours) and long-term tests (seven weeks) in the months of January and February 2022.Throughout the experiment, the temperature in the laboratory was controlled at 23-25 °C by air conditioning.And the gas from the standard gas tank was similar to dry gas with very low water content.For short-term observations, the SENSE-IAP monitored the CO2 concentration from the gas tank for seven continuous hours, and the data from the first hour were not calculated due to the need for stable ventilation.During the continuous short-term observation of the standard gas, the SENSE-IAPs exhibited a bias of −0.56 ± 0.38 ppm compared to the standard gas, with an RMSE of 1.16 ± 0.28 ppm (Figure 3 and Table 3).For the long-term observation, the CO2 concentration in the gas tank of the SENSE-IAP was monitored for one hour per week.Outside of the specific one-hour pe-  For the long-term observation, the CO 2 concentration in the gas tank of the SENSE-IAP was monitored for one hour per week.Outside of the specific one-hour period of the standard gas measurement per week, the SENSE-IAP measured the ambient CO 2 concentration.The hourly mean CO 2 concentrations monitored over six weeks compared to those of the standard gas are shown as points in Figure 4b.For all six sensors, the bias was −0.56 ± 0.38 ppm, and the RMSE was 2.08 ± 0.68 ppm (Figure 4a,b and Table 3).For each standard gas measurement, the bias was in the range of −3.87-1.63ppm, while the RMSE was in the range of 0.5-3.91 ppm (Figure 4c-h).The long-term observations also show a time-dependent drift during the testing period of approximately 1.5 months.As shown in Table 3, the long-term drift trends for all six sensors were −0.09 ± 0.03 ppm per day or −3.73 ± 1.16 ppb per hour.
Sensors 2024, 24, x FOR PEER REVIEW 7 of 13 riod of the standard gas measurement per week, the SENSE-IAP measured the ambient CO2 concentration.The hourly mean CO2 concentrations monitored over six weeks compared to those of the standard gas are shown as points in Figure 4b.For all six sensors, the bias was −0.56 ± 0.38 ppm, and the RMSE was 2.08 ± 0.68 ppm (Figure 4a,b and Table 3).For each standard gas measurement, the bias was in the range of −3.87-1.63ppm, while the RMSE was in the range of 0.5-3.91 ppm (Figure 4c-h).The long-term observations also show a time-dependent drift during the testing period of approximately 1.5 months.As shown in Table 3, the long-term drift trends for all six sensors were −0.09 ± 0.03 ppm per day or −3.73 ± 1.16 ppb per hour.Sensors 2024, 24, 2680 8 of 14

Daily to Yearly Comparisons with Standard Gases
Synchronous monitoring at the Hangzhou site revealed longer comparative observations for the SENSE-IAP and standard gas.The temperature and relative humidity (RH) ranges of the monitoring period were mainly between 25 and 35 • C and 20 and 40% RH, respectively.We analyzed the accuracy and stability of the SENSE-IAP based on the difference between the concentration measured by the SENSE-IAP and the standard gas used during the Picarro calibration period every 6 h.
Figure 5 shows the mean CO 2 concentration measured by the three K30s of the SENSE-IAP at Hangzhou.The yellow points are the values per minute obtained during the Picarro calibration every 6 h.The RMSE of the SENSE-IAP relative to the introduced standard gas was 2.0 ppm, with a mean bias of −1.1 ppm for the entire 22 months of monitoring, with only one calibration after approximately 5 months of deployment for K30 and K30_3.For K30_2, long-term drift occurred 6 months after the first calibration (at 2023-02), with a daily drift of 0.1 ppm (Figure S6).
Sensors 2024, 24, x FOR PEER REVIEW 8 of 13 mean of the sensors on pi674, and red is the mean of the sensors on pi488.The plots for all the sensors in each SENSE-IAP instrument are shown in Figures S4 and S5.

Daily to Yearly Comparisons with Standard Gases
Synchronous monitoring at the Hangzhou site revealed longer comparative observations for the SENSE-IAP and standard gas.The temperature and relative humidity (RH) ranges of the monitoring period were mainly between 25 and 35 °C and 20 and 40% RH, respectively.We analyzed the accuracy and stability of the SENSE-IAP based on the difference between the concentration measured by the SENSE-IAP and the standard gas used during the Picarro calibration period every 6 h.
Figure 5 shows the mean CO2 concentration measured by the three K30s of the SENSE-IAP at Hangzhou.The yellow points are the values per minute obtained during the Picarro calibration every 6 h.The RMSE of the SENSE-IAP relative to the introduced standard gas was 2.0 ppm, with a mean bias of −1.1 ppm for the entire 22 months of monitoring, with only one calibration after approximately 5 months of deployment for K30 and K30_3.For K30_2, long-term drift occurred 6 months after the first calibration (at 2023-02), with a daily drift of 0.1 ppm (Figure S6).

Monthly Comparisons to Standard Instruments (Picarro)
At the Jinan site, the SENSE-IAP was co-localized (side-by-side) with G4301 for 4 weeks in an environment in which the diurnal temperature and humidity changes were not apparent (Figure S7c).Throughout the instrument monitoring period, the temperature and humidity ranged from 25 to 35 °C and from 0 to 40% RH, respectively.After environmental corrections, the RMSE of the SENSE-IAP was generally consistent with that of the Picarro system, ranging from 2.6 to 5.8 ppm. Figure 6b shows the difference in the CO2 concentration between the SENSE-IAP and Picarro systems (ΔCO2) during the observation period.The K30 sensor error (Figure 6b) mainly originated from a mean bias of −4.4 ppm, with a slope of 0.37 ppm/day (<0.02 ppm/h).For the other two sensors (K30_2 and K39_3), the main source of error occurred at the time at which the concentration significantly changed.If a linear correction was performed (Section 2.2) on sensor

Monthly Comparisons to Standard Instruments (Picarro)
At the Jinan site, the SENSE-IAP was co-localized (side-by-side) with G4301 for 4 weeks in an environment in which the diurnal temperature and humidity changes were not apparent (Figure S7c).Throughout the instrument monitoring period, the temperature and humidity ranged from 25 to 35 • C and from 0 to 40% RH, respectively.After environmental corrections, the RMSE of the SENSE-IAP was generally consistent with that of the Picarro system, ranging from 2.6 to 5.8 ppm. Figure 6b shows the difference in the CO 2 concentration between the SENSE-IAP and Picarro systems (∆CO 2 ) during the observation period.The K30 sensor error (Figure 6b) mainly originated from a mean bias of −4.4 ppm, with a slope of 0.37 ppm/day (<0.02 ppm/h).For the other two sensors (K30_2 and K39_3), the main source of error occurred at the time at which the concentration significantly changed.If a linear correction was performed (Section 2.2) on sensor K30 during the start/end point of the observation period, the RMSE could be improved to at least 3.0 ppm (Figure S7).K30 during the start/end point of the observation period, the RMSE could be improved to at least 3.0 ppm (Figure S7).

Yearly Comparison to Standard Instruments
Figure 7 shows the results of the environment-corrected and long-term drift-adjusted SENSE-IAP at the Beijing-IAP site compared to those of the Picarro system.Due to the fact that the sensor of the SENSE-IAP measured the excess gas discharged from the outlet of the Picarro system, the diurnal variations in the temperature and humidity of the air were no longer significant since they were controlled by the Picarro system (Figure 1b).Throughout the observation period, the temperature and humidity ranged from 20 to 35°C and from 10 to 20% RH, respectively (Figure S8c).All three sensors exhibited long-term downward drifts of up to −16 ppm after half a year and up to −24.4 ppm after one year of deployment.For K30s on pi840, the ΔCO2 showed a continuous downward trend over the past 12 months, with RMSEs of 24.5, 9.9, and 14.6 ppm, respectively (Figure S8), and the drift trend became significantly unstable over time.We adjusted the long-term drift trends of the three sensors during the observation period from November 2022 to November 2023 based on Function (1) in Section 2.2.The corrected CO2 concentrations from the three sensors were consistent with those of the Picarro system, with a bias ranging from 0.9 to 2.1 ppm (Figure 7).Our correction method improved the RMSE from 14.3-15.4ppm of the sensor manufacturer's raw data (SenseAir, Figure S9) to 2.0-3.0 ppm (SENSE-IAP, Figure 7).

Yearly Comparison to Standard Instruments
Figure 7 shows the results of the environment-corrected and long-term drift-adjusted SENSE-IAP at the Beijing-IAP site compared to those of the Picarro system.Due to the fact that the sensor of the SENSE-IAP measured the excess gas discharged from the outlet of the Picarro system, the diurnal variations in the temperature and humidity of the air were no longer significant since they were controlled by the Picarro system (Figure 1b).Throughout the observation period, the temperature and humidity ranged from 20 to 35 • C and from 10 to 20% RH, respectively (Figure S8c).All three sensors exhibited long-term downward drifts of up to −16 ppm after half a year and up to −24.4 ppm after one year of deployment.For K30s on pi840, the ∆CO 2 showed a continuous downward trend over the past 12 months, with RMSEs of 24.5, 9.9, and 14.6 ppm, respectively (Figure S8), and the drift trend became significantly unstable over time.We adjusted the long-term drift trends of the three sensors during the observation period from November 2022 to November 2023 based on Function (1) in Section 2.2.The corrected CO 2 concentrations from the three sensors were consistent with those of the Picarro system, with a bias ranging from 0.9 to 2.1 ppm (Figure 7).Our correction method improved the RMSE from 14.3-15.4ppm of the sensor manufacturer's raw data (SenseAir, Figure S9) to 2.0-3.0 ppm (SENSE-IAP, Figure 7).

Figure 1 .
Figure 1.(a) Map of the four deployment sites; (b) Beijing sites: Institute of Atmospheric Physics (Beijing-IAP) and China National Environment Monitoring Center (Beijing-CNEMC); (c) Jinan site, Shandong Province; and (d) Hangzhou site, Zhejiang Province.

Figure 1 .
Figure 1.(a) Map of the four deployment sites; (b) Beijing sites: Institute of Atmospheric Physics (Beijing-IAP) and China National Environment Monitoring Center (Beijing-CNEMC); (c) Jinan site, Shandong Province; and (d) Hangzhou site, Zhejiang Province.

Figure 2 .
Figure 2. Synchronous observation system of the LCSs and the Picarro instrument.Diagram (a-c) of gas flow for the LCSs and Picarro instrument.Photographs (d-f) of the instrument installation.(a,d) Beijing-IAP, (b,e) Jinan, and (c,f) Beijing-CNEMC.

Figure 2 .
Figure 2. Synchronous observation system of the LCSs and the Picarro instrument.Diagram (a-c) of gas flow for the LCSs and Picarro instrument.Photographs (d-f) of the instrument installation.(a,d) Beijing-IAP, (b,e) Jinan, and (c,f) Beijing-CNEMC.

Figure 3 .Table 3 .
Figure 3.Comparison between the CO2 concentrations per minute measured by the SENSE-IAP and the CO2 concentrations of standard gas at Beijing-CNEMC on 15 January 2022.The blue lines show the time span of the sampling data.(a) The mean of the three sensors on one SENSE-IAP instrument; the plot for each sensor is shown in Figures S2 and S3.(b) The time series of the difference with standard gas.Black is the mean of the three sensors on pi674, and gray is the mean of the sensors on pi488.Table 3. Performance of the six sensors on two SENSE-IAP instruments (unit: ppm).

Figure 3 .
Figure 3.Comparison between the CO 2 concentrations per minute measured by the SENSE-IAP and the CO 2 concentrations of standard gas at Beijing-CNEMC on 15 January 2022.The blue lines show the time span of the sampling data.(a) The mean of the three sensors on one SENSE-IAP instrument; the plot for each sensor is shown in Figures S2 and S3.(b) The time series of the difference with standard gas.Black is the mean of the three sensors on pi674, and gray is the mean of the sensors on pi488.

Figure 4 .
Figure 4. Comparison between the CO2 concentrations measured by the SENSE-IAP and the CO2 concentrations of standard gas at Beijing-CNEMC in January and February 2022.(a) The time series of CO2 per minute in the whole measurement period; the blue dashed lines mark one hour of the standard gas measurement per week, and the green dashed lines mark the concentration of standard gas.(b) The points are the hourly means of values during one hour of standard gas measurement per week.(c-h) The time series of the difference in the standard gas in each week.Black is the

Figure 4 .
Figure 4. Comparison between the CO 2 concentrations measured by the SENSE-IAP and the CO 2 concentrations of standard gas at Beijing-CNEMC in January and February 2022.(a) The time series of CO 2 per minute in the whole measurement period; the blue dashed lines mark one hour of the standard gas measurement per week, and the green dashed lines mark the concentration of standard gas.(b) The points are the hourly means of values during one hour of standard gas measurement per week.(c-h) The time series of the difference in the standard gas in each week.Black is the mean of the three sensors on pi674, and red is the mean of the sensors on pi488.The plots for all the sensors in each SENSE-IAP instrument are shown in Figures S4 and S5.

Figure 5 .
Figure 5.Comparison between the minute CO2 concentrations measured by the SENSE-IAP and the CO2 concentrations of standard gas at the Hangzhou site from 18 February 2022 to 25 December 2023.(a) Time series of the CO2 concentrations; the blue points are the means of the CO2 concentrations measured by the three sensors on the SENSE-IAP; the yellow points indicate the time points when the standard gas was introduced; and (b) the difference between the measured CO2 and standard gas during the period when the gas was introduced; the points are consistent with (a) in terms of time.(c) The temperature and RH of the SENSE-IAP.

Figure 5 .
Figure 5.Comparison between the minute CO 2 concentrations measured by the SENSE-IAP and the CO 2 concentrations of standard gas at the Hangzhou site from 18 February 2022 to 25 December 2023.(a) Time series of the CO 2 concentrations; the blue points are the means of the CO 2 concentrations measured by the three sensors on the SENSE-IAP; the yellow points indicate the time points when the standard gas was introduced; and (b) the difference between the measured CO 2 and standard gas during the period when the gas was introduced; the points are consistent with (a) in terms of time.(c) The temperature and RH of the SENSE-IAP.

Figure 6 .
Figure 6.(a,d,g) Comparisons of the CO2 concentrations per minute measured by three sensors (the SENSE-IAP and the Picarro system) at the Jinan site from 17 October to 17 November in 2023.(b,e,h) The time series of ΔCO2, and (c,f,i) the scatterplots of the SENSE-IAP and Picarro.(a-c) The first sensor, K30, (d-f) the second sensor, K30_2, and (g-i) the third sensor, K30_3.

Figure 6 .
Figure 6.(a,d,g) Comparisons of the CO 2 concentrations per minute measured by three sensors (the SENSE-IAP and the Picarro system) at the Jinan site from 17 October to 17 November in 2023.(b,e,h) The time series of ∆CO 2 , and (c,f,i) the scatterplots of the SENSE-IAP and Picarro.(a-c) The first sensor, K30, (d-f) the second sensor, K30_2, and (g-i) the third sensor, K30_3.

Funding:
This research was supported by the National Key R&D Program of China (Nos.2023YFC3705500 and 2017YFB0504000) funded by Ministry of Science and Technology of the People's Republic of China; the China Quality Certification Center project: Monitoring, Simulation and Inventory Joint Assessment of Carbon Emissions in Typical Industrial Parks against the Background of Carbon Peaking and Carbon Neutrality (grant no.2022ZJYF001); Jinan Carbon Monitoring and Evaluation Pilot Project (grant no.SDGP370100000202202001740) funded by Jinan Municipal Bureau of Ecology and Environment; the Qiluzhongke Institute of Carbon Neutrality Program of Jinan Dual Carbon Simulator funded by Qilu Zhongke Carbon Neutrality Research Institute; and the CAS Proof of Concept Program: Carbon Neutralityoriented Urban Carbon Monitoring System and Its Industrialization (grant no.CAS-GNYZ-2022) funded by Zhihui Zhongke (Beijing) Technology Co., Ltd.; and the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (grant no.LZJMZ23D050002) funded by Science Technology Department of Zhejiang Province.Institutional Review Board Statement: Not applicable.Informed Consent Statement: Not applicable.

Table 1 .
Comparisons of the precision and price of medium-precision CO 2 measuring instruments.

Table 2 .
Sensor and instrument parameters for the four sites.

Table 3 .
Performance of the six sensors on two SENSE-IAP instruments (unit: ppm).