Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor

Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor Roubina Papaconstantinou, S. Bezantakos, M. Pikridas, M. Parolin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5349649/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Low-cost gas and particle sensors can significantly increase the spatial coverage of Air Quality (AQ) monitoring networks in urban settings. Considering that the accuracy of such sensors is not high enough to replace reference instruments for AQ monitoring, the question is whether they can be used to capture spatial differences among different stations, as well as temporal trends and month-to-month variabilities at a specific location. To investigate that, we carried out measurements over a period of 19 months with two Vaisala AQ Transmitters-Monitors (Model AQT530), collocated with reference-grade instruments, in two AQ monitoring stations in Nicosia: an urban traffic and an urban background station. The AQ monitors employ Low-Cost Sensors (LCSs) for gaseous pollutants (i.e., CO, NO 2 , NO, and O 3 ) and Particulate Matter (PM). Statistical analysis of the reference measurements shows that the mean concentrations of the pollutants at the two stations, determined over the entire study period and for each month separately, were significantly different. Analysis of the LCS measurements showed that that the reproducibility of the NO 2 , NO, O 3 , and PM 2.5 sensors, over a period when these were co-located at the traffic station, is poor, excluding them from further investigating their ability to capture spatial differences between different stations. The CO and PM 10 measurements from the AQ monitors effectively captured the differences in pollutant concentrations between the two stations when averaged over the entire study period or on a monthly basis, with few exceptions during specific months depending on the sensor. These LCSs were also able to capture concentration differences between the two stations on a daily or monthly basis, as long as those were above a certain threshold for each pollutant. The CO and PM sensors captured the month-to-month trend over the entire period of the measurements, similarly to the reference instruments, while the NO 2 , NO and O 3 sensors did not, mainly due to their sensitivity to the environmental conditions. Despite that, all sensors captured the statistical significance of the month-to-month concentration differences at the same station, with the PM 2.5 measurements showing the highest capability of doing so in accordance with the reference instruments. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Air pollution is a major concern of our modern societies, due to its adverse effects upon human health and the environment (Juginovic et al., 2021 ; Kuntic et al. 2023 ). This is more so in urban agglomerates, where a range of human activities can yield high concentrations of air pollutants at specific locations, typically referred to as air pollution hot spots, creating notable spatial and temporal variabilities within a city. Although capturing this variability with in-situ air quality (AQ) monitors is strongly desired, the high capital and maintenance costs of the necessary instruments still limit the density of urban AQ monitoring stations. For example, in large European cities (e.g., London, Paris, Rome, Madrid, Berlin and Athens) the number of fixed AQ monitoring stations is of the order of 1 station every ca. 50 km 2 or 170 thousand inhabitants (London Air, 2018 ; Association for the Monitoring of Air Quality in the Île-de-France, 2018; Regional Agency for the Protection of the Environment of Lazio, 2020; Madrid Air Quality Portal, 2022; Berlin Air Quality Monitoring Network, 2019 ; Ministry of Environment and Energy, 2022 ). Similarly, the city of Nicosia operates only two AQ monitoring stations, corresponding to ca. 1 station every 50 km 2 or 150 thousand inhabitants (Department of Labour Inspection, 2021). The limited number of fixed air quality monitoring stations may result in missing localized pollution hot spots, preventing them from capturing spatial variabilities in urban areas. Low-cost AQ sensors have evolved rapidly over the last few decades. Among all types of LCSs of gaseous pollutants, modern electrochemical (EC) sensors typically exhibit a wide detection range, fast enough response, as well as adequate selectivity and sensitivity that can qualify them for AQ measurements (He et al. 2023 ). Additional advantages such as simple and robust operation, low power consumption and portability, combined with the ease of installation, allow their deployment in dense AQ networks (Bulot et al. 2019 ; Bílek et al. 2021 ; Frederickson et al. 2022 ; deSouza et al. 2022 ; Raheja et al. 2023 ). The accuracy of EC sensors when tested under controlled (laboratory) conditions can range within several tens of percent (Collier-Oxandale et al. 2020 ; Pang et al. 2017 ; Castell et al. 2017 ). Similarly, the accuracy of the PM sensor employed in the Vaisala AQT530 is in the order of 5% according to the manufacturer (AQT530, 2023 ), which is at least one order of magnitude higher compared to the respective values of reference PM instruments; e.g., ± 0.75% for the tapered element oscillating microbalance (TEOM; TEOM 1405-DF, 2020). When compared against reference instruments under field conditions, the performance of low-cost AQ sensors has been shown to deviate further. This is not surprising considering that LCSs can be strongly influenced by their operating environmental conditions, including air temperature, relative humidity (RH), and the presence of other gaseous pollutants that can induce a measurable signal (Spinelle et al. 2015a , 2015b ; Lewis et al. 2016 ; Borrego et al. 2016 ; Castell et al. 2017 ; Cui et al. 2021 ). The performance limitations of EC sensors are primarily related to the nature of the redox reactions at the surface of the sensing electrode (Stetter and Li, 2008 ). When gas molecules adsorb onto the electrode, they undergo oxidation or reduction, producing an electrical current proportional to their concentration. This process, and consequently the accuracy of EC sensors, is significantly influenced by the operating temperature and relative humidity (Wei et al., 2018 ). Low-cost PM sensors, on the other hand, typically rely on optical methods using light scattered by the sampled particles to infer their concentration, and in some models also their size (Karagulian et al. 2019 ). They can be divided into two categories: particle photometers and particle counters (McMurry 2000). Both methods provide signals that are proportional to the PM concentration, as well as on the optical properties of the sampled particles defined by their size, morphology and composition. Considering that information on particle morphology and composition is hard to obtain, we typically assume that all the particles are spherical and have a refractive index (and density when determining their mass) corresponding to polystyrene latex or ammonium sulphate aerosol particles (Marx and Mulholland, 1983 ). These assumptions contribute to the measurement uncertainty when sampling ambient atmospheric aerosol particles. When the sampled particles are hygroscopic, they can take up a significant amount of water vapour from the ambient air, consequently increasing their size and altering their refractive index significantly (Carslaw et al. 2022); a phenomenon that provides another source of uncertainty in low-cost PM sensors. As shown by a growing body of literature reports, existing LCSs cannot meet the quality objectives for use in regulatory observations that require at most 15% uncertainty (specifically expressed as relative expanded uncertainty, REU, defined by Directive 2008/50/EC, 2008) for CO, NO 2 , NO and O 3 , and 25% for PM measurements. Similarly, they often fail to meet the criteria for indicative measurements, which call for a 25% REU for CO, 30% for NO 2 , NO and O 3 , and 50% for PM, as described in the same Directive. However, they have been found useful in other applications, including identification of air pollution hot spots (Gao et al. 2015 , Baruah et al. 2023 , Feinberg et al. 2019 ), distinction between local and non-local pollution sources (Heimann et al. 2015 , Popoola et al. 2018 ), and high-resolution AQ mapping (Schneider et al. 2017 ) among others, which require lower accuracy. In such studies, LCSs have been integrated as part of networks for multi-point AQ measurements, consequently increasing the spatiotemporal resolution of AQ monitoring networks. The high uncertainties associated with LCS measurements can in principle be reduced by more laborious calibration models than those provided by the manufacturers. These models can be developed through well-designed laboratory tests (e.g., Nagendra et al. 2019 ) and/or by field measurements whereby the LCSs are collocated with reference instruments over long periods of time (Raheja et al. 2023 , Bisignano et al. 2022 , Crawford et al. 2021 , Kim et al. 2018 ). Machine-learning calibration methods can additionally be applied to the measurements in order to further improve the quality of the produced data (Bisignano et al. 2022 ; Baruah et al. 2023 ; Cabaneros et al. 2019 ). Such algorithms are already embedded in some AQ monitors that employ low-cost gas and PM sensors (e.g., the Vaisala AQT530; Petäjä et al. 2021 ). Despite these efforts, critical questions still need to be addressed, including the ability of LCSs to capture spatial and temporal variations of pollutants, and their effectiveness in identifying air pollution hotspots. In this work, we evaluate the ability of the Vaisala AQT530 monitors to capture spatial and temporal variabilities of pollutants within a city agglomerate. To achieve that we carried out measurements with two monitors located at two AQ monitoring stations (a traffic and an urban background station) in the city of Nicosia, Cyprus. Both set of sensors were collocated with reference grade instruments, over a period of 19 months. 2 METHODS 2.1 Description of Air-Quality Measurement Stations Figure 1 shows an aerial photograph of part of the city of Nicosia, with the locations of the two AQ measurement stations. The distance between the two stations is 3.5 km. The Nicosia Traffic Station (referred to as TRS from now on), has a distance of ten metres from one of the main and busiest city avenues (cf. Figure 1 ), which is typically congested during the morning and the afternoon rush hours. The site is operated by the Department of Labour Inspection (DLI), which is part of the Ministry of Labour and Social Insurance of Cyprus, and one of the two reference AQ stations of Nicosia (Department of Labour Inspection, 2021). Measurements at the station are conducted according to guidelines described in the relevant EC Directives and the corresponding national laws defining the specifications that the employed instruments have to meet, as well as the procedures that must be followed for operating and maintaining them (cf. Directive 2008/50/EC, 2008 for more details). The Nicosia Cyprus Atmospheric Observatory (CAO) station is located at the campus of the Cyprus Institute (CyI) next to the Athalassa forestry park in Nicosia, and can be considered an urban background station (referred to as UBS from now on; cf. Figure 1 ). The area around the UBS station is sparsely populated with no significant local pollution sources in its vicinity (i.e., low traffic density, industry, commercial centres, restaurants, etc.). The site is sporadically influenced by minor local traffic due to the trans-pass of a small number of vehicles through the CyI campus. The concentrations of different gaseous pollutants and PM are continuously monitored at this station for research purposes. 2.2 Instrumentation The reference instruments used at the two stations are standard analysers for measuring the concentration of gaseous pollutants, and the TEOM Model 1405-DF for the PM measurements (cf. Table 1 ). The specifications of the instruments used at the TRS and UBS are provided in Tables S1 and S2 of the supplement, respectively. At the TRS, zero and span checks are performed daily using station gas and zero-air generator to monitor analyser drift. All reference gas analysers are calibrated monthly using high-concentration certified transfer gas, in accordance with manufacturer guidelines and EN standards. O 3 analysers are calibrated every three months at the National Reference Laboratory while O 3 span and zero checks are carried out daily. The measurements are validated and reported by DLI at one-hour time resolution. At the UBS, span and zero checks are performed weekly for all reference gas analysers using certified high-concentration gas cylinders and zero-air generator, in accordance with guidelines provided by the manufacturers and European (EN) standards. Calibration of gas analysers is performed monthly. The O₃ analysers are calibrated every three months at the National Reference Laboratory. O 3 span and zero checks are performed daily. Gas concentrations from the reference analysers, expressed in ppb, are recorded every 5 minutes, while PM measurements are taken every 6 minutes. Table 1 Instruments operated at the Nicosia traffic (TRS) and CAO (UBS) air quality monitoring stations, which are employed in this study for the reference measurements. The limit of detection of each instrument is also provided. Model Limit of Detection TRS UBS TRS UBS CO Ecotech Serinus 30 Teledyne Model T300 40 ppb < 40 ppb NO x Ecotech Serinus 40 Teledyne Model T500U 0.4 ppb < 0.04 ppb O 3 Thermo Scientific 49i Teledyne Model T400 0.5 ppb < 0.4 ppb with 80 Sample Digital Filter PM TEOM Model: 1405-DF TEOM Model: 1405-DF < 5µg/m 3 < 5µg/m 3 Two Vaisala Air Quality Transmitter-Monitors (AQT530) series and Weather Transmitters (WXT530) series are employed in this study. These monitors include four Alphasense B-series EC sensors for trace gas measurements (i.e., CO, NO 2 , NO, and O 3 ), and a particle counter (Model LPC200) that measures aerosol mass concentrations in two size fractions (i.e., mass concentrations of particles smaller than 2.5 and 10 µm; PM 2.5 and PM 10 , respectively). The AQ monitors also have a built-in Vaisala HUMICAP® humidity and temperature probe (Model HMP110). The WXT530 transmitter provides measurements of the wind direction and speed, rainfall, temperature, and absolute pressure. The signals from the gas sensors (reported in mV) used in the AQT530 monitors are converted to concentrations (in ppb) using calibration algorithms developed by Vaisala, which are different to the ones provided by Alphasense. We should note here that during the course of the measurements, firmware updates that included new calibration models for the NO 2 and O 3 LCSs became available by Vaisala. More specifically, the AQ monitors were updated from firmware 3.4 to 3.5 on 25 August 2022, in order to improve the measurements reported by the NO 2 sensors, and to firmware 3.6 on 26 January 2023, to improve the measurements reported by the O 3 sensors. The comparison of the measurements with the two firmware is discussed further in sections 2.3 and 3.5. Prior to installation at the two stations, the AQ monitors were co-located at the TRS station for one week (from 5 to 11 November 2021) to conduct an inter-comparison, testing differences between the sensors while measuring the same concentrations of gases and particles. Both AQ monitors were placed outdoors at a distance of a few cm from each other and 1 m from the inlet of the reference instrumentation. Following this period, one monitor was relocated to the UBS station, while the other remained at the TRS station. Both Vaisala AQ monitors, referred to as VSL TRS (at TRS) and VSL UBS (at UBS), collected data from 2 December 2021 to 22 June 2023. Over this 19-month period, the sensors provided continuous time series of measurements, with only minor interruptions. 2.3 Data processing and analysis Negative values reported by all sensors in the AQ monitors were flagged and removed as suggested by the manufacturer. All measurements were then averaged over a period of an hour for comparison with the reference measurements. To determine the impact of temperature and RH variabilities on the performance of the sensors we divided the whole dataset into different temperature (i.e., 30°C) and RH (i.e. 75%) ranges, following the same procedure described by Papaconstantinou et al. ( 2023 ). The performance of the sensors was evaluated by directly correlating and comparing the reported concentrations with measurements by the respective reference instruments to determine the associated errors at each sampling station. The parameters used to do so were the coefficient of determination (R 2 ), the Mean Bias Error (MBE), the Mean Relative Error (MRE) and the Mean Absolute Error (MAE), defined respectively as follows: $$\:{\text{R}}^{2}=1-\:\frac{{\sum\:_{\text{i}=1}^{\text{N}}({\text{C}}_{V,i}-\:{C}_{ref,i})}^{2}}{{\sum\:_{\text{i}=1}^{\text{N}}({\text{C}}_{V,i}-\:\stackrel{-}{{C}_{ref}})}^{2}}\:,$$ 1 $$\:\text{M}\text{R}\text{E}=\:\frac{1}{\text{N}}\sum\:_{\text{i}=1}^{\text{N}}\left(\frac{{(\text{C}}_{V,i}-{\text{C}}_{ref,i}}{{\text{C}}_{ref,i}}\right)\times\:\:100,$$ 2 $$\:\text{M}\text{B}\text{E}=\frac{1}{\text{N}}\sum\:_{\text{i}=1}^{\text{N}}{\text{C}}_{V,i}-{\text{C}}_{ref,i},\:\text{a}\text{n}\text{d}\:$$ 3 $$\:\text{M}\text{A}\text{E}=\frac{1}{\text{N}}\sum\:_{i=1}^{\text{N}}\left|{\text{C}}_{V,i}-{\text{C}}_{ref,i}\right|.$$ 4 In all the equations listed above, N is the total number of data points, whereas \(\:{\text{C}}_{V,i}\) and \(\:{\text{C}}_{ref,i}\) are the concentrations (expressed in ppb) measured respectively by the sensors employed in the AQ monitors and the reference instruments at time \(\:i\) . To investigate whether the differences between LCS measurements at the two different stations or LCS and reference measurements at the same station are statistically significant, we used the Wilcoxon rank-sum (WRS) test (see details in Section S.4 in the Supplement). Differences that are not statistically significant (i.e., when p > 0.05) indicate that the data are samples from continuous distributions with equal medians. The measurements from the NO 2 and O 3 LCSs were also divided into two periods, corresponding to the upgrades of their firmware. The performance of the sensors for each period (namely before and after their firmware update) is assessed with target diagrams, provided in section 3.5, where the vector distance from their origin shows the level of bias and variance of each sensor against reference measurements. The vector is the root mean square error (RMSE) calculated as: $$\:{\left(\frac{RMSE}{{\sigma\:}_{ref}}\right)}^{2}=\:{\left(\frac{MBE}{{\sigma\:}_{ref}}\right)}^{2}+\:{\left(\frac{CRMSE}{{\sigma\:}_{ref}}\right)}^{2},$$ 5 where \(\:CRMSE\) is the centred root mean square error, which is the RMSE corrected for bias, and MBE is the mean bias error. All parameters were normalized by the standard deviation of the reference measurements, \(\:\:{\sigma\:}_{ref}\) . The horizontal line passing through the centre of the target diagram corresponds to zero bias, with the points above or below it corresponding respectively to overestimations or underestimations compared to the reference measurements. When the standard deviation of the LCS measurements is higher or lower compared to those reported by the reference instruments, the points fall on the right or left quadrant of the target diagram, respectively. 3 RESULTS AND DISCUSSION 3.1 Collocated measurements and overall performance of Vaisala AQTs Table 2 shows the results from the one-week period we co-located the LCSs with reference instruments at the TRS where we tested the AQ monitors against each other and against the reference instruments. When comparing the CO, NO and PM 10 measurements reported by the LCSs employed in the two Vaisala AQT530 monitors against each other, we observed the highest agreement (i.e., MRE less than ca. 10%), and correlations (R 2 values being greater than 0.99). The rest of the sensors (i.e. NO 2 , O 3 and PM 2.5 ) exhibit good to moderate unit-to-unit agreement (MRE up to ca. 50%), and inter-correlations with R 2 values ranging from 0.72 to 0.93. Correlation plots, including linear fits, between the measurements reported by all the sensors of the two AQT530 monitors are provided in Fig. 2 . When compared against reference measurements, the CO sensors employed in both AQ monitors exhibit good agreement (MAE 0.96), followed by the PM 10 , NO 2 , NO, O 3 and PM 2.5 as shown in Table 2 (cf. Fig. S2 for the respective correlation plots). Apart from the NO LCSs, which report measurements exhibiting significantly higher mean absolute differences between the two sensors in the unit-to-unit comparison and from the reference instrument, the rest exhibit differences close to those reported by the manufacturer (AQT530, 2023 ). Overall, the results presented in Fig. 2 and Table 2 , build confidence that the measurements from the LCSs of the two AQ monitors mentioned above are consistent, and that they provide measurements that correlate well enough with the reference instruments. To go a step further and investigate whether the unit-to-unit differences during the co-location week are statistically significant, we used the WRS test. This allows us to further evaluate if the sensors produce comparable/reproducible results that can be used to determine spatial variability. The results of these tests show that the differences of the NO, O 3 , and PM 2.5 measurements reported by the two AQ monitors were statistically significant, indicating that the reproducibility of the sensors, averaged over the entire measurement period of the co-located measurements, is poor. In contrast, the respective differences of the CO, NO 2 , and PM 10 measurements were not statistically significant. Among those, however, only the CO and PM 10 measurements exhibit a high enough inter-correlation, with R 2 values being greater than 0.99 (cf. Figure 2 ), suggesting a good sensor-to-sensor reproducibility for every measurement. Considering that, only the CO and PM 10 sensors were tested for their ability to capture spatial differences between different stations (cf. section 3.2 below). Table 2 Correlation and mean absolute differences between the measurements reported by the LCSs in the two AQ monitors and between each LCS with the respective reference instrument during the period we co-located them with reference instruments at the TRS. Correlation between LCS measurements (R 2 ) Mean Absolut Difference between LCS measurements Median ± standard deviation Median ± standard deviation of LCS residuals (VSL TRS - VSL UBS ) Correlation between LCS & reference measurements (R 2 ) Mean Absolute Error between LCS & reference measurements VSL TRS VSL UBS VSL TRS VSL UBS VSL TRS VSL UBS CO 1.00 36.06 ppb 239.25 ± 393.62 238.79 ± 388.54 32.79 ± 17.90 0.96 0.97 143.27 ppb 172.68 ppb NO 2 0.72 11.55 ppb 25.83 ± 15.70 26.83 ± 17.03 -1.50 ± 9.10 0.66 0.70 13.48 ppb 9.60 ppb NO 1.00 22.11 ppb 93.33 ± 54.20 103.37 ± 65.43 -10.25 ± 5.34 0.60 0.70 67.48 ppb 75.86 ppb O 3 0.83 12.28 ppb 11.50 ± 13.10 5.67 ± 12.30 2.00 ± 5.32 0.51 0.63 8.38 ppb 8.18 ppb PM 2.5 0.93 2.82 µg/m 3 5.72 ± 3.36 8.12 ± 4.81 -2.34 ± 1.70 0.44 0.61 12.70 µg/m 3 9.98 µg/m 3 PM 10 0.99 2.46 µg/m 3 26.56 ± 27.17 28.68 ± 26.05 -1.60 ± 2.78 0.67 0.71 19.78 µg/m 3 18.11 µg/m 3 Following the co-located measurements described above, one of the AQ monitors was installed at the UBS while the other remained at the TRS, and both systems were allowed to collect data over a period of 19 months, as described in section 2.2. Figure 3 shows the correlation between the measurements recorded by the monitor employed at the TRS (Figs. 3 a-f) and UBS (Figs. 3 g-l) against the respective measurements by the reference instruments. All the data points are colour coded based on the season of measurements, with blue dots corresponding to the cold and red to the warm seasons (cf. Figs. S2 and S3 in the supplement that provide the same data in the form of time series). Statistical measures of the differences between the VSL TRS and the VSL UBS measurements against their respective reference measurements are provided in Table 3 . Overall, the CO measurements from both AQ monitors (cf. Figure 3 a for VSL TRS , and Fig. 3 g for VSL UBS ) exhibit good agreement with the respective reference measurements as reflected by the relatively low MRE values (-50.51% for VSL TRS , and 38.32% for VSL UBS ), and the high correlation with those reported by the respective reference instrument, exhibiting R 2 values of 0.91 for VSL TRS , and 0.70 for VSL UBS . The O 3 LCS measurements (cf. Figures 3 d and 3 j) also exhibit deviations from their respective reference measurements (MRE is 89.22% for VSL TRS and − 7.13% for VSL UBS ), but weaker correlations compared to the CO LCSs (the R 2 for VSL TRS and VSL UBS are 0.18 and 0.16, respectively). The performance of the NO 2 sensors (Fig. 3 b and 3 h) was among the poorest as indicated by the high MREs (224.48% for VSL TRS and 732.98% for VSL UBS ) and the lack of correlation (R 2 value of 0.012 for VSL TRS and of 0.0014 for VSL UBS ). Similarly, the NO sensors (Figs. 3 c and 3 i) exhibit very high MREs (1.17×10 4 % for VSL TRS and 1.03×10 5 % for VSL UBS ), mainly overestimating the reference concentrations, and extremely low correlation (R 2 being in the range of 10 − 2 and 10 − 4 for VSL TRS and VSL UBS , respectively). In general, all gas sensors show weaker correlations during the warm period between June and September, corroborating previous findings reported by our group (Papaconstantinou et al. 2023 ). Apart from the relative errors and correlations discussed above, it is important to investigate whether the month-to-month trends captured by the LCS measurements are similar to those from the reference instruments. Overall, the CO and PM sensors captured the month-to-month trend over the entire period of the measurements similarly to the reference instruments. In contrast, however, the measurements by the O 3 and NO LCSs, as well as part of those by the NO 2 LCSs (measurements corresponding to the first half of the study period), exhibited different overall trends and significant deviations against reference measurements, particularly during the summer periods (cf. Fig. S2 in the supplement). This difference in the temporal trends indicates that the performance of these sensors is affected more strongly by the high temperature and RH conditions compared to the CO LCSs, warranting further investigation and efforts to improve their performance. The PM concentrations reported by the VSL TRS and VSL UBS AQ monitors are lower than the reference measurements, as reflected by the negative MRE values shown in Table 3 . Compared to PM 10 , the PM 2.5 measurements reported by the AQ monitors (Figs. 3 e and 3 k) exhibit higher deviations against those provided by the reference instruments in both stations (Figs. 3 f and 3 l). This is because the PM LCSs have a high cut-off diameter (50% detection efficiency for particle sizes of 0.6 µm; Vaisala, 2022), resulting in a portion of particles to go undetected. These undetected particles contribute proportionally more to the PM 2.5 mass than to the PM 10 mass. Overall, the PM concentrations reported by the VSL TRS monitor show greater discrepancies compared to those from the VSL UBS monitor when measured against the respective reference values (cf. Table 3 ). Despite that only the PM 10 measurements showed good sensor-to-sensor reproducibility when the two AQ monitors were collocated at the TRS in the beginning of our study period (cf. Table 2 ), the difference in the PM measurements between the two stations, as observed here, can be attributed to the higher fraction of particles smaller than the cut-off diameter of the PM sensors at the TRS compared to the UBS. This is highly possible considering that fresh and smaller particles, which are characteristic of traffic emissions (Zhu, 2002), should be higher at the TRS. We should highlight here that the agreement between PM LCSs and reference instruments improved significantly during periods with dust events. More specifically, the MRE between the PM 2.5 and PM 10 measurements reported by the LCSs decreased respectively from − 74.16 to -59.98% and from − 64.26 to -55.48% at the TRS, and from − 57.28 to -27.75% and from − 43.98 to -28.78% at the UBS. Similarly, the R 2 values of PM 2.5 measurements increased from 0.12 during the non-dust period (i.e., summer and winter) to 0.43 during the dust period (i.e., spring and autumn) for VSL TRS , and from 0.18 to 0.47 for VSL UBS , respectively (see Fig. S4 in the supplement). The respective R 2 values for PM 10 measurements increased from 0.32 to 0.78 for VSL TRS and from 0.47 to 0.92 for VSL UBS, respectively (cf. Fig. S5 in the supplement). Another reason contributing to the improved performance of the PM sensors during the dust events is the similarity in the optical properties of the micron-sized dust particles to those of calibration aerosol particles (AQT530, 2023 ). Table 3 Summary statistics, including the number of useful measurements (N) recorded by the Vaisala AQ monitors, and the mean value of hourly averaged concentrations measured by the respective LCSs and the reference instruments for the entire study period, together with the Standard Deviation (SD) for each case, as well as the associated values of the MRE, MBE, MAE and R 2 . The Limit of Detection (LoD) values for each LCS employed in the Vaisala monitors are also provided. Pollutant Vaisala N Vaisala Mean ± SD Reference Mean ± SD MRE (%) MBE MAE R 2 Vaisala LoD CO (ppb) VSL TRS 13216 190.16 ± 226.41 357.89 ± 292.26 -50.51 -165.44 169.47 0.91 10 ppb VSL UBS 9963 129.84 ± 136.50 235.53 ± 169.25 38.32 -66.44 85.35 0.70 NO 2 (ppb) VSL TRS 13216 32.35 ± 29.23 14.82 ± 9.80 224.48 17.16 18.57 0.012 5 ppb VSL UBS 12325 27.24 ± 24.47 8.07 ± 9.01 732.98 18.87 19.46 0.0014 NO (ppb) VSL TRS 13216 111.05 ± 102.63 11.72 ± 22.71 1.17×10 4 98.33 98.62 5.64×10 − 4 5 ppb VSL UBS 12354 86.87 ± 84.88 3.30 ± 11.50 1.03×10 5 60.35 60.43 0.0064 O 3 (ppb) VSL TRS 13216 40.69 ± 47.20 28.34 ± 16.85 89.22 11.94 23.53 0.18 5 ppb VSL UBS 10453 31.23 ± 26.06 36.18 ± 16.20 -7.13 -5.19 15.54 0.16 PM 2.5 (µg/m 3 ) VSL TRS 13216 5.43 ± 6.34 19.17 ± 12.32 -62.71 -13.43 13.66 0.15 0.1 µg/m 3 VSL UBS 13258 7.05 ± 8.87 13.29 ± 9.07 -36.84 -5.64 7.47 0.22 PM 10 (µg/m 3 ) VSL TRS 13216 15.62 ± 19.86 38.78 ± 26.20 -58.99 -22.65 23.24 0.44 0.1 µg/m 3 VSL UBS 13258 18.05 ± 27.99 25.82 ± 20.10 -29.51 -6.75 10.54 0.56 As shown in Fig. 3 , the measurements of the low-cost gas sensors are in better agreement with those of the reference instruments during the cold periods (i.e., from September to May when RH is higher than 55%) than during the warm periods (i.e., from June to August when RH is decreased below 55%); cf. Figs. S6 and S8 for the dependence of the LCSs on temperature and Figs. S10 and S12 on the RH. Some of the LCSs (particularly the NO 2 , NO and O 3 sensors) provide measurements that deviate substantially from those of the reference instruments over the warm periods. We have observed similar behaviour in previous measurements with LCSs, and have attributed it to the evaporation of water in the electrolyte of the sensors (Papaconstantinou et al., 2023 ; Alphasense AAN106). We should note here that gas sensors used in the Vaisala AQ monitors exhibit better performance compared to the sensors tested in our previous work, which, in contrast to this study, did not fully recover after being exposed to moderate temperature and RH conditions. Although the sensors were of the same model and manufacturer in both studies, this difference can be attributed to the different production batches or the type/design of electrolyte enclosure used. The measurements of the PM LCSs are in better agreement with those of reference instruments at RH conditions below 30% (cf. Figs. S11 and S13 in the supplement for VSL TRS and VSL UBS , respectively). This is because the size and refractive index of the particles change due to water uptake at higher RH conditions, increasing significantly their scattering efficiency, and causing deviations from the reference measurements that are carried out at dry conditions (Titos et al., 2016 ; Bezantakos et al., 2021 ). Since typically low RH conditions are associated with high temperatures, PM LCS measurements tend to correlate better with reference measurements at higher temperatures. At both stations, agreement between the Vaisala PM 10 with the reference measurements is better, compared to that of PM 2.5 for all temperature and RH conditions due to the reasons described above. 3.2 Spatial variabilities determined by the reference instruments and the LCSs The results shown in Table 3 indicate that the reference concentrations (averaged over the entire study period) of all the pollutants at the two stations are different, with deviations ranging from ca. 25 to 110%. For example, the mean reference CO concentration at the TRS is 357.9 ppb while the respective value at the UBS is 235.5 ppb. To investigate whether these differences are statistically significant both when the values are averaged over the entire measurement period and on a monthly basis, we used the WRS test. The results of this test show that the differences for all pollutants measured by the reference instruments are significant at a 95% confidence interval for the average values over the entire measurement period and for each individual month (cf. Tables S3-S6 in the supplement for more details). These differences are expected, as we are comparing two different types of stations: a traffic and an urban background station, which naturally experience varying pollutant levels due to their differing environments. By identifying whether the LCSs are also able to capture these spatial differences, one can in principle determine if they can be reliably used for spatial hot-spot recognition. The same statistical test was used for the measurements by the CO and PM 10 LCSs of the AQ monitors operated at the two stations, which were the ones exhibiting high sensor-to-sensor reproducibility during the co-location period as discussed in section 3.1. For the entire study period (i.e., all 19 months), the mean concentrations determined by the LCSs in the two stations are statistically different at the same confidence interval (i.e., 95%; cf. Table S3), as also indicated by comparing the respective reference instruments at the two stations. However, when the same test is applied for every individual month, there are a few cases where the LCSs show no statistically significant differences between the two stations while the reference instruments do (see orange-coloured cells in Tables S4-S5). Figure 4 shows time series of the monthly-average concentrations of the pollutants as those were determined by the measurements from the reference instruments (cf. solid lines) and the AQT sensors (cf. dashed lines), as well as the concentration differences between reference and LCSs measurements. The CO sensors seem to report measurements that follow the overall concentration variability as this is captured by the respective reference instrument. The PM 10 measurements reported by the AQ monitors generally follow the trend of the reference measurements. The striking peak observed in April 2022 is due to a strong dust event as discussed above; a phenomenon that occurs with higher frequency and intensity during this period of the year in the region (Yukhymchuk et al. 2022, Kezoudi et al. 2021 ). PM 10 concentrations, however, are generally higher at the TRS compared to the UBS, as indicated by the reference instruments (Fig. 3 g), which is in contrast to what the LCS measurements indicate (Fig. 3 h). This discrepancy could be explained by the systematic presence of particles smaller than the detection limit of the low-cost PM sensor (i.e., 600 nm) employed in the Vaisala monitor at the TRS compared to the UBS. This is a plausible explanation considering that TRS is affected to a higher degree by particles from anthropogenic sources that are typically smaller than the LCS detection threshold. In contrast, background particles, which are more likely to represent the atmospheric aerosol at the UBS, are generally larger and thus the majority (if not all) of those are detected and counted by the low-cost PM sensor in the monitor. Red-shaded areas in Fig. 4 denote the months when the measurements by the LCSs did not exhibit statistically significant differences while the reference instruments did. The respective p-values calculated by the statistical test are tabulated in the supplement (cf. Tables S4 and S5). The CO LCSs show no statistically significant differences in 2 out of the 19 months (July and August 2022). During these months, the difference of the CO concentrations between the two stations as determined by the LCSs and the reference instruments was below 4 and 80 ppb, respectively (cf. Figure 4 b). The PM 10 measurements reported by the LCSs show no statistically significant differences in 7 out of the 19 months (i.e., December 2021, February, March and November 2022 and March, April and May 2023; Fig. 4 e). The PM concentration differences between the two stations in these cases, as measured by the LCSs, were below 3–4 µg/m 3 while those measured by the reference instruments were below 6–10 µg/m 3 . The ability of the LCSs to capture spatial variabilities is important for their use for the identification of pollution hot-spots. With the exemption of the two warm months, the CO LCSs can be used for capturing the reference month-to-month trends/variabilities, urban gradients and pollution hot spots. Figure 5 shows the averaged diurnal trends/variabilities on workdays and weekends for CO and PM 10 during the two months of January in our dataset (i.e., for 2022 and 2023) when the mean hourly temperatures ranged from 0.2 to 20.7°C, and for the two months of June, when the respective values ranged from 15.6 to 37.7°C; note that the diurnal variations observed in January 2022 and January 2023, as well as in June 2022 and June 2023, exhibited a high degree of similarity (cf. Figs. S14 and S15 in the supplement). Tables S6 and S7 in the supplement provide the p-values from the tests using the data shown in Fig. 5 . The hourly reference measurements at the two stations are significantly different (p < 0.05), with few exemptions mainly for CO during morning and night hours when the concentration difference is insignificant (p ranging from \(\:5.3\times\:{10}^{-02}\) to \(\:9.5\times\:{10}^{-01}\) ). The LCSs in the AQTs are able to capture the differences in the diurnal variation between the two stations better during workdays than weekends, mainly due to the higher concentrations observed during the former (cf. reference measurements also in Fig. 5 ) that can be captured with higher fidelity by the LCSs. More specifically, the CO sensors in the two AQ monitors appear to follow better the diurnal variabilities, as these are captured by the reference instruments, during the cold (January 2023 and January 2023) rather than the warm (June 2022 and June 2023) months. This is not surprising considering that the performance of EC sensors drops at high temperatures and at low concentrations that occur in the warm period (cf. Figure 5 a-b against 5c-d). What is more, they fail to capture the significant difference observed between reference measurements during the middle of the days in the weekends when the CO concentration differences are small between the two stations (< 80 ppb as indicated by the reference instruments), especially during the warm period (i.e., between 10.00 am and 6.00 pm in June; cf. Table S6 in the supplement). The PM 10 LCSs captured well the diurnal trend/variability of the reference measurements during the weekdays of the cold periods (cf. Figure 5 e), but fail to do so during the weekends of the same period (cf. Figure 5 f), most likely due to the lower concentration of particles that are large enough to be detected by these sensors. The PM 10 measurements also fail to capture the trends observed by the respective reference instruments during the warmer months (both during weekdays and weekends; cf. Figures 5 g and 5 h), due to the same reason. Overall, the ability of the PM 10 measurements by the LCSs to capture the spatial differences was higher in cases where the difference in the concentration at the two stations was higher (i.e., > 10 µg/m 3 ). It should also be noted that these differences were in the opposite direction compared to that when comparing the measurements reported by the references instruments: i.e., the LCSs concentrations at UBS are systematically higher than those at the TRS, while the opposite is true for the reference measurements. As explained earlier in the same section, this is due to less sub-600 nm particles at the UBS compared to TRS station. 3.3 Temporal variabilities determined by the reference instruments and the LCSs at the same station The next step was to examine whether the LCSs can capture the temporal variabilities as those are determined by the reference instruments at each station. To do so, we carried out WRS tests between successive months for the reference and the LCS measurements at each of the two stations. Table 4 shows the calculated p-values for each test, and whether this indicates significant difference (p < 0.05). When the reference month-to-month concentration differences are statistically significant, the LCSs can also reproduce this result for most of the cases (indicated by the white cells corresponding either to REF TRS , REF UBS , VSL TRS , or VSL UBS in Table 4). In contrast, when the month-to-month variability of the reference measurements is not statistically significant (blue and orange cells for the measurements at TRS and UBS, respectively), it is more likely for the respective LCSs to suggest the opposite: i.e., that the differences are significant. This can be attributed to the low accuracy of the LCSs compared to the reference instruments. The CO LCS measurements at the two stations identified accurately the statistically significant differences in ca. 55–60% of the cases (11 and 10 out of the 18 sets of consecutive months at TRS and UBS, respectively). The respective numbers for the NO 2 and NO LCSs were ca. 60 and 70% for TRS and ca. 70 and 45% for UBS. Regarding O 3 , the LCSs captured the statistically significant or insignificant variabilities in ca. 65% of the cases at both stations. The PM 2.5 and PM 10 LCSs captured the statistical significance (or non-significance) of the month-to-month variability in ca. 80 and 70%, respectively, at both stations. Considering that the PM sensors, and marginally the NO and NO 2 sensors, capture accurately the month-to-month variabilities in more than 70% of the cases, they can qualify as appropriate to determine the seasonal variabilities. However, taking into account that the NO and NO 2 sensors fail to capture the overall trends, reporting values that are significantly different compared to the reference instruments, they can be excluded, leaving only the PM sensors as the most reliable indicator of temporal variabilities. Table 4: P-values determined by the WSR tests between all pairs of consecutive months of measurements using either the reference or the LCS measurements. Blue (for TRS) and orange (for UBS) cells indicate that the month-to-month differences are not statistically significant (p > 0.05), whereas white cells indicate the opposite (p < 0.05). Colour agreement between the reference and LCS measurements for each pair of consecutive months indicate that the AQT sensors are able to capture the temporal variabilities. 3.4 Comparison of the performance of NO 2 and O 3 sensors before and after firmware update As discussed in Section 2.2, the firmware for the NO 2 and O 3 sensors changed during the course of the measurements (on 25/08/2022 for the NO 2 and on 26/01/2023 for the O 3 sensors), providing an opportunity to test the improvement to the measurements. The target diagrams provided in Fig. 6 show the performance of these sensors (Fig. 6 a for NO 2 and Fig. 6 b for O 3 ) against measurements by the respective reference instruments before and after the firmware updates. The distance of each point from the centre of the circle corresponds to the normalized, by the standard deviation of the reference measurements, RMSE (nRMSE) described in Section 2.3. As shown in the target diagrams, the performance of the Vaisala AQT530 NO 2 and O 3 sensors at both stations was improved after the firmware updates, as indicated by the lower nRMSE values. More specifically, the magnitude of the nRMSE vector decreased by 63.5% (VSL TRS ) and 73.4% (VSL UBS ) for the NO 2 sensors, and by 57.9% (VSL TRS ) and 27.5% (VSL UBS ) for the O 3 sensors following their firmware updates. Regarding the MBE, there was an improvement of 68.0% (VSL TRS ) and 70.2% (VSL UBS ) for the NO 2 sensors, and of 58.6% (VSL TRS ) and 356.3% (VSL UBS ) for the O 3 sensors. The CRMSE also improved by 57.0% (VSL TRS ) and 60.6% (VSL UBS ) for the NO 2 sensors and 61.19% (VSL TRS ) and 71.6% (VSL UBS ) for the O 3 sensors. Determining whether this overall performance improvement enables the sensors to capture the spatial and temporal differences discussed above, would require a more extended study in which the new firmware is used for at least 12 months with a new set of sensors, considering their limited lifespan. 4 CONCLUSIONS We have carried out air quality monitoring measurements using two Vaisala AQT530 monitors and reference instruments at a traffic and an urban background station in Nicosia, Cyprus, and investigated if the LCSs employed in the former can capture the spatial and temporal variabilities similarly to the latter. Initial measurements where both AQ monitors were collocated with reference instruments at the traffic station showed that only the CO and PM 10 measurements exhibit a high enough correlation and agreement with the reference measurements, suggesting a good sensor-to-sensor reproducibility. Considering that, only these sensors were tested further for their ability to capture spatial differences between the two stations. Analysis of the reference measurements shows that the mean concentrations of the pollutants at the two stations, over the entire study period and for each month separately, were statistically significantly different at a 95% confidence interval. On an hourly basis, the reference measurements also showed that there are statistically significant differences for certain hours of the day at the two stations. The respective low-cost measurements for CO and PM 10 were able to capture the significance of the spatial gradiences between the two stations for the entire study period and on monthly basis, with the exceptions of a few months depending on the sensor. On daily (workdays or weekends) or hourly basis, the ability of the LCS CO and PM 10 measurements to capture the spatial differences was higher in cases where the difference in the concentration of the pollutants at the two stations was higher (i.e., > 80 ppb for CO and > 10 µg/m 3 for PM 10 ). Regarding the temporal (i.e., monthly) variations, the CO and PM LCSs captured the month-to-month reference trend over the entire period, while the NO 2 , NO and O 3 sensors did not, mainly due to their sensitivity on the environmental conditions. PM and marginally the NO and NO 2 LCSs capture the reference month-to-month differences in more than 70% of the cases. However, considering that the latter fail to reproduce the overall trends, reporting values that are significantly different compared to the reference instruments, they can be excluded, leaving only the PM sensor as the most reliable indicator of temporal variabilities at the same station. Overall, among all LCSs, the ones measuring PM appear to capture better than the others both the temporal and spatial resolutions (which is not a surprise given the more robust operating principle compared to the gas sensors) despite their relatively high cut-off diameter, while the CO sensors can be used to capture effectively mainly spatial differences. The CO LCSs managed to capture the monthly and diurnal trends/variabilities regardless of the environmental conditions, while the rest of the low-cost gas sensors appear to report measurements that are comparable to those from the reference instruments only at lower temperatures ( 55%). Declarations Author Contribution R.P. drafted the manuscript, which was reviewed and edited by SB and GB; RP performed the measurements; M.PI., M.PA., M.S. and C.S. provided the reference station data; R.P. analysed the data. The research was conceptualised by S.B., G.B.. The funding was acquired by J.S.. Acknowledgement The authors would like to thank the EMME-CARE project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 856612 and the Cyprus Government. Data Availability The data that support the findings of this study are available in Zenodo:Papaconstantinou, R. (2024). Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13985490 References Alphasense AAN106: Humidity Extremes: Drying Out and Water Absorption, Alphasense Application Note AAN106, https://www. alphasense.com/wp-content/uploads/2013/07/AAN_106.pdf (last access: 16/06/2023), 2013. AQT530, 2023, Air Quality Transmitter AQT530 Datasheet, Available at: https://docs.vaisala.com/v/u/B211817EN-F/en-US, Last access: 10/08/2023. Regional Agency for the Protection of the Environment of Lazio (ARPA Lazio), Regional Emission Inventory – 2015 Emissions in the Lazio Region, [In Italian], Available at: http://www.arpalazio.gov.it/ambiente/aria/inventario.htm, Last accessed: 31/08/2020. Baruah, A., Zivan, O., Bigi, A., Ghermandi, G.: Evaluation of low- cost gas sensors to quantify intra-urban variability of atmospheric pollutaTRS, Environ. Sci.: Atmos, 3, 830, https://doi.org/10.1039/d2ea00165a, 2023. Berlin Air Quality Monitoring Network (Berliner Luftgütemessnetz – BLUME), AIR QUALITY PLAN FOR BERLIN – 2ND UPDATE, Available at: https://www.berlin.de/sen/uvk/_assets/umwelt/luft/luftreinhaltung/luftreinhalteplan-2-fortschreibung/luftreinhalteplan_2019_en.pdf?ts=1666617271, 2019. Bezantakos, S., Costi, M., Barmpounis , K., Antoniou, P., Vouterakos, P., Keleshis, C., Sciare, J., Biskos, G., Qualification of the Alphasense optical particle counter for inline air quality monitoring, Aerosol Sci. Technol., 55, 361– 370, https://doi.org/10.1080/02786826.2020.1864276, 2021. Bílek, J., Bílek, O., Maršolek, P. and Bucek, P.: Ambient Air Quality Measurement with Low-Cost Optical and EC Sensors: An Evaluation of Continuous Year-Long Operation, J. Environ., 8(11), 114, https://doi.org/10.3390/environments8110114, 2021. Bisignano, A., Carotenuto, F., Zaldei, A., Giovannini, L.: Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway, Atm. Env., 275, 119008, https://doi.org/10.1016/j.atmosenv.2022.119008, 2022. Borrego, C., Costa, A. M., Ginja, J., Amorim, M., Coutinho, M., Karatzas, K., Sioumis, Th, et al.: Assessment of air quality microsensors versus reference methods: the EuNetAir joint exercise, J. Atmos. Environ., 147 (2), 246–263, https://doi.org/10.1016/j.atmosenv.2016.09.050, 2016. Bulot, F.M.J., Johnston, S.J., Basford, P.J., Easton, N.H.C., Apetroaie-Cristea, M., Foster, G.L., Morris, A.K.R., Cox, S.J., Loxham, M.: Long-term feld comparison of multiple low-cost particulate matter sensors in an outdoor urban environment, Sci. Rep., 9(1):7497, https://doi.org/10.1038/s41598-019-43716-3, 2019. Cabaneros, S.M., Calautit, J.K., Hughes, B.R.: A review of artificial neural network models for ambient air pollution prediction, Environ. Model. Softw., 119, 285-304, https://doi.org/10.1016/j.envsoft.2019.06.014, 2019. Carslaw, K.S.: Aerosols and Climate, Elsevier, 1 st Edition, ISBN: 9780128231722, Published: August 19, 2022. Castell, N., Dauge, F.R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., Bartonova, A.: Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293-302, http://dx.doi.org/10.1016/j.envint.2016.12.007, 2017. Collier-Oxandale, A., Feenstra, B., Papapostolou, V., Zhang, H., Kuang, M., Der Boghossian, B., Polidori, A.: Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program, J. Atmos. Env., 220, https://doi.org/10.1016/j.atmosenv.2019.117092, 2020. Crawford, B., Hagan, D.H., Grossman, I., Cole, E., Holland, L., Heald, C.L., Kroll, J.H.: Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kılauea eruption) using a low-cost sensor network, Proceedings of the National Academy of Sciences of the United States of America, 118(27), https://doi.org/10.1073/pnas.2025540118, 2021. Cui, H., Zhang, L., Li, W., Yuan, Z., Wu, M., Wang, C., Ma, J., Li, Y.: A new calibration system for low-cost Sensor Network in air pollution monitoring, Atmos. Pollut. Res., 12, 101049, https://doi.org/10.1016/j.apr.2021.03.012, 2021. Department of Labour Inspection (DLI), Annual Air Quality Technical Report 2021, Available at: https://www.airquality.dli.mlsi.gov.cy//sites/default/files/2022-11/Etisia%20Techniki%20Ekthesi%202021.pdf, Last accessed: 23/08/2023. deSouza, P., Kahn, R., Stockman, T., Obermann, W., Crawford, B., Wang, A., Crooks, J., Li, J., Kinney, P.: Calibrating networks of low-cost air quality sensors, Atmos. Meas. Tech., 15, 6309–6328, https://doi.org/10.5194/amt-15-6309-2022, 2022. European Parliament and Council of the European Union, 2008. Directive 2008/50/EC of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal of the European Union, L 152, pp.1-44. Available at: http://data.europa.eu/eli/dir/2008/50/oj [Accessed 04/09/2024]. Feinberg, S.N., Williams, R., Hagler, G., Low, J., Smith, L., Brown, R., Garver, D., Davis, M., Morton, M., Schaefer, J., Campbell, J.: Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors, Atm. Env., 213, 579-584, https://doi.org/10.1016/j.atmosenv.2019.06.026, 2019. Frederickson, L.B., Sidaraviciute, R., Schmidt, J.A., Hertel, O., Johnson, M.S.: Are dense networks of low-cost nodes really useful for monitoring air pollution? A case study in Staffordshire, Atmos. Chem. Phys., 22, 13949-13965, https://doi.org/10.5194/acp-22-13949-2022, 2022. Gao, M., Cao, J., Seto, E.: A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM 2.5 in Xi'an, China, Environ. Pollut., 199, 56-65, http://dx.doi.org/10.1016/j.envpol.2015.01.013, 2015. He, Q., Wang, B., Liang, J., Liu, J., Liang, B., Li, G., Long, Y., Zhang, G., Liu, H.: Research on the construction of portable electrochemical sensors for environmental compounds quality monitoring, Mater. Today Adv., 17, 100340, https://doi.org/10.1016/j.mtadv.2022.100340, 2023. Heimann, I., Bright, V.B., McLeod, M.W., Mead, M.I., Popoola, O.A.M., Stewart, G.B., Jones, R.L.: Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors, Atmos. Environ., 113, 10-19, http://dx.doi.org/10.1016/j.atmosenv.2015.04.057, 2015. Juginovic, A., Vukovic, M., Aranza, I., Bilos, V.: Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries, J. Sci. Rep., 11, https://www.nature.com/articles/s41598-021-01802-5, 2021. Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506, 2019. Kezoudi, M., Tesche, M., Smith, H., Tsekeri, A., Baars, H., Dollner, M., Estellés, V., Bühl, J., Weinzierl, B., Ulanowski, Z., Müller, D., Amiridis, V.: Measurement report: Balloon-borne in situ profiling of Saharan dust over Cyprus with the UCASS optical particle counter, Atmos. Chem. Phys., 21, 6781-6797, https://doi.org/10.5194/acp-21-6781-2021, 2021. Kim, J., Shusterman, A.A., Lieschke, K.J., Newman, C., Cohen, R.C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atms. Meas. Tech., 11, 1937-1946, https://doi.org/10.5194/amt-11-1937-2018, 2018. Kuntic, M., Kuntic, I., Hadad, O., Lelieveld, J., Münzel, T., Daiber, A.: Impact of air pollution on cardiovascular aging, Mech. Ageing Dev., 111857, https://doi.org/10.1016/j.mad.2023.111857, 2023. Lewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller, S. J., Smith, K. R., Buckley, J. W., Ellis, M., Gillot, S. R., and White, A.: Evaluating the performance of low cost chemical sensors for air pollution research, J. Faraday Discuss., 189, 85-103, https://doi.org/10.1039/c5fd00201j, 2016. London Air, 2018, Environmental Research Group (ERG), Imperial College London, Available at: https://www.londonair.org.uk/london/asp/publicbulletin.asp?la_id=7&MapType=Google, Last accessed: 05/10/2023. Madrid Air Quality portal, Annual air quality report 2022, Available at: https://airedemadrid.madrid.es/UnidadesDescentralizadas/Sostenibilidad/CalidadAire/Publicaciones/Memorias_anuales/Ficheros/MEMORIA_2022_02.pdf, Last accessed: 05/10/2023. Marx, E., and Mulholland, G.: Size and Refractive Index Determination of Single Polystyrene Spheres., J. Res. Nat. Bur. Stand., 321–338, 88, 5, https://doi.org/10.6028/jres.088.016, 1983. McMurry, P.H: A review of atmospheric aerosol measurements, Atmos. Environ., 34, 12-14, 1959-1999, https://doi.org/10.1016/S1352-2310(99)00455-0, 2000. Ministry of Environment and Energy, Annual Air Quality Report 2022, Last accesses: 23/08/2023, Available at: https://ypen.gov.gr/perivallon/poiotita-tis-atmosfairas/ektheseis/# Nagendra, S.S.M., Yasa, P.R., Narayana M.V., Khadirnaikar, S., Rani, P.: Mobile monitoring of air pollution using low cost sensors to visualize spatiotemporal variation of pollutaTRS at urban hotspots, Sustain. Cities Soc., 44, 520-535, https://doi.org/10.1016/j.scs.2018.10.006, 2019. Pang, X., Shaw, M.D., Lewis, A.C., Carpenter, L.J., Batchellier, T., Electrochemical ozone sensors: A miniaturised alternative for ozone measurements in laboratory experiments and air-quality monitoring, Sens. Actuators B Chem., 240, 829-837, https://doi.org/10.1016/j.snb.2016.09.020, 2017. Papaconstantinou, R., Demosthenous, M., Bezantakos, S., Hadjigeorgiou, N., Costi, M., Stylianou, M., Symeou, E., Savvides, C., Biskos, G., Field Evaluation of Low-cost Electrochemical Air Quality Gas Sensors at Extreme Temperature and Relative Humidity Conditions, Atm. Meas. Tech., 16, 3313–3329, https://doi.org/10.5194/amt-16-3313-2023, 2023. Petäjä, T., Ovaska, A., Fung, P.L., Poutanen, P., Yli-Ojanperä, J., Suikkola, J., Laakso, M., Mäkelä, T., Niemi, J.V., Keskinen, J., Järvinen, A., Kuula, J., Kurppa, M., Hussein, T., Tarkoma, S., Kulmala, M., Karppinen, A., Manninen, H.E. and Timonen, H.: Added Value of Vaisala AQT530 Sensors as a Part of a Sensor Network for Comprehensive Air Quality Monitoring, Front. Environ. Sci., 9, 719567, https://doi.org/10.3389/fenvs.2021.719567, 2021. Popoola, O.A.M., Carruthers, D., Lad, C., Bright, V.B., Mead, M.I., Stettler, M.E.J., Saffell, J.R., Jones, R.L.: Use of networks of low cost air quality sensors to quantify air quality in urban settings, Atmos. Environ., 194, 58-70, https://doi.org/10.1016/j.atmosenv.2018.09.030, 2018. Raheja, G., Nimo, J., Appoh, E. K.-E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R.E., Penn, S.L., Giordano, M.R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E.N., Borketey, M.A., Hughes, A.F., Westervelt, D.M.: Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM 2.5 Monitoring in Accra, Ghana, Environ. Sci. Technol., 57 (29), 10708-10720, https://doi.org/10.1021/acs.est.2c09264, 2023. Schneider, P., Castell, N., Vogt, M., Dauge, F.R., Lahoz, W.A., Bartonova, A.: Mapping urban air quality in near real-time using observations from low cost sensors and model information, Environ. Int., 106, 234-247, http://dx.doi.org/10.1016/j.envint.2017.05.005, 2017. Spinelle, L., Gerboles, M., & Aleixandre, M.: Performance Evaluation of Amperometric Sensors for the Monitoring of O3 and NO2 in Ambient Air at ppb Level, Procedia Engineering, 120, 480–483. https://doi.org/10.1016/j.proeng.2015.08.676, 2015a. Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide, Sens. Actuat. B, 215, 249–257, https://doi.org/10.1016/j.snb.2015.03.031, 2015b. Stetter, J., Li, Jing: Amperometric gas sensors e a review. Chem. Rev. 108, 2, 352-366. http://dx.doi.org/10.1021/cr0681039, 2008. TEOM 1405-DF, Thermo Fisher Scientific Inc., Last accessed: 08/12/2023, Available at: https://assets.thermofisher.com/TFS-Assets/CAD/Datasheets/1405-df-teom-ambient-particulate-monitor-datasheet.pdf, 2020. Titos, G., Cazorla, A., Zieger, P., Andrews, E., Lyamani, H., Granados-Muñoz, M.J., Olmo, F.J., Alados-Arboledas, L.: Effect of hygroscopic growth on the aerosol light-scattering coefficient: A review of measurements, techniques and error sources, 141, 494-507, https://doi.org/10.1016/j.atmosenv.2016.07.021, 2016. Wei, P. Ning, Z., Sun, L., Yang, F., Wong, K.C., Westerdahl, D., Louie, P.K.K.: Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring, Sensors, 18(2), 59, https://doi.org/10.3390/s18020059, 2018. Yukhymchuk, Y., Milinevsky, G., Syniavskyi, I., Popovici, I., Unga, F., Sciare, J., Marenco, F., Pikridas, M., and Goloub, P.: Atmospheric Aerosol Outbreak over Nicosia, Cyprus, in April 2019: Case Study, 13, 1997, https://doi.org/10.3390/atmos13121997, 2022. Zhu, Y., Hinds, W.C., Kim, S., Shen, S., Sioutas, C.: Study of ultrafine particles near a major highway with heavy-duty diesel traffic, 36, 27, 4323-4335, https://doi.org/10.1016/S1352-2310(02)00354-0, 2002. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5349649","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":378563404,"identity":"e596287a-5d8a-44fc-aaad-279c0d200f1f","order_by":0,"name":"Roubina Papaconstantinou","email":"","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":false,"prefix":"","firstName":"Roubina","middleName":"","lastName":"Papaconstantinou","suffix":""},{"id":378563406,"identity":"cbe0915e-f6ad-44e6-923f-5673019c781e","order_by":1,"name":"S. Bezantakos","email":"","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"","lastName":"Bezantakos","suffix":""},{"id":378563407,"identity":"164e1a4d-5648-4905-90bd-f393e13ee4fd","order_by":2,"name":"M. Pikridas","email":"","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Pikridas","suffix":""},{"id":378563409,"identity":"ace9d1e4-1215-4e6b-bd67-0272fcba5d21","order_by":3,"name":"M. Parolin","email":"","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Parolin","suffix":""},{"id":378563410,"identity":"252cbac8-1327-432c-b3ba-318d6c633e1f","order_by":4,"name":"M. Stylianou","email":"","orcid":"","institution":"Medisell Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Stylianou","suffix":""},{"id":378563411,"identity":"51ae23be-225e-4996-b335-ceb97f413a6a","order_by":5,"name":"C. Savvides","email":"","orcid":"","institution":"Ministry of Labour and Social Insurance, Department of Labour Inspection","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"","lastName":"Savvides","suffix":""},{"id":378563413,"identity":"e10d1325-687c-45e4-95b2-bd067137de01","order_by":6,"name":"J. Sciare","email":"","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"","lastName":"Sciare","suffix":""},{"id":378563414,"identity":"00cf890e-9fed-4db5-9a25-79993760244a","order_by":7,"name":"George Biskos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFACHoYDQMjAD2QeIE2LZAMpWsBqDYhVzyDvwHvw4I8zNvbGx88YHmCosWPgn5GAX4vhAb6Ewzw30hK3nckBWnQsmUHiBiEtDTwGhxk+HE4wu8ED1MJ2gMFAgggtB398OGxvPAOk5R8RWuQZgCp5bhxm3CABZDC2EaHFgBnklzNpiTPOpBUcSOxL5pE484CALe29hz/+OGZjz99+ePOHD9/s5PjbCdlyGM7kMGBIAEWTACG/NMCZ7FAH8R/Ar2UUjIJRMApGHAAAdWRICxIRnVAAAAAASUVORK5CYII=","orcid":"","institution":"The Cyprus Institute","correspondingAuthor":true,"prefix":"","firstName":"George","middleName":"","lastName":"Biskos","suffix":""}],"badges":[],"createdAt":"2024-10-28 20:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5349649/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5349649/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69321230,"identity":"2dc086a3-f186-4be8-8666-eedd614e170f","added_by":"auto","created_at":"2024-11-19 07:02:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":895822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAerial photograph showing the locations of the Nicosia Traffic Station (35.1520N, 33.3479E) and of the Nicosia CAO Station (35.1460N, 33.3806E), where the AQT530 monitors were installed during the study period (©Google Earth 2023).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/d35351b4ef0074c64b4005c2.png"},{"id":69321226,"identity":"04847026-582d-4c4a-8a65-5402efe64b93","added_by":"auto","created_at":"2024-11-19 07:02:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between hourly-averaged measurements from the LCSs in the two Vaisala AQT530 monitors over a period of one week that those were co-located at TRS. The y axis (VSL\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eTRS\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) corresponds to the measurements with the AQ monitor operated at TRS after the co-location period, whereas the x axis (VSL\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eUBS\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e) to the measurements of the monitor that moved to UBS after the co-location week. The slope and intercept of the linear regression fitting (y = ax + b) for each sensor pair is also indicated. The black dashed lines correspond to the 1:1 correlation.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/ec7092440fe8302164824802.png"},{"id":69321228,"identity":"c627f4a1-e002-4e86-8091-fef652d38634","added_by":"auto","created_at":"2024-11-19 07:02:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186030,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between hourly averaged measurements recorded by the VSL\u003csub\u003eTRS\u003c/sub\u003e (Fig.3a-f, left panel) or the VSL\u003csub\u003eUBS\u003c/sub\u003e (Fig. 3g-l, right panel) AQ monitors, and those provided by the respective reference instruments. The black dashed lines correspond to the 1:1 correlation. The data points are colour-tagged with respect to season, with blue indicating the cold and red the warm seasons.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/af3e61f374af9c11f4a34a2e.png"},{"id":69322323,"identity":"dff9f09d-c991-4238-a4a5-cbbab4e746c1","added_by":"auto","created_at":"2024-11-19 07:10:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime series showing the monthly-average concentrations of CO (a-b) and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (d-e) measured by the reference instruments (solid lines) and the low-cost gas sensors employed in the Vaisala AQ monitors (dashed lines) at the two stations, as well as the differences in the concentrations between the two stations as those were captured by the reference (solid lines) and LCS (dashed lines) measurements for each pollutant (c and f). The blue and orange lines correspond to measurements at TRS and UBS stations, respectively. The red-shaded areas indicate that the monthly differences between the same LCSs employed in the Vaisala monitors for every month are not statistically significant (p \u0026gt; 0.05), in contrast to the reference measurements that showed statistically significant different station-to-station concentrations for all the months.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/466ade1e8dabbbf83eed8d63.png"},{"id":69322324,"identity":"b993c802-6fb3-4359-874b-f54c194fe0d3","added_by":"auto","created_at":"2024-11-19 07:10:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiurnal variability of hourly-averaged CO (a-d) and PM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (e-h) during January (merged data from 2022 and 2023; left column) and June (merged data from 2022 and 2023; right column) as measured by the reference instrument (solid lines) and the LCSs in the Vaisala monitor (dashed lines) at the two stations. The blue and orange lines correspond to measurements at TRS and UBS stations, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/f349de557a3a8597085a598d.png"},{"id":69321227,"identity":"4aa70f28-a895-4bff-99ca-6be0244bd4af","added_by":"auto","created_at":"2024-11-19 07:02:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":52290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTarget diagrams showing the bias and variance of the NO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (a) and the O\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e(b) LCSs employed in the AQT530 monitors against reference measurements before (open symbols) and after (solid symbols) their firmware updates (on 25/08/2022 for the NO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e and on 26/01/2023 for the O\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e sensor). If the variance of the residuals (VSL-Ref) is smaller than the variance of the reference measurements, the data points should fall within the blue circle.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/667a06e11c73c4ecc5409748.png"},{"id":74634183,"identity":"84369e5f-8f5f-4c0e-8fa0-864b6f1ee0d5","added_by":"auto","created_at":"2025-01-24 08:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4174888,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/ff3392ac-76c5-4511-87e3-89a9b67c94c5.pdf"},{"id":69322439,"identity":"886db4a8-79ae-4386-aa2a-efc591abd72c","added_by":"auto","created_at":"2024-11-19 07:18:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3632845,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-5349649/v1/66546540cd945ac202cf13f7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eAir pollution is a major concern of our modern societies, due to its adverse effects upon human health and the environment (Juginovic et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kuntic et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is more so in urban agglomerates, where a range of human activities can yield high concentrations of air pollutants at specific locations, typically referred to as air pollution hot spots, creating notable spatial and temporal variabilities within a city. Although capturing this variability with in-situ air quality (AQ) monitors is strongly desired, the high capital and maintenance costs of the necessary instruments still limit the density of urban AQ monitoring stations. For example, in large European cities (e.g., London, Paris, Rome, Madrid, Berlin and Athens) the number of fixed AQ monitoring stations is of the order of 1 station every ca. 50 km\u003csup\u003e2\u003c/sup\u003e or 170 thousand inhabitants (London Air, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Association for the Monitoring of Air Quality in the \u0026Icirc;le-de-France, 2018; Regional Agency for the Protection of the Environment of Lazio, 2020; Madrid Air Quality Portal, 2022; Berlin Air Quality Monitoring Network, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ministry of Environment and Energy, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, the city of Nicosia operates only two AQ monitoring stations, corresponding to ca. 1 station every 50 km\u003csup\u003e2\u003c/sup\u003e or 150 thousand inhabitants (Department of Labour Inspection, 2021). The limited number of fixed air quality monitoring stations may result in missing localized pollution hot spots, preventing them from capturing spatial variabilities in urban areas.\u003c/p\u003e \u003cp\u003eLow-cost AQ sensors have evolved rapidly over the last few decades. Among all types of LCSs of gaseous pollutants, modern electrochemical (EC) sensors typically exhibit a wide detection range, fast enough response, as well as adequate selectivity and sensitivity that can qualify them for AQ measurements (He et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additional advantages such as simple and robust operation, low power consumption and portability, combined with the ease of installation, allow their deployment in dense AQ networks (Bulot et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; B\u0026iacute;lek et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Frederickson et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; deSouza et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raheja et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accuracy of EC sensors when tested under controlled (laboratory) conditions can range within several tens of percent (Collier-Oxandale et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Castell et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, the accuracy of the PM sensor employed in the Vaisala AQT530 is in the order of 5% according to the manufacturer (AQT530, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is at least one order of magnitude higher compared to the respective values of reference PM instruments; e.g., \u0026plusmn; 0.75% for the tapered element oscillating microbalance (TEOM; TEOM 1405-DF, 2020). When compared against reference instruments under field conditions, the performance of low-cost AQ sensors has been shown to deviate further. This is not surprising considering that LCSs can be strongly influenced by their operating environmental conditions, including air temperature, relative humidity (RH), and the presence of other gaseous pollutants that can induce a measurable signal (Spinelle et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Lewis et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Borrego et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Castell et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cui et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe performance limitations of EC sensors are primarily related to the nature of the redox reactions at the surface of the sensing electrode (Stetter and Li, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). When gas molecules adsorb onto the electrode, they undergo oxidation or reduction, producing an electrical current proportional to their concentration. This process, and consequently the accuracy of EC sensors, is significantly influenced by the operating temperature and relative humidity (Wei et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Low-cost PM sensors, on the other hand, typically rely on optical methods using light scattered by the sampled particles to infer their concentration, and in some models also their size (Karagulian et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They can be divided into two categories: particle photometers and particle counters (McMurry 2000). Both methods provide signals that are proportional to the PM concentration, as well as on the optical properties of the sampled particles defined by their size, morphology and composition. Considering that information on particle morphology and composition is hard to obtain, we typically assume that all the particles are spherical and have a refractive index (and density when determining their mass) corresponding to polystyrene latex or ammonium sulphate aerosol particles (Marx and Mulholland, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). These assumptions contribute to the measurement uncertainty when sampling ambient atmospheric aerosol particles. When the sampled particles are hygroscopic, they can take up a significant amount of water vapour from the ambient air, consequently increasing their size and altering their refractive index significantly (Carslaw et al. 2022); a phenomenon that provides another source of uncertainty in low-cost PM sensors.\u003c/p\u003e \u003cp\u003eAs shown by a growing body of literature reports, existing LCSs cannot meet the quality objectives for use in regulatory observations that require at most 15% uncertainty (specifically expressed as relative expanded uncertainty, REU, defined by Directive 2008/50/EC, 2008) for CO, NO\u003csub\u003e2\u003c/sub\u003e, NO and O\u003csub\u003e3\u003c/sub\u003e, and 25% for PM measurements. Similarly, they often fail to meet the criteria for indicative measurements, which call for a 25% REU for CO, 30% for NO\u003csub\u003e2\u003c/sub\u003e, NO and O\u003csub\u003e3\u003c/sub\u003e, and 50% for PM, as described in the same Directive. However, they have been found useful in other applications, including identification of air pollution hot spots (Gao et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Baruah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Feinberg et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), distinction between local and non-local pollution sources (Heimann et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Popoola et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and high-resolution AQ mapping (Schneider et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) among others, which require lower accuracy. In such studies, LCSs have been integrated as part of networks for multi-point AQ measurements, consequently increasing the spatiotemporal resolution of AQ monitoring networks.\u003c/p\u003e \u003cp\u003eThe high uncertainties associated with LCS measurements can in principle be reduced by more laborious calibration models than those provided by the manufacturers. These models can be developed through well-designed laboratory tests (e.g., Nagendra et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and/or by field measurements whereby the LCSs are collocated with reference instruments over long periods of time (Raheja et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Bisignano et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Crawford et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kim et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Machine-learning calibration methods can additionally be applied to the measurements in order to further improve the quality of the produced data (Bisignano et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Baruah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cabaneros et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such algorithms are already embedded in some AQ monitors that employ low-cost gas and PM sensors (e.g., the Vaisala AQT530; Pet\u0026auml;j\u0026auml; et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite these efforts, critical questions still need to be addressed, including the ability of LCSs to capture spatial and temporal variations of pollutants, and their effectiveness in identifying air pollution hotspots.\u003c/p\u003e \u003cp\u003eIn this work, we evaluate the ability of the Vaisala AQT530 monitors to capture spatial and temporal variabilities of pollutants within a city agglomerate. To achieve that we carried out measurements with two monitors located at two AQ monitoring stations (a traffic and an urban background station) in the city of Nicosia, Cyprus. Both set of sensors were collocated with reference grade instruments, over a period of 19 months.\u003c/p\u003e"},{"header":"2 METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of Air-Quality Measurement Stations\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an aerial photograph of part of the city of Nicosia, with the locations of the two AQ measurement stations. The distance between the two stations is 3.5 km. The Nicosia Traffic Station (referred to as TRS from now on), has a distance of ten metres from one of the main and busiest city avenues (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which is typically congested during the morning and the afternoon rush hours. The site is operated by the Department of Labour Inspection (DLI), which is part of the Ministry of Labour and Social Insurance of Cyprus, and one of the two reference AQ stations of Nicosia (Department of Labour Inspection, 2021). Measurements at the station are conducted according to guidelines described in the relevant EC Directives and the corresponding national laws defining the specifications that the employed instruments have to meet, as well as the procedures that must be followed for operating and maintaining them (cf. Directive 2008/50/EC, 2008 for more details).\u003c/p\u003e \u003cp\u003eThe Nicosia Cyprus Atmospheric Observatory (CAO) station is located at the campus of the Cyprus Institute (CyI) next to the Athalassa forestry park in Nicosia, and can be considered an urban background station (referred to as UBS from now on; cf. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The area around the UBS station is sparsely populated with no significant local pollution sources in its vicinity (i.e., low traffic density, industry, commercial centres, restaurants, etc.). The site is sporadically influenced by minor local traffic due to the trans-pass of a small number of vehicles through the CyI campus. The concentrations of different gaseous pollutants and PM are continuously monitored at this station for research purposes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Instrumentation\u003c/h2\u003e \u003cp\u003eThe reference instruments used at the two stations are standard analysers for measuring the concentration of gaseous pollutants, and the TEOM Model 1405-DF for the PM measurements (cf. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The specifications of the instruments used at the TRS and UBS are provided in Tables S1 and S2 of the supplement, respectively.\u003c/p\u003e \u003cp\u003eAt the TRS, zero and span checks are performed daily using station gas and zero-air generator to monitor analyser drift. All reference gas analysers are calibrated monthly using high-concentration certified transfer gas, in accordance with manufacturer guidelines and EN standards. O\u003csub\u003e3\u003c/sub\u003e analysers are calibrated every three months at the National Reference Laboratory while O\u003csub\u003e3\u003c/sub\u003e span and zero checks are carried out daily. The measurements are validated and reported by DLI at one-hour time resolution.\u003c/p\u003e \u003cp\u003eAt the UBS, span and zero checks are performed weekly for all reference gas analysers using certified high-concentration gas cylinders and zero-air generator, in accordance with guidelines provided by the manufacturers and European (EN) standards. Calibration of gas analysers is performed monthly. The O₃ analysers are calibrated every three months at the National Reference Laboratory. O\u003csub\u003e3\u003c/sub\u003e span and zero checks are performed daily. Gas concentrations from the reference analysers, expressed in ppb, are recorded every 5 minutes, while PM measurements are taken every 6 minutes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstruments operated at the Nicosia traffic (TRS) and CAO (UBS) air quality monitoring stations, which are employed in this study for the reference measurements. The limit of detection of each instrument is also provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLimit of Detection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUBS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTRS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUBS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcotech Serinus 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeledyne Model T300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;40 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcotech Serinus 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeledyne Model T500U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.04\u0026nbsp;ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThermo Scientific 49i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeledyne Model T400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.4 ppb with 80 Sample Digital Filter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTEOM Model: 1405-DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTEOM Model: 1405-DF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo Vaisala Air Quality Transmitter-Monitors (AQT530) series and Weather Transmitters (WXT530) series are employed in this study. These monitors include four Alphasense B-series EC sensors for trace gas measurements (i.e., CO, NO\u003csub\u003e2\u003c/sub\u003e, NO, and O\u003csub\u003e3\u003c/sub\u003e), and a particle counter (Model LPC200) that measures aerosol mass concentrations in two size fractions (i.e., mass concentrations of particles smaller than 2.5 and 10 \u0026micro;m; PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, respectively). The AQ monitors also have a built-in Vaisala HUMICAP\u0026reg; humidity and temperature probe (Model HMP110). The WXT530 transmitter provides measurements of the wind direction and speed, rainfall, temperature, and absolute pressure.\u003c/p\u003e \u003cp\u003eThe signals from the gas sensors (reported in mV) used in the AQT530 monitors are converted to concentrations (in ppb) using calibration algorithms developed by Vaisala, which are different to the ones provided by Alphasense. We should note here that during the course of the measurements, firmware updates that included new calibration models for the NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e LCSs became available by Vaisala. More specifically, the AQ monitors were updated from firmware 3.4 to 3.5 on 25 August 2022, in order to improve the measurements reported by the NO\u003csub\u003e2\u003c/sub\u003e sensors, and to firmware 3.6 on 26 January 2023, to improve the measurements reported by the O\u003csub\u003e3\u003c/sub\u003e sensors. The comparison of the measurements with the two firmware is discussed further in sections 2.3 and 3.5.\u003c/p\u003e \u003cp\u003ePrior to installation at the two stations, the AQ monitors were co-located at the TRS station for one week (from 5 to 11 November 2021) to conduct an inter-comparison, testing differences between the sensors while measuring the same concentrations of gases and particles. Both AQ monitors were placed outdoors at a distance of a few cm from each other and 1 m from the inlet of the reference instrumentation. Following this period, one monitor was relocated to the UBS station, while the other remained at the TRS station. Both Vaisala AQ monitors, referred to as VSL\u003csub\u003eTRS\u003c/sub\u003e (at TRS) and VSL\u003csub\u003eUBS\u003c/sub\u003e (at UBS), collected data from 2 December 2021 to 22 June 2023. Over this 19-month period, the sensors provided continuous time series of measurements, with only minor interruptions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data processing and analysis\u003c/h2\u003e \u003cp\u003eNegative values reported by all sensors in the AQ monitors were flagged and removed as suggested by the manufacturer. All measurements were then averaged over a period of an hour for comparison with the reference measurements. To determine the impact of temperature and RH variabilities on the performance of the sensors we divided the whole dataset into different temperature (i.e., \u0026lt; 10\u0026deg;C, 10\u0026ndash;20\u0026deg;C, 20\u0026ndash;30\u0026deg;C and \u0026gt;\u0026thinsp;30\u0026deg;C) and RH (i.e. \u0026lt; 30%, 30\u0026ndash;55%, 55\u0026ndash; 75% and \u0026gt;\u0026thinsp;75%) ranges, following the same procedure described by Papaconstantinou et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe performance of the sensors was evaluated by directly correlating and comparing the reported concentrations with measurements by the respective reference instruments to determine the associated errors at each sampling station. The parameters used to do so were the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), the Mean Bias Error (MBE), the Mean Relative Error (MRE) and the Mean Absolute Error (MAE), defined respectively as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{\\text{R}}^{2}=1-\\:\\frac{{\\sum\\:_{\\text{i}=1}^{\\text{N}}({\\text{C}}_{V,i}-\\:{C}_{ref,i})}^{2}}{{\\sum\\:_{\\text{i}=1}^{\\text{N}}({\\text{C}}_{V,i}-\\:\\stackrel{-}{{C}_{ref}})}^{2}}\\:,$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{R}\\text{E}=\\:\\frac{1}{\\text{N}}\\sum\\:_{\\text{i}=1}^{\\text{N}}\\left(\\frac{{(\\text{C}}_{V,i}-{\\text{C}}_{ref,i}}{{\\text{C}}_{ref,i}}\\right)\\times\\:\\:100,$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{B}\\text{E}=\\frac{1}{\\text{N}}\\sum\\:_{\\text{i}=1}^{\\text{N}}{\\text{C}}_{V,i}-{\\text{C}}_{ref,i},\\:\\text{a}\\text{n}\\text{d}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\text{M}\\text{A}\\text{E}=\\frac{1}{\\text{N}}\\sum\\:_{i=1}^{\\text{N}}\\left|{\\text{C}}_{V,i}-{\\text{C}}_{ref,i}\\right|.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn all the equations listed above, N is the total number of data points, whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}}_{V,i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}}_{ref,i}\\)\u003c/span\u003e\u003c/span\u003e are the concentrations (expressed in ppb) measured respectively by the sensors employed in the AQ monitors and the reference instruments at time \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo investigate whether the differences between LCS measurements at the two different stations or LCS and reference measurements at the same station are statistically significant, we used the Wilcoxon rank-sum (WRS) test (see details in Section S.4 in the Supplement). Differences that are not statistically significant (i.e., when p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicate that the data are samples from continuous distributions with equal medians.\u003c/p\u003e \u003cp\u003eThe measurements from the NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e LCSs were also divided into two periods, corresponding to the upgrades of their firmware. The performance of the sensors for each period (namely before and after their firmware update) is assessed with target diagrams, provided in section 3.5, where the vector distance from their origin shows the level of bias and variance of each sensor against reference measurements. The vector is the root mean square error (RMSE) calculated as:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{\\left(\\frac{RMSE}{{\\sigma\\:}_{ref}}\\right)}^{2}=\\:{\\left(\\frac{MBE}{{\\sigma\\:}_{ref}}\\right)}^{2}+\\:{\\left(\\frac{CRMSE}{{\\sigma\\:}_{ref}}\\right)}^{2},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CRMSE\\)\u003c/span\u003e\u003c/span\u003e is the centred root mean square error, which is the RMSE corrected for bias, and MBE is the mean bias error. All parameters were normalized by the standard deviation of the reference measurements,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\sigma\\:}_{ref}\\)\u003c/span\u003e\u003c/span\u003e. The horizontal line passing through the centre of the target diagram corresponds to zero bias, with the points above or below it corresponding respectively to overestimations or underestimations compared to the reference measurements. When the standard deviation of the LCS measurements is higher or lower compared to those reported by the reference instruments, the points fall on the right or left quadrant of the target diagram, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Collocated measurements and overall performance of Vaisala AQTs\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results from the one-week period we co-located the LCSs with reference instruments at the TRS where we tested the AQ monitors against each other and against the reference instruments. When comparing the CO, NO and PM\u003csub\u003e10\u003c/sub\u003e measurements reported by the LCSs employed in the two Vaisala AQT530 monitors against each other, we observed the highest agreement (i.e., MRE less than ca. 10%), and correlations (R\u003csup\u003e2\u003c/sup\u003e values being greater than 0.99). The rest of the sensors (i.e. NO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e) exhibit good to moderate unit-to-unit agreement (MRE up to ca. 50%), and inter-correlations with R\u003csup\u003e2\u003c/sup\u003e values ranging from 0.72 to 0.93. Correlation plots, including linear fits, between the measurements reported by all the sensors of the two AQT530 monitors are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWhen compared against reference measurements, the CO sensors employed in both AQ monitors exhibit good agreement (MAE\u0026thinsp;\u0026lt;\u0026thinsp;175 ppb) and the highest correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.96), followed by the PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, NO, O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (cf. Fig. S2 for the respective correlation plots). Apart from the NO LCSs, which report measurements exhibiting significantly higher mean absolute differences between the two sensors in the unit-to-unit comparison and from the reference instrument, the rest exhibit differences close to those reported by the manufacturer (AQT530, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, build confidence that the measurements from the LCSs of the two AQ monitors mentioned above are consistent, and that they provide measurements that correlate well enough with the reference instruments. To go a step further and investigate whether the unit-to-unit differences during the co-location week are statistically significant, we used the WRS test. This allows us to further evaluate if the sensors produce comparable/reproducible results that can be used to determine spatial variability. The results of these tests show that the differences of the NO, O\u003csub\u003e3\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e measurements reported by the two AQ monitors were statistically significant, indicating that the reproducibility of the sensors, averaged over the entire measurement period of the co-located measurements, is poor. In contrast, the respective differences of the CO, NO\u003csub\u003e2\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e measurements were not statistically significant. Among those, however, only the CO and PM\u003csub\u003e10\u003c/sub\u003e measurements exhibit a high enough inter-correlation, with R\u003csup\u003e2\u003c/sup\u003e values being greater than 0.99 (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting a good sensor-to-sensor reproducibility for every measurement. Considering that, only the CO and PM\u003csub\u003e10\u003c/sub\u003e sensors were tested for their ability to capture spatial differences between different stations (cf. section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e below).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation and mean absolute differences between the measurements reported by the LCSs in the two AQ monitors and between each LCS with the respective reference instrument during the period we co-located them with reference instruments at the TRS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation between LCS measurements (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean Absolut Difference between\u003c/p\u003e \u003cp\u003eLCS measurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMedian\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation of LCS residuals\u003c/p\u003e \u003cp\u003e(VSL\u003csub\u003eTRS\u003c/sub\u003e - VSL\u003csub\u003eUBS\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCorrelation between LCS \u0026amp; reference measurements (R\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eMean Absolute Error between\u003c/p\u003e \u003cp\u003eLCS \u0026amp; reference measurements\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.06 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e239.25\u0026thinsp;\u0026plusmn;\u0026thinsp;393.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e238.79\u0026thinsp;\u0026plusmn;\u0026thinsp;388.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.79\u0026thinsp;\u0026plusmn;\u0026thinsp;17.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e143.27\u003c/p\u003e \u003cp\u003eppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e172.68 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.55 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.83\u0026thinsp;\u0026plusmn;\u0026thinsp;15.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.83\u0026thinsp;\u0026plusmn;\u0026thinsp;17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.48 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.60 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.11 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e93.33\u0026thinsp;\u0026plusmn;\u0026thinsp;54.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e103.37\u0026thinsp;\u0026plusmn;\u0026thinsp;65.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-10.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.48 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e75.86 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.28 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.38 ppb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.18\u003c/p\u003e \u003cp\u003eppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e2.5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.70 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.98 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.56\u0026thinsp;\u0026plusmn;\u0026thinsp;27.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.68\u0026thinsp;\u0026plusmn;\u0026thinsp;26.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e-1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.78 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.11 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing the co-located measurements described above, one of the AQ monitors was installed at the UBS while the other remained at the TRS, and both systems were allowed to collect data over a period of 19 months, as described in section 2.2. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the correlation between the measurements recorded by the monitor employed at the TRS (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-f) and UBS (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-l) against the respective measurements by the reference instruments. All the data points are colour coded based on the season of measurements, with blue dots corresponding to the cold and red to the warm seasons (cf. Figs. S2 and S3 in the supplement that provide the same data in the form of time series). Statistical measures of the differences between the VSL\u003csub\u003eTRS\u003c/sub\u003e and the VSL\u003csub\u003eUBS\u003c/sub\u003e measurements against their respective reference measurements are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOverall, the CO measurements from both AQ monitors (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea for VSL\u003csub\u003eTRS\u003c/sub\u003e, and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg for VSL\u003csub\u003eUBS\u003c/sub\u003e) exhibit good agreement with the respective reference measurements as reflected by the relatively low MRE values (-50.51% for VSL\u003csub\u003eTRS\u003c/sub\u003e, and 38.32% for VSL\u003csub\u003eUBS\u003c/sub\u003e), and the high correlation with those reported by the respective reference instrument, exhibiting R\u003csup\u003e2\u003c/sup\u003e values of 0.91 for VSL\u003csub\u003eTRS\u003c/sub\u003e, and 0.70 for VSL\u003csub\u003eUBS\u003c/sub\u003e. The O\u003csub\u003e3\u003c/sub\u003e LCS measurements (cf. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej) also exhibit deviations from their respective reference measurements (MRE is 89.22% for VSL\u003csub\u003eTRS\u003c/sub\u003e and \u0026minus;\u0026thinsp;7.13% for VSL\u003csub\u003eUBS\u003c/sub\u003e), but weaker correlations compared to the CO LCSs (the R\u003csup\u003e2\u003c/sup\u003e for VSL\u003csub\u003eTRS\u003c/sub\u003e and VSL\u003csub\u003eUBS\u003c/sub\u003e are 0.18 and 0.16, respectively). The performance of the NO\u003csub\u003e2\u003c/sub\u003e sensors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh) was among the poorest as indicated by the high MREs (224.48% for VSL\u003csub\u003eTRS\u003c/sub\u003e and 732.98% for VSL\u003csub\u003eUBS\u003c/sub\u003e) and the lack of correlation (R\u003csup\u003e2\u003c/sup\u003e value of 0.012 for VSL\u003csub\u003eTRS\u003c/sub\u003e and of 0.0014 for VSL\u003csub\u003eUBS\u003c/sub\u003e). Similarly, the NO sensors (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei) exhibit very high MREs (1.17\u0026times;10\u003csup\u003e4\u003c/sup\u003e% for VSL\u003csub\u003eTRS\u003c/sub\u003e and 1.03\u0026times;10\u003csup\u003e5\u003c/sup\u003e% for VSL\u003csub\u003eUBS\u003c/sub\u003e), mainly overestimating the reference concentrations, and extremely low correlation (R\u003csup\u003e2\u003c/sup\u003e being in the range of 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e for VSL\u003csub\u003eTRS\u003c/sub\u003e and VSL\u003csub\u003eUBS\u003c/sub\u003e, respectively). In general, all gas sensors show weaker correlations during the warm period between June and September, corroborating previous findings reported by our group (Papaconstantinou et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from the relative errors and correlations discussed above, it is important to investigate whether the month-to-month trends captured by the LCS measurements are similar to those from the reference instruments. Overall, the CO and PM sensors captured the month-to-month trend over the entire period of the measurements similarly to the reference instruments. In contrast, however, the measurements by the O\u003csub\u003e3\u003c/sub\u003e and NO LCSs, as well as part of those by the NO\u003csub\u003e2\u003c/sub\u003e LCSs (measurements corresponding to the first half of the study period), exhibited different overall trends and significant deviations against reference measurements, particularly during the summer periods (cf. Fig. S2 in the supplement). This difference in the temporal trends indicates that the performance of these sensors is affected more strongly by the high temperature and RH conditions compared to the CO LCSs, warranting further investigation and efforts to improve their performance.\u003c/p\u003e \u003cp\u003eThe PM concentrations reported by the VSL\u003csub\u003eTRS\u003c/sub\u003e and VSL\u003csub\u003eUBS\u003c/sub\u003e AQ monitors are lower than the reference measurements, as reflected by the negative MRE values shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Compared to PM\u003csub\u003e10\u003c/sub\u003e, the PM\u003csub\u003e2.5\u003c/sub\u003e measurements reported by the AQ monitors (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek) exhibit higher deviations against those provided by the reference instruments in both stations (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el). This is because the PM LCSs have a high cut-off diameter (50% detection efficiency for particle sizes of 0.6 \u0026micro;m; Vaisala, 2022), resulting in a portion of particles to go undetected. These undetected particles contribute proportionally more to the PM\u003csub\u003e2.5\u003c/sub\u003e mass than to the PM\u003csub\u003e10\u003c/sub\u003e mass. Overall, the PM concentrations reported by the VSL\u003csub\u003eTRS\u003c/sub\u003e monitor show greater discrepancies compared to those from the VSL\u003csub\u003eUBS\u003c/sub\u003e monitor when measured against the respective reference values (cf. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Despite that only the PM\u003csub\u003e10\u003c/sub\u003e measurements showed good sensor-to-sensor reproducibility when the two AQ monitors were collocated at the TRS in the beginning of our study period (cf. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the difference in the PM measurements between the two stations, as observed here, can be attributed to the higher fraction of particles smaller than the cut-off diameter of the PM sensors at the TRS compared to the UBS. This is highly possible considering that fresh and smaller particles, which are characteristic of traffic emissions (Zhu, 2002), should be higher at the TRS.\u003c/p\u003e \u003cp\u003eWe should highlight here that the agreement between PM LCSs and reference instruments improved significantly during periods with dust events. More specifically, the MRE between the PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e measurements reported by the LCSs decreased respectively from \u0026minus;\u0026thinsp;74.16 to -59.98% and from \u0026minus;\u0026thinsp;64.26 to -55.48% at the TRS, and from \u0026minus;\u0026thinsp;57.28 to -27.75% and from \u0026minus;\u0026thinsp;43.98 to -28.78% at the UBS. Similarly, the R\u003csup\u003e2\u003c/sup\u003e values of PM\u003csub\u003e2.5\u003c/sub\u003e measurements increased from 0.12 during the non-dust period (i.e., summer and winter) to 0.43 during the dust period (i.e., spring and autumn) for VSL\u003csub\u003eTRS\u003c/sub\u003e, and from 0.18 to 0.47 for VSL\u003csub\u003eUBS\u003c/sub\u003e, respectively (see Fig. S4 in the supplement). The respective R\u003csup\u003e2\u003c/sup\u003e values for PM\u003csub\u003e10\u003c/sub\u003e measurements increased from 0.32 to 0.78 for VSL\u003csub\u003eTRS\u003c/sub\u003e and from 0.47 to 0.92 for VSL\u003csub\u003eUBS,\u003c/sub\u003e respectively (cf. Fig. S5 in the supplement). Another reason contributing to the improved performance of the PM sensors during the dust events is the similarity in the optical properties of the micron-sized dust particles to those of calibration aerosol particles (AQT530, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics, including the number of useful measurements (N) recorded by the Vaisala AQ monitors, and the mean value of hourly averaged concentrations measured by the respective LCSs and the reference instruments for the entire study period, together with the Standard Deviation (SD) for each case, as well as the associated values of the MRE, MBE, MAE and R\u003csup\u003e2\u003c/sup\u003e. The Limit of Detection (LoD) values for each LCS employed in the Vaisala monitors are also provided.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollutant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVaisala N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVaisala\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMRE (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMBE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVaisala LoD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCO (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e190.16\u0026thinsp;\u0026plusmn;\u0026thinsp;226.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e357.89\u0026thinsp;\u0026plusmn;\u0026thinsp;292.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-50.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-165.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e169.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e129.84\u0026thinsp;\u0026plusmn;\u0026thinsp;136.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e235.53\u0026thinsp;\u0026plusmn;\u0026thinsp;169.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-66.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e85.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e32.35\u0026thinsp;\u0026plusmn;\u0026thinsp;29.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e14.82\u0026thinsp;\u0026plusmn;\u0026thinsp;9.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e224.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e27.24\u0026thinsp;\u0026plusmn;\u0026thinsp;24.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.07\u0026thinsp;\u0026plusmn;\u0026thinsp;9.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e732.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNO (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e111.05\u0026thinsp;\u0026plusmn;\u0026thinsp;102.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.72\u0026thinsp;\u0026plusmn;\u0026thinsp;22.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.17\u0026times;10\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.64\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e86.87\u0026thinsp;\u0026plusmn;\u0026thinsp;84.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03\u0026times;10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e (ppb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.69\u0026thinsp;\u0026plusmn;\u0026thinsp;47.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.34\u0026thinsp;\u0026plusmn;\u0026thinsp;16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5 ppb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e31.23\u0026thinsp;\u0026plusmn;\u0026thinsp;26.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e36.18\u0026thinsp;\u0026plusmn;\u0026thinsp;16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-62.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-13.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e13.29\u0026thinsp;\u0026plusmn;\u0026thinsp;9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-36.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eTRS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e15.62\u0026thinsp;\u0026plusmn;\u0026thinsp;19.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e38.78\u0026thinsp;\u0026plusmn;\u0026thinsp;26.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-58.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-22.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e23.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVSL\u003csub\u003eUBS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18.05\u0026thinsp;\u0026plusmn;\u0026thinsp;27.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.82\u0026thinsp;\u0026plusmn;\u0026thinsp;20.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-29.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the measurements of the low-cost gas sensors are in better agreement with those of the reference instruments during the cold periods (i.e., from September to May when RH is higher than 55%) than during the warm periods (i.e., from June to August when RH is decreased below 55%); cf. Figs. S6 and S8 for the dependence of the LCSs on temperature and Figs. S10 and S12 on the RH. Some of the LCSs (particularly the NO\u003csub\u003e2\u003c/sub\u003e, NO and O\u003csub\u003e3\u003c/sub\u003e sensors) provide measurements that deviate substantially from those of the reference instruments over the warm periods. We have observed similar behaviour in previous measurements with LCSs, and have attributed it to the evaporation of water in the electrolyte of the sensors (Papaconstantinou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alphasense AAN106). We should note here that gas sensors used in the Vaisala AQ monitors exhibit better performance compared to the sensors tested in our previous work, which, in contrast to this study, did not fully recover after being exposed to moderate temperature and RH conditions. Although the sensors were of the same model and manufacturer in both studies, this difference can be attributed to the different production batches or the type/design of electrolyte enclosure used.\u003c/p\u003e \u003cp\u003eThe measurements of the PM LCSs are in better agreement with those of reference instruments at RH conditions below 30% (cf. Figs. S11 and S13 in the supplement for VSL\u003csub\u003eTRS\u003c/sub\u003e and VSL\u003csub\u003eUBS\u003c/sub\u003e, respectively). This is because the size and refractive index of the particles change due to water uptake at higher RH conditions, increasing significantly their scattering efficiency, and causing deviations from the reference measurements that are carried out at dry conditions (Titos et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bezantakos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since typically low RH conditions are associated with high temperatures, PM LCS measurements tend to correlate better with reference measurements at higher temperatures. At both stations, agreement between the Vaisala PM\u003csub\u003e10\u003c/sub\u003e with the reference measurements is better, compared to that of PM\u003csub\u003e2.5\u003c/sub\u003e for all temperature and RH conditions due to the reasons described above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Spatial variabilities determined by the reference instruments and the LCSs\u003c/h2\u003e \u003cp\u003eThe results shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e indicate that the reference concentrations (averaged over the entire study period) of all the pollutants at the two stations are different, with deviations ranging from ca. 25 to 110%. For example, the mean reference CO concentration at the TRS is 357.9 ppb while the respective value at the UBS is 235.5 ppb. To investigate whether these differences are statistically significant both when the values are averaged over the entire measurement period and on a monthly basis, we used the WRS test. The results of this test show that the differences for all pollutants measured by the reference instruments are significant at a 95% confidence interval for the average values over the entire measurement period and for each individual month (cf. Tables S3-S6 in the supplement for more details). These differences are expected, as we are comparing two different types of stations: a traffic and an urban background station, which naturally experience varying pollutant levels due to their differing environments. By identifying whether the LCSs are also able to capture these spatial differences, one can in principle determine if they can be reliably used for spatial hot-spot recognition.\u003c/p\u003e \u003cp\u003eThe same statistical test was used for the measurements by the CO and PM\u003csub\u003e10\u003c/sub\u003e LCSs of the AQ monitors operated at the two stations, which were the ones exhibiting high sensor-to-sensor reproducibility during the co-location period as discussed in section 3.1. For the entire study period (i.e., all 19 months), the mean concentrations determined by the LCSs in the two stations are statistically different at the same confidence interval (i.e., 95%; cf. Table S3), as also indicated by comparing the respective reference instruments at the two stations. However, when the same test is applied for every individual month, there are a few cases where the LCSs show no statistically significant differences between the two stations while the reference instruments do (see orange-coloured cells in Tables S4-S5). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows time series of the monthly-average concentrations of the pollutants as those were determined by the measurements from the reference instruments (cf. solid lines) and the AQT sensors (cf. dashed lines), as well as the concentration differences between reference and LCSs measurements. The CO sensors seem to report measurements that follow the overall concentration variability as this is captured by the respective reference instrument. The PM\u003csub\u003e10\u003c/sub\u003e measurements reported by the AQ monitors generally follow the trend of the reference measurements. The striking peak observed in April 2022 is due to a strong dust event as discussed above; a phenomenon that occurs with higher frequency and intensity during this period of the year in the region (Yukhymchuk et al. 2022, Kezoudi et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). PM\u003csub\u003e10\u003c/sub\u003e concentrations, however, are generally higher at the TRS compared to the UBS, as indicated by the reference instruments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), which is in contrast to what the LCS measurements indicate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). This discrepancy could be explained by the systematic presence of particles smaller than the detection limit of the low-cost PM sensor (i.e., 600 nm) employed in the Vaisala monitor at the TRS compared to the UBS. This is a plausible explanation considering that TRS is affected to a higher degree by particles from anthropogenic sources that are typically smaller than the LCS detection threshold. In contrast, background particles, which are more likely to represent the atmospheric aerosol at the UBS, are generally larger and thus the majority (if not all) of those are detected and counted by the low-cost PM sensor in the monitor.\u003c/p\u003e \u003cp\u003eRed-shaded areas in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e denote the months when the measurements by the LCSs did not exhibit statistically significant differences while the reference instruments did. The respective p-values calculated by the statistical test are tabulated in the supplement (cf. Tables S4 and S5). The CO LCSs show no statistically significant differences in 2 out of the 19 months (July and August 2022). During these months, the difference of the CO concentrations between the two stations as determined by the LCSs and the reference instruments was below 4 and 80 ppb, respectively (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The PM\u003csub\u003e10\u003c/sub\u003e measurements reported by the LCSs show no statistically significant differences in 7 out of the 19 months (i.e., December 2021, February, March and November 2022 and March, April and May 2023; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). The PM concentration differences between the two stations in these cases, as measured by the LCSs, were below 3\u0026ndash;4 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while those measured by the reference instruments were below 6\u0026ndash;10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ability of the LCSs to capture spatial variabilities is important for their use for the identification of pollution hot-spots. With the exemption of the two warm months, the CO LCSs can be used for capturing the reference month-to-month trends/variabilities, urban gradients and pollution hot spots.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the averaged diurnal trends/variabilities on workdays and weekends for CO and PM\u003csub\u003e10\u003c/sub\u003e during the two months of January in our dataset (i.e., for 2022 and 2023) when the mean hourly temperatures ranged from 0.2 to 20.7\u0026deg;C, and for the two months of June, when the respective values ranged from 15.6 to 37.7\u0026deg;C; note that the diurnal variations observed in January 2022 and January 2023, as well as in June 2022 and June 2023, exhibited a high degree of similarity (cf. Figs. S14 and S15 in the supplement). Tables S6 and S7 in the supplement provide the p-values from the tests using the data shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The hourly reference measurements at the two stations are significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with few exemptions mainly for CO during morning and night hours when the concentration difference is insignificant (p ranging from \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:5.3\\times\\:{10}^{-02}\\)\u003c/span\u003e\u003c/span\u003e to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:9.5\\times\\:{10}^{-01}\\)\u003c/span\u003e\u003c/span\u003e). The LCSs in the AQTs are able to capture the differences in the diurnal variation between the two stations better during workdays than weekends, mainly due to the higher concentrations observed during the former (cf. reference measurements also in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) that can be captured with higher fidelity by the LCSs.\u003c/p\u003e \u003cp\u003eMore specifically, the CO sensors in the two AQ monitors appear to follow better the diurnal variabilities, as these are captured by the reference instruments, during the cold (January 2023 and January 2023) rather than the warm (June 2022 and June 2023) months. This is not surprising considering that the performance of EC sensors drops at high temperatures and at low concentrations that occur in the warm period (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b against 5c-d). What is more, they fail to capture the significant difference observed between reference measurements during the middle of the days in the weekends when the CO concentration differences are small between the two stations (\u0026lt;\u0026thinsp;80 ppb as indicated by the reference instruments), especially during the warm period (i.e., between 10.00 am and 6.00 pm in June; cf. Table S6 in the supplement).\u003c/p\u003e \u003cp\u003eThe PM\u003csub\u003e10\u003c/sub\u003e LCSs captured well the diurnal trend/variability of the reference measurements during the weekdays of the cold periods (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), but fail to do so during the weekends of the same period (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef), most likely due to the lower concentration of particles that are large enough to be detected by these sensors. The PM\u003csub\u003e10\u003c/sub\u003e measurements also fail to capture the trends observed by the respective reference instruments during the warmer months (both during weekdays and weekends; cf. Figures\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh), due to the same reason. Overall, the ability of the PM\u003csub\u003e10\u003c/sub\u003e measurements by the LCSs to capture the spatial differences was higher in cases where the difference in the concentration at the two stations was higher (i.e., \u0026gt; 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e). It should also be noted that these differences were in the opposite direction compared to that when comparing the measurements reported by the references instruments: i.e., the LCSs concentrations at UBS are systematically higher than those at the TRS, while the opposite is true for the reference measurements. As explained earlier in the same section, this is due to less sub-600 nm particles at the UBS compared to TRS station.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Temporal variabilities determined by the reference instruments and the LCSs at the same station\u003c/h2\u003e \u003cp\u003eThe next step was to examine whether the LCSs can capture the temporal variabilities as those are determined by the reference instruments at each station. To do so, we carried out WRS tests between successive months for the reference and the LCS measurements at each of the two stations. Table\u0026nbsp;4 shows the calculated p-values for each test, and whether this indicates significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When the reference month-to-month concentration differences are statistically significant, the LCSs can also reproduce this result for most of the cases (indicated by the white cells corresponding either to REF\u003csub\u003eTRS\u003c/sub\u003e, REF\u003csub\u003eUBS\u003c/sub\u003e, VSL\u003csub\u003eTRS\u003c/sub\u003e, or VSL\u003csub\u003eUBS\u003c/sub\u003e in Table\u0026nbsp;4). In contrast, when the month-to-month variability of the reference measurements is not statistically significant (blue and orange cells for the measurements at TRS and UBS, respectively), it is more likely for the respective LCSs to suggest the opposite: i.e., that the differences are significant. This can be attributed to the low accuracy of the LCSs compared to the reference instruments.\u003c/p\u003e \u003cp\u003eThe CO LCS measurements at the two stations identified accurately the statistically significant differences in ca. 55\u0026ndash;60% of the cases (11 and 10 out of the 18 sets of consecutive months at TRS and UBS, respectively). The respective numbers for the NO\u003csub\u003e2\u003c/sub\u003e and NO LCSs were ca. 60 and 70% for TRS and ca. 70 and 45% for UBS. Regarding O\u003csub\u003e3\u003c/sub\u003e, the LCSs captured the statistically significant or insignificant variabilities in ca. 65% of the cases at both stations. The PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e LCSs captured the statistical significance (or non-significance) of the month-to-month variability in ca. 80 and 70%, respectively, at both stations.\u003c/p\u003e \u003cp\u003eConsidering that the PM sensors, and marginally the NO and NO\u003csub\u003e2\u003c/sub\u003e sensors, capture accurately the month-to-month variabilities in more than 70% of the cases, they can qualify as appropriate to determine the seasonal variabilities. However, taking into account that the NO and NO\u003csub\u003e2\u003c/sub\u003e sensors fail to capture the overall trends, reporting values that are significantly different compared to the reference instruments, they can be excluded, leaving only the PM sensors as the most reliable indicator of temporal variabilities.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable\u0026nbsp;4: P-values determined by the WSR tests between all pairs of consecutive months of measurements using either the reference or the LCS measurements. Blue (for TRS) and orange (for UBS) cells indicate that the month-to-month differences are not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), whereas white cells indicate the opposite (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Colour agreement between the reference and LCS measurements for each pair of consecutive months indicate that the AQT sensors are able to capture the temporal variabilities.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"690\" height=\"540\"\u003e\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparison of the performance of NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e sensors before and after firmware update\u003c/h2\u003e \u003cp\u003eAs discussed in Section 2.2, the firmware for the NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e sensors changed during the course of the measurements (on 25/08/2022 for the NO\u003csub\u003e2\u003c/sub\u003e and on 26/01/2023 for the O\u003csub\u003e3\u003c/sub\u003e sensors), providing an opportunity to test the improvement to the measurements. The target diagrams provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the performance of these sensors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea for NO\u003csub\u003e2\u003c/sub\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb for O\u003csub\u003e3\u003c/sub\u003e) against measurements by the respective reference instruments before and after the firmware updates.\u003c/p\u003e \u003cp\u003eThe distance of each point from the centre of the circle corresponds to the normalized, by the standard deviation of the reference measurements, RMSE (nRMSE) described in Section 2.3. As shown in the target diagrams, the performance of the Vaisala AQT530 NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e sensors at both stations was improved after the firmware updates, as indicated by the lower nRMSE values. More specifically, the magnitude of the nRMSE vector decreased by 63.5% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 73.4% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the NO\u003csub\u003e2\u003c/sub\u003e sensors, and by 57.9% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 27.5% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the O\u003csub\u003e3\u003c/sub\u003e sensors following their firmware updates. Regarding the MBE, there was an improvement of 68.0% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 70.2% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the NO\u003csub\u003e2\u003c/sub\u003e sensors, and of 58.6% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 356.3% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the O\u003csub\u003e3\u003c/sub\u003e sensors. The CRMSE also improved by 57.0% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 60.6% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the NO\u003csub\u003e2\u003c/sub\u003e sensors and 61.19% (VSL\u003csub\u003eTRS\u003c/sub\u003e) and 71.6% (VSL\u003csub\u003eUBS\u003c/sub\u003e) for the O\u003csub\u003e3\u003c/sub\u003e sensors. Determining whether this overall performance improvement enables the sensors to capture the spatial and temporal differences discussed above, would require a more extended study in which the new firmware is used for at least 12 months with a new set of sensors, considering their limited lifespan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 CONCLUSIONS","content":"\u003cp\u003eWe have carried out air quality monitoring measurements using two Vaisala AQT530 monitors and reference instruments at a traffic and an urban background station in Nicosia, Cyprus, and investigated if the LCSs employed in the former can capture the spatial and temporal variabilities similarly to the latter. Initial measurements where both AQ monitors were collocated with reference instruments at the traffic station showed that only the CO and PM\u003csub\u003e10\u003c/sub\u003e measurements exhibit a high enough correlation and agreement with the reference measurements, suggesting a good sensor-to-sensor reproducibility. Considering that, only these sensors were tested further for their ability to capture spatial differences between the two stations.\u003c/p\u003e \u003cp\u003eAnalysis of the reference measurements shows that the mean concentrations of the pollutants at the two stations, over the entire study period and for each month separately, were statistically significantly different at a 95% confidence interval. On an hourly basis, the reference measurements also showed that there are statistically significant differences for certain hours of the day at the two stations. The respective low-cost measurements for CO and PM\u003csub\u003e10\u003c/sub\u003e were able to capture the significance of the spatial gradiences between the two stations for the entire study period and on monthly basis, with the exceptions of a few months depending on the sensor. On daily (workdays or weekends) or hourly basis, the ability of the LCS CO and PM\u003csub\u003e10\u003c/sub\u003e measurements to capture the spatial differences was higher in cases where the difference in the concentration of the pollutants at the two stations was higher (i.e., \u0026gt; 80 ppb for CO and \u0026gt;\u0026thinsp;10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eRegarding the temporal (i.e., monthly) variations, the CO and PM LCSs captured the month-to-month reference trend over the entire period, while the NO\u003csub\u003e2\u003c/sub\u003e, NO and O\u003csub\u003e3\u003c/sub\u003e sensors did not, mainly due to their sensitivity on the environmental conditions. PM and marginally the NO and NO\u003csub\u003e2\u003c/sub\u003e LCSs capture the reference month-to-month differences in more than 70% of the cases. However, considering that the latter fail to reproduce the overall trends, reporting values that are significantly different compared to the reference instruments, they can be excluded, leaving only the PM sensor as the most reliable indicator of temporal variabilities at the same station.\u003c/p\u003e \u003cp\u003eOverall, among all LCSs, the ones measuring PM appear to capture better than the others both the temporal and spatial resolutions (which is not a surprise given the more robust operating principle compared to the gas sensors) despite their relatively high cut-off diameter, while the CO sensors can be used to capture effectively mainly spatial differences. The CO LCSs managed to capture the monthly and diurnal trends/variabilities regardless of the environmental conditions, while the rest of the low-cost gas sensors appear to report measurements that are comparable to those from the reference instruments only at lower temperatures (\u0026lt;\u0026thinsp;20\u0026deg;C) and higher RH (\u0026gt;\u0026thinsp;55%).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.P. drafted the manuscript, which was reviewed and edited by SB and GB; RP performed the measurements; M.PI., M.PA., M.S. and C.S. provided the reference station data; R.P. analysed the data. The research was conceptualised by S.B., G.B.. The funding was acquired by J.S..\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the EMME-CARE project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 856612 and the Cyprus Government.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available in Zenodo:Papaconstantinou, R. (2024). Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13985490\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlphasense AAN106: Humidity Extremes: Drying Out and Water Absorption, Alphasense Application Note AAN106, https://www. alphasense.com/wp-content/uploads/2013/07/AAN_106.pdf (last access: 16/06/2023), 2013.\u003c/li\u003e\n\u003cli\u003eAQT530, 2023, Air Quality Transmitter AQT530 Datasheet, Available at: https://docs.vaisala.com/v/u/B211817EN-F/en-US, Last access: 10/08/2023.\u003c/li\u003e\n\u003cli\u003eRegional Agency for the Protection of the Environment of Lazio (ARPA Lazio), Regional Emission Inventory \u0026ndash; 2015 Emissions in the Lazio Region, [In Italian], Available at: http://www.arpalazio.gov.it/ambiente/aria/inventario.htm, Last accessed: 31/08/2020.\u003c/li\u003e\n\u003cli\u003eBaruah, A., Zivan, O., Bigi, A., Ghermandi, G.: Evaluation of low- cost gas sensors to quantify intra-urban variability of atmospheric pollutaTRS, Environ. Sci.: Atmos, 3, 830, https://doi.org/10.1039/d2ea00165a, 2023.\u003c/li\u003e\n\u003cli\u003eBerlin Air Quality Monitoring Network (Berliner Luftg\u0026uuml;temessnetz \u0026ndash; BLUME), AIR QUALITY PLAN FOR BERLIN \u0026ndash; 2ND UPDATE, Available at: https://www.berlin.de/sen/uvk/_assets/umwelt/luft/luftreinhaltung/luftreinhalteplan-2-fortschreibung/luftreinhalteplan_2019_en.pdf?ts=1666617271, 2019.\u003c/li\u003e\n\u003cli\u003eBezantakos, S., Costi, M., Barmpounis , K., Antoniou, P., Vouterakos, P., Keleshis, C., Sciare, J., Biskos, G., Qualification of the Alphasense optical particle counter for inline air quality monitoring, Aerosol Sci. Technol., 55, 361\u0026ndash; 370, https://doi.org/10.1080/02786826.2020.1864276, 2021.\u003c/li\u003e\n\u003cli\u003eB\u0026iacute;lek, J., B\u0026iacute;lek, O., Mar\u0026scaron;olek, P. and Bucek, P.: Ambient Air Quality Measurement with Low-Cost Optical and EC Sensors: An Evaluation of Continuous Year-Long Operation, J. Environ., 8(11), 114, https://doi.org/10.3390/environments8110114, 2021.\u003c/li\u003e\n\u003cli\u003eBisignano, A., Carotenuto, F., Zaldei, A., Giovannini, L.: Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway, Atm. Env., 275, 119008, https://doi.org/10.1016/j.atmosenv.2022.119008, 2022.\u003c/li\u003e\n\u003cli\u003eBorrego, C., Costa, A. M., Ginja, J., Amorim, M., Coutinho, M., Karatzas, K., Sioumis, Th, et al.: Assessment of air quality microsensors versus reference methods: the EuNetAir joint exercise, J. Atmos. Environ., 147 (2), 246\u0026ndash;263, https://doi.org/10.1016/j.atmosenv.2016.09.050, 2016.\u003c/li\u003e\n\u003cli\u003eBulot, F.M.J., Johnston, S.J., Basford, P.J., Easton, N.H.C., Apetroaie-Cristea, M., Foster, G.L., Morris, A.K.R., Cox, S.J., Loxham, M.: Long-term feld comparison of multiple low-cost particulate matter sensors in an outdoor urban environment, Sci. Rep., 9(1):7497, https://doi.org/10.1038/s41598-019-43716-3, 2019.\u003c/li\u003e\n\u003cli\u003eCabaneros, S.M., Calautit, J.K., Hughes, B.R.: A review of artificial neural network models for ambient air pollution prediction, Environ. Model. Softw., 119, 285-304, https://doi.org/10.1016/j.envsoft.2019.06.014, 2019.\u003c/li\u003e\n\u003cli\u003eCarslaw, K.S.: Aerosols and Climate, Elsevier, 1\u003csup\u003est\u003c/sup\u003e Edition, ISBN: 9780128231722, Published: August 19, 2022. \u003c/li\u003e\n\u003cli\u003eCastell, N., Dauge, F.R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., Bartonova, A.: Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293-302, http://dx.doi.org/10.1016/j.envint.2016.12.007, 2017.\u003c/li\u003e\n\u003cli\u003eCollier-Oxandale, A., Feenstra, B., Papapostolou, V., Zhang, H., Kuang, M., Der Boghossian, B., Polidori, A.: Field and laboratory performance evaluations of 28 gas-phase air quality sensors by the AQ-SPEC program, J. Atmos. Env., 220, https://doi.org/10.1016/j.atmosenv.2019.117092, 2020.\u003c/li\u003e\n\u003cli\u003eCrawford, B., Hagan, D.H., Grossman, I., Cole, E., Holland, L., Heald, C.L., Kroll, J.H.: Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kılauea eruption) using a low-cost sensor network, Proceedings of the National Academy of Sciences of the United States of America, 118(27), https://doi.org/10.1073/pnas.2025540118, 2021.\u003c/li\u003e\n\u003cli\u003eCui, H., Zhang, L., Li, W., Yuan, Z., Wu, M., Wang, C., Ma, J., Li, Y.: A new calibration system for low-cost Sensor Network in air pollution monitoring, Atmos. Pollut. Res., 12, 101049, https://doi.org/10.1016/j.apr.2021.03.012, 2021.\u003c/li\u003e\n\u003cli\u003eDepartment of Labour Inspection (DLI), Annual Air Quality Technical Report 2021, Available at: https://www.airquality.dli.mlsi.gov.cy//sites/default/files/2022-11/Etisia%20Techniki%20Ekthesi%202021.pdf, Last accessed: 23/08/2023.\u003c/li\u003e\n\u003cli\u003edeSouza, P., Kahn, R., Stockman, T., Obermann, W., Crawford, B., Wang, A., Crooks, J., Li, J., Kinney, P.: Calibrating networks of low-cost air quality sensors, Atmos. Meas. Tech., 15, 6309\u0026ndash;6328, https://doi.org/10.5194/amt-15-6309-2022, 2022.\u003c/li\u003e\n\u003cli\u003eEuropean Parliament and Council of the European Union, 2008. Directive 2008/50/EC of 21 May 2008 on ambient air quality and cleaner air for Europe. Official Journal of the European Union, L 152, pp.1-44. Available at: http://data.europa.eu/eli/dir/2008/50/oj [Accessed 04/09/2024].\u003c/li\u003e\n\u003cli\u003eFeinberg, S.N., Williams, R., Hagler, G., Low, J., Smith, L., Brown, R., Garver, D., Davis, M., Morton, M., Schaefer, J., Campbell, J.: Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors, Atm. Env., 213, 579-584, https://doi.org/10.1016/j.atmosenv.2019.06.026, 2019.\u003c/li\u003e\n\u003cli\u003eFrederickson, L.B., Sidaraviciute, R., Schmidt, J.A., Hertel, O., Johnson, M.S.: Are dense networks of low-cost nodes really useful for monitoring air pollution? A case study in Staffordshire, Atmos. Chem. Phys., 22, 13949-13965, https://doi.org/10.5194/acp-22-13949-2022, 2022.\u003c/li\u003e\n\u003cli\u003eGao, M., Cao, J., Seto, E.: A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM\u003csub\u003e2.5\u003c/sub\u003e in Xi\u0026apos;an, China, Environ. Pollut., 199, 56-65, http://dx.doi.org/10.1016/j.envpol.2015.01.013, 2015.\u003c/li\u003e\n\u003cli\u003eHe, Q., Wang, B., Liang, J., Liu, J., Liang, B., Li, G., Long, Y., Zhang, G., Liu, H.: Research on the construction of portable electrochemical sensors for environmental compounds quality monitoring, Mater. Today Adv., 17, 100340, https://doi.org/10.1016/j.mtadv.2022.100340, 2023.\u003c/li\u003e\n\u003cli\u003eHeimann, I., Bright, V.B., McLeod, M.W., Mead, M.I., Popoola, O.A.M., Stewart, G.B., Jones, R.L.: Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors, Atmos. Environ., 113, 10-19, http://dx.doi.org/10.1016/j.atmosenv.2015.04.057, 2015.\u003c/li\u003e\n\u003cli\u003eJuginovic, A., Vukovic, M., Aranza, I., Bilos, V.: Health impacts of air pollution exposure from 1990 to 2019 in 43 European countries, J. Sci. Rep., 11, https://www.nature.com/articles/s41598-021-01802-5, 2021.\u003c/li\u003e\n\u003cli\u003eKaragulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506, 2019.\u003c/li\u003e\n\u003cli\u003eKezoudi, M., Tesche, M., Smith, H., Tsekeri, A., Baars, H., Dollner, M., Estell\u0026eacute;s, V., B\u0026uuml;hl, J., Weinzierl, B., Ulanowski, Z., M\u0026uuml;ller, D., Amiridis, V.: Measurement report: Balloon-borne in situ profiling of Saharan dust over Cyprus with the UCASS optical particle counter, Atmos. Chem. Phys., 21, 6781-6797, https://doi.org/10.5194/acp-21-6781-2021, 2021.\u003c/li\u003e\n\u003cli\u003eKim, J., Shusterman, A.A., Lieschke, K.J., Newman, C., Cohen, R.C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atms. Meas. Tech., 11, 1937-1946, https://doi.org/10.5194/amt-11-1937-2018, 2018.\u003c/li\u003e\n\u003cli\u003eKuntic, M., Kuntic, I., Hadad, O., Lelieveld, J., M\u0026uuml;nzel, T., Daiber, A.: Impact of air pollution on cardiovascular aging, Mech. Ageing Dev., 111857, https://doi.org/10.1016/j.mad.2023.111857, 2023.\u003c/li\u003e\n\u003cli\u003eLewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller, S. J., Smith, K. R., Buckley, J. W., Ellis, M., Gillot, S. R., and White, A.: Evaluating the performance of low cost chemical sensors for air pollution research, J. Faraday Discuss., 189, 85-103, https://doi.org/10.1039/c5fd00201j, 2016.\u003c/li\u003e\n\u003cli\u003eLondon Air, 2018, Environmental Research Group (ERG), Imperial College London, Available at: https://www.londonair.org.uk/london/asp/publicbulletin.asp?la_id=7\u0026amp;MapType=Google, Last accessed: 05/10/2023.\u003c/li\u003e\n\u003cli\u003eMadrid Air Quality portal, Annual air quality report 2022, Available at: https://airedemadrid.madrid.es/UnidadesDescentralizadas/Sostenibilidad/CalidadAire/Publicaciones/Memorias_anuales/Ficheros/MEMORIA_2022_02.pdf, Last accessed: 05/10/2023.\u003c/li\u003e\n\u003cli\u003eMarx, E., and Mulholland, G.: Size and Refractive Index Determination of Single Polystyrene Spheres., J. Res. Nat. Bur. Stand., 321\u0026ndash;338, 88, 5, https://doi.org/10.6028/jres.088.016, 1983.\u003c/li\u003e\n\u003cli\u003eMcMurry, P.H: A review of atmospheric aerosol measurements, Atmos. Environ., 34, 12-14, 1959-1999, https://doi.org/10.1016/S1352-2310(99)00455-0, 2000.\u003c/li\u003e\n\u003cli\u003eMinistry of Environment and Energy, Annual Air Quality Report 2022, Last accesses: 23/08/2023, Available at: https://ypen.gov.gr/perivallon/poiotita-tis-atmosfairas/ektheseis/#\u003c/li\u003e\n\u003cli\u003eNagendra, S.S.M., Yasa, P.R., Narayana M.V., Khadirnaikar, S., Rani, P.: Mobile monitoring of air pollution using low cost sensors to visualize spatiotemporal variation of pollutaTRS at urban hotspots, Sustain. Cities Soc., 44, 520-535, https://doi.org/10.1016/j.scs.2018.10.006, 2019.\u003c/li\u003e\n\u003cli\u003ePang, X., Shaw, M.D., Lewis, A.C., Carpenter, L.J., Batchellier, T., Electrochemical ozone sensors: A miniaturised alternative for ozone measurements in laboratory experiments and air-quality monitoring, Sens. Actuators B Chem., 240, 829-837, https://doi.org/10.1016/j.snb.2016.09.020, 2017.\u003c/li\u003e\n\u003cli\u003ePapaconstantinou, R., Demosthenous, M., Bezantakos, S., Hadjigeorgiou, N., Costi, M., Stylianou, M., Symeou, E., Savvides, C., Biskos, G., Field Evaluation of Low-cost Electrochemical Air Quality Gas Sensors at Extreme Temperature and Relative Humidity Conditions, Atm. Meas. Tech., 16, 3313\u0026ndash;3329, https://doi.org/10.5194/amt-16-3313-2023, 2023.\u003c/li\u003e\n\u003cli\u003ePet\u0026auml;j\u0026auml;, T., Ovaska, A., Fung, P.L., Poutanen, P., Yli-Ojanper\u0026auml;, J., Suikkola, J., Laakso, M., M\u0026auml;kel\u0026auml;, T., Niemi, J.V., Keskinen, J., J\u0026auml;rvinen, A., Kuula, J., Kurppa, M., Hussein, T., Tarkoma, S., Kulmala, M., Karppinen, A., Manninen, H.E. and Timonen, H.: Added Value of Vaisala AQT530 Sensors as a Part of a Sensor Network for Comprehensive Air Quality Monitoring, Front. Environ. Sci., 9, 719567, https://doi.org/10.3389/fenvs.2021.719567, 2021.\u003c/li\u003e\n\u003cli\u003ePopoola, O.A.M., Carruthers, D., Lad, C., Bright, V.B., Mead, M.I., Stettler, M.E.J., Saffell, J.R., Jones, R.L.: Use of networks of low cost air quality sensors to quantify air quality in urban settings, Atmos. Environ., 194, 58-70, https://doi.org/10.1016/j.atmosenv.2018.09.030, 2018.\u003c/li\u003e\n\u003cli\u003eRaheja, G., Nimo, J., Appoh, E. K.-E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R.E., Penn, S.L., Giordano, M.R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E.N., Borketey, M.A., Hughes, A.F., Westervelt, D.M.: Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM\u003csub\u003e2.5\u003c/sub\u003e Monitoring in Accra, Ghana, Environ. Sci. Technol., \u003cem\u003e57\u003c/em\u003e (29), 10708-10720, https://doi.org/10.1021/acs.est.2c09264, 2023.\u003c/li\u003e\n\u003cli\u003eSchneider, P., Castell, N., Vogt, M., Dauge, F.R., Lahoz, W.A., Bartonova, A.: Mapping urban air quality in near real-time using observations from low cost sensors and model information, Environ. Int., 106, 234-247, http://dx.doi.org/10.1016/j.envint.2017.05.005, 2017.\u003c/li\u003e\n\u003cli\u003eSpinelle, L., Gerboles, M., \u0026amp; Aleixandre, M.: Performance Evaluation of Amperometric Sensors for the Monitoring of O3 and NO2 in Ambient Air at ppb Level, Procedia Engineering, 120, 480\u0026ndash;483. https://doi.org/10.1016/j.proeng.2015.08.676, 2015a.\u003c/li\u003e\n\u003cli\u003eSpinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide, Sens. Actuat. B, 215, 249\u0026ndash;257, https://doi.org/10.1016/j.snb.2015.03.031, 2015b.\u003c/li\u003e\n\u003cli\u003eStetter, J., Li, Jing: Amperometric gas sensors e a review. Chem. Rev. 108, 2, 352-366. http://dx.doi.org/10.1021/cr0681039, 2008.\u003c/li\u003e\n\u003cli\u003eTEOM 1405-DF, Thermo Fisher Scientific Inc., Last accessed: 08/12/2023, Available at: https://assets.thermofisher.com/TFS-Assets/CAD/Datasheets/1405-df-teom-ambient-particulate-monitor-datasheet.pdf, 2020.\u003c/li\u003e\n\u003cli\u003eTitos, G., Cazorla, A., Zieger, P., Andrews, E., Lyamani, H., Granados-Mu\u0026ntilde;oz, M.J., Olmo, F.J., Alados-Arboledas, L.: Effect of hygroscopic growth on the aerosol light-scattering coefficient: A review of measurements, techniques and error sources, 141, 494-507, https://doi.org/10.1016/j.atmosenv.2016.07.021, 2016.\u003c/li\u003e\n\u003cli\u003eWei, P. Ning, Z., Sun, L., Yang, F., Wong, K.C., Westerdahl, D., Louie, P.K.K.: Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring, Sensors, 18(2), 59, https://doi.org/10.3390/s18020059, 2018.\u003c/li\u003e\n\u003cli\u003eYukhymchuk, Y., Milinevsky, G., Syniavskyi, I., Popovici, I., Unga, F., Sciare, J., Marenco, F., Pikridas, M., and Goloub, P.: Atmospheric Aerosol Outbreak over Nicosia, Cyprus, in April 2019: Case Study, 13, 1997, https://doi.org/10.3390/atmos13121997, 2022.\u003c/li\u003e\n\u003cli\u003eZhu, Y., Hinds, W.C., Kim, S., Shen, S., Sioutas, C.: Study of ultrafine particles near a major highway with heavy-duty diesel traffic, 36, 27, 4323-4335, https://doi.org/10.1016/S1352-2310(02)00354-0, 2002.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5349649/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5349649/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLow-cost gas and particle sensors can significantly increase the spatial coverage of Air Quality (AQ) monitoring networks in urban settings. Considering that the accuracy of such sensors is not high enough to replace reference instruments for AQ monitoring, the question is whether they can be used to capture spatial differences among different stations, as well as temporal trends and month-to-month variabilities at a specific location. To investigate that, we carried out measurements over a period of 19 months with two Vaisala AQ Transmitters-Monitors (Model AQT530), collocated with reference-grade instruments, in two AQ monitoring stations in Nicosia: an urban traffic and an urban background station. The AQ monitors employ Low-Cost Sensors (LCSs) for gaseous pollutants (i.e., CO, NO\u003csub\u003e2\u003c/sub\u003e, NO, and O\u003csub\u003e3\u003c/sub\u003e) and Particulate Matter (PM). Statistical analysis of the reference measurements shows that the mean concentrations of the pollutants at the two stations, determined over the entire study period and for each month separately, were significantly different. Analysis of the LCS measurements showed that that the reproducibility of the NO\u003csub\u003e2\u003c/sub\u003e, NO, O\u003csub\u003e3\u003c/sub\u003e, and PM\u003csub\u003e2.5\u003c/sub\u003e sensors, over a period when these were co-located at the traffic station, is poor, excluding them from further investigating their ability to capture spatial differences between different stations. The CO and PM\u003csub\u003e10\u003c/sub\u003e measurements from the AQ monitors effectively captured the differences in pollutant concentrations between the two stations when averaged over the entire study period or on a monthly basis, with few exceptions during specific months depending on the sensor. These LCSs were also able to capture concentration differences between the two stations on a daily or monthly basis, as long as those were above a certain threshold for each pollutant. The CO and PM sensors captured the month-to-month trend over the entire period of the measurements, similarly to the reference instruments, while the NO\u003csub\u003e2\u003c/sub\u003e, NO and O\u003csub\u003e3\u003c/sub\u003e sensors did not, mainly due to their sensitivity to the environmental conditions. Despite that, all sensors captured the statistical significance of the month-to-month concentration differences at the same station, with the PM\u003csub\u003e2.5\u003c/sub\u003e measurements showing the highest capability of doing so in accordance with the reference instruments.\u003c/p\u003e","manuscriptTitle":"Assessing Spatial and Temporal Urban Air Quality Variabilities with the Vaisala AQT530 Monitor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 07:02:41","doi":"10.21203/rs.3.rs-5349649/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c977604b-0da4-4ad8-8a6a-707104d1f1ed","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40315055,"name":"Earth and environmental sciences/Climate sciences"},{"id":40315060,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-01-24T08:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 07:02:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5349649","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5349649","identity":"rs-5349649","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}