The nodes include low-cost air pollution sensors, signal processing, and communications. The units, 88×88×90 mm, are assembled by AirLabs into weatherproof enclosures with full exposure to ambient air and are set up to report measurements to a cloud hosted by Amazon Web Services. The low-cost air pollution sensor nodes are generation 4P and are referred to as AirNode, AirNode4PX, or 4PX, with X being the node number. Each AirNode includes sensors for measuring NO2 (NO2-B43F from Alphasense Ltd.) and O3 (MiCS-6814 from SGX Sensortech) as well as PM2.5 and PM10 (SDS-011 from Nova Fitness Co.) at a 1 min time resolution. In addition, each node is equipped with a control board and micro-controller unit (ESP32) for programming the sensors. The AirNodes were laboratory tested in Copenhagen, Denmark, to validate their response and obtain laboratory-based calibration coefficients, which are used to interpret the preliminary data. After laboratory calibration, the AirNodes were shipped to Newcastle-under-Lyme in Staffordshire, UK, and were mounted 2.5 to 3 m above street level on lamp posts which also provided power as shown in Fig. . Since the study focuses on NO2 and PM2.5, a brief description of the sensors is given below.

The SDS-011 sensor is a low-cost air pollution sensor measuring PM2.5 and PM10. Its principle of operation is based on light scattering , where particle density distribution is determined using the intensity distribution patterns produced when particles scatter a laser beam . The sensor module includes a fan to ensure a continuous flow of air through the sensor chamber . An algorithm converts the particle density distribution into particle mass, and it can measure the particle density distribution between 0.3 and 10 µm .

For NO2 measurements, the NO2-B43F sensor is used. This is an amperometric electrochemical gas sensor containing four electrodes, where the principle of operation is based on electrochemistry . When the working electrode (WE) is exposed to ambient air, the target gas can diffuse onto the surface of the electrode, where it is chemically reduced, resulting in a change in current. The counter electrode balances the current, and the reference electrode sets the operating potential of WE. The fourth electrode is an auxiliary electrode (AE) and has the same structure as WE but is not exposed to ambient air and hence is not affected by the target gas concentration, only by environmental parameters such as temperature. Therefore, the difference in voltage between the WE and AE corresponds to changes in target gas concentration at the electrochemical cell surface. A transimpedance amplifier converts the currents from the electrochemical cell into a voltage. The voltage is amplified further by a non-inverting operational amplifier, and then a 16-bit analogue-to-digital (A/D) converter (ADS1115) samples the output and produces a digital reading of the voltage level. This is used by the microprocessor to calculate the actual gas concentration . To minimize possible cross-interference from ozone, the NO2 sensors were fitted with integrated catalytic ozone filters (MnO2 filters). The performance of these filters was verified in the laboratory, and the NO2 sensors showed no significant response to ozone in the range of 0–100 ppb. Cross-interferences from other common gas pollutants were not considered important based on prior studies .

The calibration of the electrochemical sensors measuring NO2 is known to vary at high (>20 ∘C) and low (<0 ∘C) temperatures and with rapid temperature change . Therefore, we apply a correction with coefficients determined by using a linear regression model: 1NO2corT=a0+a1⋅T+a2⋅dT/dt+a3⋅NO2(dV), where NO2 (dV) is the raw output obtained by the Alphasense NO2 cell. The NO2 (dV) readings are found from the voltage change in cell 2, which is determined by the difference between the WE and AE outputs, WE2v, and AE2v: 2NO2(dV)=WE2v-AE2v. T is filtered temperature data obtained from the nearest reference station. Filtered temperature represents the temperature reading when ambient temperature exceeds 10 ∘C and is transformed according to 3f(t)=0ifT<10,T-10otherwise, and dT/dt is the rate of change in the filtered temperature. The temperature threshold of 10 ∘C was chosen because the internal temperatures of the LCS nodes often exceed the ambient temperatures, and the performance of the correction was sufficient. The linear regression coefficients, a0, a1, a2, and a3, are calculated using the method of multiple least squares, separately for each AirNode . In this formula, a3 is a measure of the sensor's sensitivity, and a0 is the offset of the sensor, whereas a1 and a2 are temperature correction coefficients.

All electrochemical sensors have a different inherent sensitivity, and hence the NO2 readings need to be scale-corrected. The scale correction is carried out by multiplying the temperature-corrected NO2 readings (NO2 (corT)), from each AirNode, by α, which is the ratio between the 0.80 and 0.20 quantiles of the NO2 readings obtained from the AirNodes (Qdiff,AirNode) and from the reference (Qdiff,Reference). The reference is the NO2 readings obtained by chemiluminescence from the reference-grade instrument at the AQMS at Stoke-on-Trent centre, 4.1 km from the network, from the same period as the measurements took place. All data from the reference station are used for the correction. The difference between the 0.80 and 0.20 quantiles is a proxy for the variation obtained in the measurements. 4Qdiff,AirNode=Q0.80,AirNode-Q0.20,AirNode5Qdiff,Reference=Q0.80,Reference-Q0.20,Reference6α=Qdiff,Reference/Qdiff,AirNode7NO2corT,S=NO2corT⋅α

The offsets of the readings are determined by calculating the difference between the 0.25 quantile (Q0.25) obtained from each AirNode (Q0.25,AirNode) and from the reference (Q0.25,Reference). Hence, the offset of the temperature- and scale-corrected reading (NO2 (corT,S)) is adjusted by subtracting the calculated offset (β). The reference used in the offset correction is the same as the one used for the scale correction. The 0.25 quantile is a proxy for the measured background concentration. 8β=Q0.25,AirNode-Q0.25,Reference, 9NO2(cor)=NO2corT,S-β, where NO2 (corT,S) is the temperature- and scale-corrected NO2 reading, and NO2 (cor) is the temperature-, offset-, and scale-corrected NO2 reading.

Regarding the SDS-011 PM2.5 readings, outliers were removed by excluding all values exceeding 5 times the standard deviation. Scale and offset correction was performed for PM2.5, similarly to the one for the NO2 readings. However, there was no significant difference between the corrected and uncorrected PM2.5 readings since the PM2.5 readings were already highly correlated (mean R2=0.72) with the reference readings from the Stoke-on-Trent centre.

The data obtained from the network are compared with data from the three nearest regulatory air quality monitoring stations: the roadside monitoring station at the Stoke-on-Trent A50 roadside (52.980436∘ N, 2.111898∘ W; 8.7 km from the network), the urban background monitoring station at Stoke-on-Trent centre (53.028210∘ N, 2.175133∘ W; 4.1 km from the network), and the regional background monitoring station at Ladybower (53.403370∘ N, 1.752006∘ W; 54 km from the network). We do not expect perfect agreement, but nonetheless the exercise is useful.

Ladybower is located in the Peak District National Park around 800 m to the south-west of the Ladybower reservoir. The nearest road is 20 m from the station and is only used by the nearby farmsteads. The surrounding area is mainly open moorland. The urban background monitoring station is located in Stoke-on-Trent and is in the northern part of downtown Hanley. This station is located 5 m from a road connected to a busy multi-story car parking facility (50 m from the monitoring station). The surrounding area is open grass, with a few trees and commercial properties. The A50 Potteries Way is a busy ring road which lies approximately 130 m to the north-east of the monitoring site. The roadside monitoring station is located between the main road and a parallel side road, near a pedestrian footbridge, beside the dual-carriageway A50 through Stoke. All three AQMSs are equipped with instruments for measuring NO2 by chemiluminescence, but only the Stoke-on-Trent centre measures PM2.5. Hourly air pollution data from each monitoring station were manually downloaded using the UK-Air data selector .

Spectral analysis is widely used for investigating cycles and variations of pollutants in time series to reveal the sources of pollution . Within spectral analysis, the Fourier transform is a powerful tool for analysing time series including periodicities and rate of change. To use the method, it is necessary to overcome obstacles including the often unevenly spaced time points in time series due to technical and practical problems during monitoring . The unequally spaced or missing data can be circumvented by applying the fast Fourier transform after filling the gaps and missing values with the mean. In addition, the linear trend in the time series is removed by subtracting the average concentration obtained by each LCS. The periodogram for a finite time series is calculated as the square of the magnitude of X: 10Φνk=X(k)2=1N∑t=0N-1xte-2πiνkt2, where k=0, 1, …, N-1, and N is the number of observations, xt is the time series, and νk=k/N. The periodogram indicates the strength of the signal as a function of frequency, while its spectrum over the frequency range corresponds to the variance of the time series data. Parseval's theorem states that the energy, or in this case intensity, is conserved during Fourier transformation. Thus, the contribution of the different pollution sources can be quantified by integrating the peaks in the periodograms .

There is a relationship between temporal and spatial scales of the different air pollutants. Rapid, short-term fluctuations of the pollutant concentrations happen as a result of local phenomena, e.g. local-scale dispersion, local emissions, and short-term atmospheric chemistry. Rapid changes contribute to the periodogram at high frequencies, which in this work are defined as above 0.0417 h-1, i.e. events with a frequencies higher/shorter than 1 d. This is referred to as the “local” contribution to the pollutant concentration. The local cutoff is chosen based on the European Environment Agency's definition of a local timescale . The seasonal changes in the emissions and long-range transport of the pollution contribute to the periodogram at low frequencies (<0.0139 h-1), i.e. events with a frequency lower/longer than 3 d. This is then referred to as the “regional” contribution to the pollutant concentration. In this model, intermediate frequencies are due to the “urban” contribution to the pollutant concentration. The cutoff frequency for the regional contribution is based on the intercontinental transport, which occurs on timescales of the order of 3 d to 1 month . As noted in the introduction, the mixing of pollution with time provides an upper limit on frequency for distant sources; only local sources can give a high-frequency signal. The above-mentioned definitions are illustrated in Fig. . One of the properties of diffusion is that a pulse of pollution will propagate in a Gaussian concentration profile depending on the diffusion constant and time. Under the Fourier transform, a Gaussian is mapped onto another Gaussian with a different width. The transform of a wide function is narrow and vice versa. By integrating periodograms in the three different frequency bins by the equations below, the relative contribution of local, urban, and regional pollution of the LCS data can be quantified. 11JN(g)=∫νstartνendgνkΦνkdνk, where νstart and νend are the start and end frequencies in the chosen frequency bins, which is elaborated below. 12RegionalN(g)=∫00.0139gνkΦνkdνkUrbanN(g)=∫0.01390.0417gνkΦνkdνkLocalN(g)=∫0.04170.5gνkΦνkdνk g is non-negative and square-integrable with respect to the Lebesgue measure on νk. After the relative contributions are calculated for each LCS node, the average concentration together with the standard deviation can be calculated across the AirNode network to illustrate how much local pollution the network is seeing on average and how much variation is seen across all AirNodes.

The first requirement is to establish the fidelity of the monitoring network.

Air quality data for NO2 and PM2.5 measured at the different sites during 2020 and 2021 were analysed. For this section, only 1 year of data (1 August 2020 to 1 August 2021) is used to compare with official guidelines. Descriptive statistics of the air quality measurements are presented in Table . The mean concentrations are compared with the WHO's recently updated European AQGs and the legally binding EU standards; see Table . It should be noted that the legally binding values for annual means are defined for 1 January to 31 December. The mean annual NO2 and PM2.5 concentrations across the network exceed the updated WHO guidelines by 7 and 3 µg m-3 for NO2 and PM2.5, respectively. All sites have days where the daily average NO2 and PM2.5 concentrations exceed the WHO daily average AQG limits during the period. However, none of the sites are above the legally binding EU standards.

The values obtained from the network can be analysed in relation to the values reported by AQMSs, as long as the significant distance between the measurement locations is kept in mind. The values from the network are compared with the three AQMSs: the monitoring station at the Stoke-on-Trent A50 roadside, the urban background monitoring station at the Stoke-on-Trent centre, and the regional background monitoring station at Ladybower. Only the urban background station at the Stoke-on-Trent centre is reported in PM2.5. Concentrations of NO2 and PM2.5 (when available) are averaged within the same period as the AirNodes in the network (i.e. 1 August 2020 to 1 August 2021), and the descriptive statistics are shown in Table . The mean concentrations obtained from the network show similar values for NO2 and PM2.5 to those seen at the urban background station. On average, while the network sees lower NO2 values than the roadside monitoring station (21.4 µg m-3), there is an NO2 excess relative to the regional background exposure (12.5 µg m-3). In an environment such as a city with an elevated urban background concentration, exposure to air pollution in micro-environments can cause exceedance of recommended threshold values for many individuals, in addition to the dangers of transient and continued exposure.

Figure  shows the temporal variation in NO2. On weekdays, the NO2 concentration increases in two time periods during the day, with peaks at 07:00 and 18:00 LT. On weekends, the NO2 concentration rises steadily throughout the day. There is a notable decrease in concentration during the weekend compared to the weekdays at all sites. For both weekends and weekdays, the NO2 concentration is lowest at night. The two time periods with increased NO2 concentration during the weekdays are typically periods of increased traffic during morning and afternoon rush hours when people commute to and from work . Thus, traffic likely drives this observed variation, in line with the declining NO2 concentration during the night and over the weekend.

In terms of monthly trends, Fig.  displays the monthly average of the NO2 concentration measured by one of the AirNodes together with the monthly readings from the nearest urban background AQMS (Stoke-on-Trent centre). All AirNodes have the same tendencies, so Fig.  is characteristic of all AirNodes. The readings from the AirNode and the AQMS follow the same trends, with the highest NO2 concentrations in the spring and the winter.

Wind speed and direction have been shown to provide essential information that can help identify source locations . The description of variation with wind direction and wind speed on a specific street (the so-called street canyon effect) is given in . Bivariate polar plots are a powerful tool for source characterization including mean pollutant concentrations for specific wind speed and direction bins . In these plots wind direction is displayed from 0 to 360∘ clockwise on the angular axis, and wind speed is shown on the radial scale.

The wind speed and direction data used in this study are shown as a wind rose in Fig. . The wind rose shows that the prevailing winds come from the south and north-west during the measurement period. To assess spatially resolved source patterns, bivariate polar plots of the NO2 and PM2.5 are investigated. The bivariate polar plots for each pollutant for all sites are shown in Fig. . Reddish colours represent higher values compared to the blueish ones.

The bivariate polar plots show patterns that depend on deployment location. AirNode4P23, AirNode4P19, and AirNode4P02 are located in the southern part of the ring road, and they display similar patterns in their bivariate polar plots. Their surroundings are almost identical, and the traffic influence on their readings is similar. The nodes located in the northern part of the ring road have different patterns relative to the ones in the southern area. They experience the highest values at lower wind speeds. When peak concentrations occur at low wind speeds, it suggests local sources. For example, in a street canyon, there is both a direct and recycled contribution to the concentration, where the relative size of these two contributions depends on whether the measuring site at a given time is on the leeward or windward side of the street. AirNode4P10 is located in front of a school, and at lower wind speeds or with westerly wind, elevated levels of NO2 were observed. In general, the highest concentrations are observed at low wind speeds, where no whirlwind is formed inside the street, independent of wind directions, or when the measuring site is on the leeward side of the street (in relation to the whirlwind). In the latter case, pollution from the traffic in the specific section of the street will be led directly to the measuring site, at the same time as there is a contribution due to trapping of pollution within the limited volume of the whirlwind.

Higher NO2 values are correlated with wind speed and the orientation of the road. The traffic comes from the ring road area and continues through St Paul's Road. Near the school, traffic stops frequently and accelerates and idles while children are being dropped off and picked up. The lowest values of NO2 are seen at higher wind speeds with north-westerly winds. The bivariate polar plots for AirNode4P01 and AirNode4P05 show similar patterns, with the highest concentrations found for easterly and south-westerly winds, whereas the lowest concentrations were seen with westerly and south-westerly winds. Higher-speed south-westerly winds contributed to the peak concentrations at these locations. A wide-open parking area is located next to the ring road in that direction, which could explain the elevated concentrations.

The wind speed dependence of concentrations in a street canyon can be complex, as there are opposing effects: higher wind speeds lead to more O3 but also more dilution of NOx (NO + NO2). High wind speeds will therefore lead to lower NO2, while at low wind speeds, NO2 formation is limited by O3, which goes towards zero in the street . Bivariate polar plots are good at revealing these interrelationships. The wind speed dependence can help distinguish sources from one another. When several measurement sites are available, polar plots can triangulate different sources . As expected, NO2 is dominated by local emissions, and peak values mainly occur for low wind speeds, where elevated concentrations were observed due to accumulation and lack of dispersion. The most obvious features of NO2 bivariate polar plots are that the elevated levels are attributable to the orientation of the road or the place with the highest traffic density.

Relative to the bivariate polar plots of NO2, the bivariate polar plots of PM2.5 do not show as much variation across the network. Generally, the highest concentrations of PM2.5 are seen for south-easterly winds and higher wind speeds. This is confirmed by the frequency spectrum showing slow changes consistent with large air masses. This indicates that particles originate from long-range transport. The bivariate polar plots for PM2.5 also suggest that the locally sourced particulate matter is present, shown by the elevated concentrations at low wind speeds, where the atmospheric conditions are more stable.

In general, sites across the sensor network show a variation in their bivariate polar plots (however, more for NO2 than for PM2.5) due to the different pollution sources. Thus, there are additional benefits of multi-sensor node measurements for characterizing sources in detail, especially when combining them with meteorological information.

Figure  shows data from the urban background AQMS at the Stoke-on-Trent centre at a time resolution of 1 h . Data from one of the AirNodes with a time resolution of 30 min are shown for comparison. The raw readings from the AirNodes have a time resolution of 1 min; however, the temperature correction aggregates the data into 30 min bins. Still, with a time resolution of 30 min, we see more local variability in the data compared to the readings from the reference station. The data have a measurement density in both time and space, which cannot be achieved using current conventional measurement methods. As seen in Fig. , the readings from the AirNode and the AQMS follow the same trend, but the correlation of determination is only 0.28. This is expected, since the AQMS is located around 4 km from the AirNode network. However, increasing the time resolution will increase the correlation of determination; i.e. a time resolution of 3 h results in a correlation of determination of 0.38, and a time resolution of 1 d yields 0.63.

Spectral analysis is performed on the air pollution data to investigate its hidden periodicities and to quantify their magnitude. The contributions of local and regional sources to the pollution concentrations are determined based on the determined amplitudes and frequencies. The local sources are shown in the high-frequency periodogram, and the regional or long-range sources are revealed in the low-frequency periodogram. Note however that local sources may be present in both the low- and high-frequency regions. For example, in an urban street, the traffic patterns follow stable patterns with daily and weekly periodicities. Holiday periods follow their own pattern, and for wood smoke, emissions follow variations in outdoor temperature. By comparing the spectra for the different pollutants measured by the same AirNode, information on the sources can be revealed. If the emission sources for the different pollutants are the same, similar cyclic patterns can be expected. The differences in the pollution spectra can indicate a contribution from the different pollutant sources or the presence of chemical transformation since all other conditions are identical.

Spectral analysis is performed on NO2 data from three different types of AQMSs to illustrate how periodograms vary depending on location. These AQMSs are (1) regional background (Ladybower), (2) urban background (Stoke-on-Trent centre), and (3) street (Stoke-on-Trent A50 roadside). The three periodograms are shown in Fig. . While all three periodograms have significant peaks in the low-frequency region, only the urban background and street AQMSs have significant peaks in the high-frequency region. We conclude that these high-frequency peaks are due to the proximity and strength of local NO2 sources.

Figure  displays the periodograms for NO2 and PM2.5 measured by AirNode4P01. The periodogram of NO2 features three distinct peaks at 0.125, 0.084, and 0.042 h-1 corresponding to periods of 8, 12, and 24 h, respectively. In addition, one peak is identifiable in the high-frequency region at 0.17 h−1 (6 h). In the low-frequency region, there are multiple peaks close to each other; however, the peaks corresponding to 5 d (0.0083 h-1), 1 week (0.0061 h−1), and 1 month (0.00135 h−1) can still be identified. All these cycles can be related to local sources of pollution, e.g. traffic or meteorological changes. Peaks located in the low-frequency region can originate from changes over either a synoptic or larger scale. The highest intensity occurs in the high-frequency region since most of the NO2 originates from local sources. The daily changes in NO2 concentrations can be associated with the daily changes in traffic from nearby roads and the diurnal variation caused by sunlight. Weekly periodicity may also originate from changes in traffic.

The periodogram for PM2.5 (see Fig. ) features one distinct peak in the high-frequency region at 0.042 h-1 (24 h) and a prominent peak at 0.084 h-1 (12 h). Besides these two peaks, most peaks are seen in the low-frequency region of the periodogram, which is expected since PM2.5 is dominated by long-range transport and non-local sources. However, the contribution by PM from a nearby road can originate from traffic, since vehicles, in general, can re-suspend particles from the road into the air, and abrasion from brakes and tires also produces PM .

Periodograms for the rest of the AirNodes in the network show results similar to the ones shown in Fig. , with small changes in position and amplitude at specific locations. Conclusions regarding trends in pollution sources can be drawn by examining the relative contributions from local, urban, and regional sources. Figure  shows the calculated percentages of local, urban, and regional contributions for the AirNodes as well as for the three different types of AQMSs. The results for the network indicate that local emissions are the most important source of NO2 with an average of 54.3±4.3 %, whereas PM2.5 is mainly due to regional sources (62.1±3.5 %). For NO2, urban sources contribute 14.3±1.9 % and regional sources 31.2±4.5 %. For PM2.5, urban sources contribute 20.0±1.2 % and local sources 17.9±3.2 %.

As expected, the regional background AQMS shows the highest relative contribution from regionally sourced NO2, and the street AQMS has the highest level of locally sourced NO2. The AirNodes in the network show a distribution of contributions.

The results obtained for both NO2 and PM2.5 reveal contributions of short-term (12 and 24 h) and long-term fluctuations. The contributions at low frequencies are significantly different between the two pollutants, indicating that temporal variations are influenced by different processes. The methodology is a powerful tool for analysing the causes of air pollution.