The ARISense monitoring package (Fig. S1 in the Supplement) integrated the following sensors from Alphasense Ltd., UK: carbon monoxide (CO-B4), nitric oxide (NO-B4), nitrogen dioxide (NO2-B43F), total oxidants (Ox-B421), and the OPC-N2 optical particle counter. The ARISense package reported voltage readings from electrochemical gas sensor working electrodes (WEs) and auxiliary electrodes (AEs). Sensor differential voltage (ΔV) was calculated as WE–AE. The Alphasense OPC-N2 recorded counts in 16 size bins spanning particle diameters from 0.38 to 17.5 µm, meaning the OPC-N2 primarily measures coarse-mode aerosol particles (>2 µm) and some accumulation-mode (0.1 to 2 µm) aerosol particles (Badura et al., 2018; Crilley et al., 2018; Sousan et al., 2016). Although the OPC-N2 has embedded algorithms to convert count measurements into mass concentrations of PM1.0, PM2.5, and PM10 (particulate matter with aerodynamic diameters less than 1.0, 2.5, and 10 µm, respectively), the bin count data were manually integrated, converted to number concentration (cm-3) assuming unity measurement efficiency across the bin range and then to mass concentration assuming spherical particles with uniform density (1.65 g cm-3). The values reported for PM2.5 are PM2. The location of the adjacent bin separations at 2.0 and 2.99 µm did not allow for direct estimates of PM2.5. However, this was only one of many contributing sources of error in approximating true mass concentration with the Alphasense OPC-N2. Given the minimum cut-off diameter, we were unable to measure (nor did we try to estimate) the mass from particles smaller than 0.38 µm.

We used four ARISense monitors in this study: serial numbers ARI013, ARI014, ARI015 (v1.0, 2017), and ARI023 (v2.0, 2018). The monitors were powered by solar panels charging external batteries and recorded data to an internal USB device. Details and images are provided in Sect. S1 of the Supplement. Additional environmental and meteorological sensors (i.e., T, RH, pressure, solar intensity, and noise) and the system design are described in Cross et al. (2017).

Gas concentration measurements for NOx/NO/NO2 (Teledyne Model T200UP), CO (Thermo Scientific Model 48i-TLE), and ozone (Ecotech Federal Equivalent Method instrument) were obtained from reference instruments operated by the North Carolina Department of Environmental Quality (NC-DEQ) and the U.S. EPA.

The semi-reference MicroPEM (RTI International) instrument was used to assess the performance of the OPC-N2 in Malawi. The MicroPEM, equipped with T and RH sensors, sampled (0.50 L min-1, 100 % duty cycle) via a PM2.5 inlet into a nephelometer (0.1 Hz) and 25 mm PTFE filter. In previous evaluation studies, after gravimetric correction, the MicroPEM real-time nephelometer agreed with fixed-site reference monitors across a wide range of ambient PM concentrations (Du et al., 2019; Williams et al., 2014a). However, deployments observed baseline (zero) drift and poor performance at RH conditions above 94 % (Williams et al., 2014a; Zhang et al., 2018). To account for baseline drift, the MicroPEM was zeroed before each deployment using a HEPA filter. Additional details on the MicroPEM sensor, filter analysis, and quality assurance are provided in Sect. 1 of the Supplement.

Before deployment to Malawi, ARI013, ARI014, and ARI015 were colocated with EPA and NC-DEQ reference instruments (Fig. S2) at a near-highway site near Durham, North Carolina, USA (35.865∘ N, 78.820∘ W) between 29 May and 15 June 2017 (boreal summer, i.e., a warm, mild season). ARI013 and ARI014 were colocated for 17 d. ARI015 was colocated for only 8 d due to a defect identified early in the colocation. All data were recorded at 1 min resolution. Colocation site details are provided in Sect. 2 of the Supplement.

The pre-deployment colocation data were used to train, assess, and compare the performance of five modeling approaches to convert the raw voltage data to concentration units and to account for sensor interference and cross-sensitivities. Outlying data points in the raw ARISense gas sensor voltage data due to noise and power cycling were visually identified and removed. Raw NO sensor data collected within 8 h of a power cycle were also removed due to the extended warmup time of the NO-B4 sensor. ARISense data were time-aligned with the reference data, and both data sets were averaged to 5 min resolution. A random 70 % of the colocation data were used for model training, and the remaining 30 % were withheld for testing. Performance assessment metrics were calculated only for the withheld data.

Individual calibration models were built for each gas sensor (Ox, NO, NO2, CO) in each monitor (ARI013, ARI014, ARI015) using five modeling approaches: k-nearest neighbors (kNN) hybrid (Hagan et al., 2018), random forest (RF) hybrid (Malings et al., 2019a), high-dimensional model representation (HDMR) (Cross et al., 2017), quadratic regression (QR) (Malings et al., 2019a), and multi-linear regression (MLR). The five models were selected for consideration based on their performance in previous studies. The kNN hybrid model was found to enable accurate measurements even when pollutant levels were higher than encountered during calibration (Hagan et al., 2018). Given that we expected levels of some pollutants to be higher in Malawi than during calibration in NC, we expected kNN hybrid models to be well suited for our application. Further, the kNN hybrid approach is expected to be widely applicable to a range of pollutants, sensors, and environments (Hagan et al., 2018). In a calibration and validation study conducted by Malings et al. (2019a), RF hybrid models were recommended for any low-cost monitor using electrochemical sensors similar to their sensor package, the Real-time Affordable Multi-Pollutant (RAMP) monitor. Given that the RAMP and ARISense monitors use the same electrochemical sensors and have similar integrated designs, we expected RF hybrid models to perform well for our data set. HDMR models were found to effectively model interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons for the sensor types used in the ARISense package (Cross et al., 2017). Finally, MLR and QR are simple, popular calibration approaches, and they were included in this study for that reason.

The modeling inputs are summarized in Table 1. O3 models were designed to account for sensor cross-sensitivity to NO2 (Cross et al., 2017). Note that references to “O3” indicate estimates made from calibrating the Ox sensor data. References to “Ox” indicate raw voltage measurements from the total oxidant sensor. “Ozone” is used when referring to the gaseous air pollutant. For our study, the CO HDMR models were set to allow only first-dimensional interactions, as second-order interactions were observed to lead to spurious results for data collected outside the bounds of training data (see Sect. 3.3 for more information on deployment conditions). For the CO sensors, this effectively made the HDMR model equivalent to the MLR model. Therefore, the statistical metrics achieved by both models were identical and are shown as overlaid points in Fig. 2a.

ARI023 was colocated with a MicroPEM in an ambient, combustion-source-influenced environment on a house rooftop (4 m a.g.l.) in Mikundi village in Mulanje District, Malawi (16.056∘ S, 35.535∘ E) between 25 July 2018 and 7 August 2018 (austral winter – cool, dry season). We collected 130 h of colocation data over three multi-day collection periods (i.e., three PTFE filters). A 75 % completeness requirement was applied before the raw 1 min data were averaged to 1 and 24 h intervals. Sub-daily averaging intervals were used to assess the OPC-N2 for near real-time (1 min) and diurnal trend (1 h) monitoring applications. A bin-wise RH-correction algorithm based on κ–Köhler theory was applied to correct for hygroscopic growth under high RH conditions, initially assuming particle density (ρ) equal to 1.65 g cm-3 and aerosol hygroscopicity (κ) of 0.6 (Di Antonio et al., 2018). To observe sensitivity of this correction to the assumed hygroscopicity, the density was held constant at 1.65 g cm-3 and the κ value was varied (κ= 0.15, 0.6, and 1). To observe variability due to the assumed source of the aerosol, the density and hygroscopicity were varied to approximate ammonium nitrate, dust, wildfire, and background aerosols. Aerosol property assumptions (κ and density) are based on Hagan and Kroll (2020) and Petters and Kreidenweis (2007).

We adapted performance metrics and target values from recently published U.S. EPA guidelines (Duvall et al., 2021a, b) to assess ARISense performance (Table S1 in the Supplement). The EPA guidelines suggest using linearity, bias, precision, and error metrics to assess air sensor performance, and they offer target values for each. We use the U.S. EPA target values as quantitative markers to indicate satisfactory or unsatisfactory sensor performance; however, given the differences in our study compared to the U.S. EPA methodology, we do not consider these categorizations to be definitive. Further, we emphasize that even if a sensor meets or surpasses the performance target values for each metric, this does not constitute endorsement by the U.S. EPA. Their guidelines were developed for Ox and PM2.5 air sensors, and we used these to assess the ARISense Ox-B421 and OPC-N2 sensors, respectively. Although there are no formal guidelines for CO, NO, and NO2 sensors at the time of writing, for coherency we opt to assess those sensors using a similar approach.

The coefficient of determination (R2), an indicator of the correlation between estimated and true concentrations, was used to assess linearity. The root-mean-square error (RMSE) was used to assess error in the estimated measurements compared to the true values. The coefficient of variation (CV) was used to assess precision. Finally, to assess bias, a linear regression model (y=mx+b) was fit using the ARISense measurements as the dependent variable (y) and the reference measurements as the input variable (x), and the resulting slope (m) and intercept (b) were calculated. Quantitative descriptions for each metric are given in Sect. 3 of the Supplement.

In addition, prediction intervals between the OPC-N2 and MicroPEM data were calculated to provide a statistical confidence interval to interpret OPC-N2 sensor measurements collected after the evaluation period (Bean, 2021). We calculated 68 % (1-sigma) prediction intervals for the ARISense using colocation data from ARI023 (Table 2) collected at the Village 2 site (Fig. 1d). The 1 h averaged observations were used to fit a linear model, which required a Box–Cox transformation (Box and Cox, 1964) to obtain normally distributed residuals (Fig. S3). Details are given in Sect. 3 of the Supplement.

ARI013, ARI014, and ARI015 were deployed to their respective monitoring locations in Malawi from July 2017 to July 2018 (shown as blue markers in Fig. 1). The three locations were selected to provide measures of regional variation and replicates in two paired village sites. ARI013 (Village 2 site) and ARI014 (Village 1 site) were deployed <5 km apart (Fig. S5) in two rural villages in Mulanje, Malawi, adjacent to private residences. ARI015 (University site) was deployed >375 km northwest of the village sites at a rural university campus ∼30 km from the capital city (Fig. S6). Additional satellite images are given in Sect. 4 of the Supplement.

Almost all rural households in Malawi (99.7 %) use solid fuels (e.g., firewood, charcoal) for cooking (National Statistics Office, 2017). Emissions from widespread biomass cookstove use are known to impact local ambient air quality (Aung et al., 2016; Zhou et al., 2011; Amegah and Agyei-Mensah, 2017). Homes regularly using biomass cookstoves within 50 m of the monitoring sites were visually identified at the onset of the study (shown with red crosses in Fig. 1c–d).

A timeline of the ARISense colocations and deployments is given in Table 2. After the 1-year ambient deployment was completed, the ARISense were used for high-concentration emissions monitoring experiments in rural Malawi in July and August 2018. The details of those experiments (i.e., number of experiments, duration, approximate CO concentrations) are discussed in Sect. 5 of the Supplement. We explore the impact of these experiments on sensor operation, but we do not discuss the data itself in this paper.

At the conclusion of the emissions monitoring experiments, ARI013 and ARI014 were returned to NC and were colocated with reference instruments at the near-highway Durham, NC, site used in the pre-deployment colocation (described in Sect. 2.3). ARI015 was relocated to a new monitoring site in Malawi.

Two publicly available NASA data products were obtained from the Goddard Earth Sciences Data and Information Services Center (GES-DISC) Interactive Online Visualization and Analysis Infrastructure (GIOVANNI): (1) area-averaged, monthly multispectral CO surface mixing ratio (daytime / descending) from MOPITT and (2) monthly averaged CO surface concentration (ENSEMBLE) from MERRA-2, henceforth referred to as “MOPITT” and “MERRA-2”, respectively. MOPITT is a calibrated satellite observation and MERRA-2 is a global reanalysis data product. MERRA-2 is the output of an atmospheric chemistry model that has assimilated other data, including satellite data, in making its estimations. Monthly averaged MOPITT and MERRA-2 observations were compared to ARISense CO surface data collected at the Village and University locations. Given the physical proximity of Village 1 and Village 2, and the similarity in monthly mean CO concentration at each site (Fig. S7), the average of the data sets (Village Mean) was used. Additional details are given in Sect. 6 of the Supplement.

Raw gas sensor voltages (5 min averaged data) from all three ARISense monitors (ARI013, ARI014, ARI015), excluding the Ox sensor in ARI015, were highly correlated (R2>0.8) during the pre-deployment colocation, suggesting changes in sensor response were due to environmental changes, not sensor-to-sensor variability (Fig. S9). The sensors in ARI013 and ARI014 were most closely correlated (R2>0.9). The raw ARI015 Ox sensor data showed weaker temperature dependence and the lowest correlation (R2<0.6) with Ox sensors in ARI013 and ARI014 (Fig. S9).

Figure 2 shows two performance metrics representing each sensor–model combination for the three ARISense. Data points toward the lower-left corner of each Fig. 2 panel indicate better performance. Results from all ARISense-sensor–model combinations for all five performance metrics are given in Tables S4–S6. We found that performance varied by ARISense monitor, but none of the ARISense consistently performed better than the others. Overall performance varied by gas sensor type and modeling approach. The calibrated NO2 sensors in all three ARISense were the least correlated with reference measurements compared to the other gas sensors. Only the ARI015 NO2 sensor, calibrated by the RF hybrid model, surpassed the target value for the linearity metric (R2>0.8). Further, no NO2 sensor–model combination met the bias target values for slope and intercept. For all three ARISense, the calibrated NO2 sensors underestimated the true concentration compared to the reference (0.26<m<0.71). However, all NO2 sensor–model combinations met the error target (RMSE <5 ppb) and approached the precision metric target.

At the other end of the performance spectrum, the calibrated Ox sensors performed the best compared to the other gas sensors during pre-colocation. Nearly all Ox sensor–model combinations attained similar linearity and error metrics (0.85<R2<0.99 and 2< RMSE <5 ppb, well within the target values). Only the ARI015 Ox sensor calibrated by the RF hybrid model failed to meet the RMSE target value, yet it returned the highest R2 value compared to the other models. Additionally, all Ox sensor–model combinations met the slope and intercept target values for bias. For the kNN hybrid model, the calibrated O3 observations had a slope approximating 1 (m>0.98) and an intercept of 0, suggesting minimal bias. Only the precision values (37 % < CV <54 %) were outside the EPA guideline target range (CV <30 %).

Most NO sensor–model combinations met the target value for the bias, error, and linearity metrics, but precision was low for all combinations assessed, with most CV values >100 %. This suggests that the variation in the NO data set was in the raw sensor or reference measurements, rather than the modeling approaches. The MLR model was associated with the worst performance for all three NO sensors compared to the other models. However, for ARI015, all NO sensor–model combinations surpassed the target for every metric except precision. Again, the ARI015 gas sensor–RF hybrid model combination was the outlier compared to ARI013 and ARI014 sensor–model combinations (Table S6). We hypothesize that the shorter colocation period of ARI015 (8 d compared to 17 d of colocation for ARI013 and ARI014) led some of the sensor–model combinations to be overfit or poorly constrained.

Most CO sensor–model combinations met or approached the target values for bias, linearity, and precision. The U.S. EPA recommended Ox target values for these three indicators (Table S1) can be used to compare against the CO sensor values to approximate performance, but we surmise that the error target value (RMSE ≤ 5 ppb) cannot. The U.S. EPA National Ambient Air Quality Standards suggest CO concentrations are 1–2 orders of magnitude larger than ambient ozone or NOx concentrations. By extension, we posit that a reasonable error target value for the CO sensor is 50 ppb. Except for the CO–kNN hybrid model combination, most CO sensor–model combinations did not meet our adapted error target value. However, considering the magnitude differences, the CO sensor–model combinations performed similarly to the NO, NO2, and Ox sensors in terms of error. The CO RMSE values (40–70 ppb) were correspondingly 1 order of magnitude larger than NO, NO2, and O3 RMSE values (2–7 ppb).

For the suite of gas sensors in the ARISense monitors, we found the kNN hybrid model to be the best among the modeling approaches used in the pre-deployment colocation testing (Fig. 2). In almost all cases, the kNN hybrid model returned higher R2 values, slope values closer to 1, and lower RMSE values than any other model. The RF hybrid model attained similar and occasionally higher R2 values than the kNN hybrid, but it had higher (and therefore worse) RMSE values by comparison. Further, the kNN hybrid model showed the least inter-monitor variation in performance. In Fig. 2b–d, the kNN hybrid points are closely clustered together, suggesting that this model was able to attain similar performance for each of the three ARISense. Conversely, the other models, in particular the RF hybrid and MLR, showed a wide range in performance across the three ARISense. Even if another model was able to attain performance metrics higher than the kNN hybrid (e.g., HDMR and MLR CO models in Fig. 2a), it was only for one of the three ARISense monitors and never all three. Additionally, the MLR failed to meet target values for some ARISense–gas sensor combinations (Fig. 2a–b). Taken together, these findings suggest the kNN hybrid model is the best choice among these five modeling approaches for our application, given that we sought an approach uniformly applicable to all the gas sensors and all three ARISense.

Pre-deployment colocation PM2.5 measurements in North Carolina (where no reference monitor or data were available) from ARI013, ARI014, and ARI015 suggest the Alphasense OPC-N2 sensors in each monitor responded similarly (R2>0.9) when in the same environment (Fig. S10). ARI013 PM2.5 mass concentration measurements were higher than measurements made by ARI014 and ARI015 (slope >1), despite all ARISense being in the same location. ARI015 underestimated the mass at low concentrations compared to ARI013 and ARI014 (nonlinear clustering at concentrations <5 µg m-3 in Fig. S10a and c). The OPC-N2 sensors in ARI014 and ARI015 showed the highest similarity (slope =1 ± 0.05, R2=0.96).

Figure 3 shows scatterplots of the ARI023 OPC-N2 and MicroPEM 1 min, 1 h, and 24 h averaged data collected during colocation at the Village 2 site in Malawi (individual 1 min scatterplots for each of the three tests are shown in Fig. S11). RH correction partially mitigated the impact of overestimation due to hygroscopic growth but did not remove the artifact entirely (Fig. S12). RH correction improved the precision and error metrics, bringing RMSE within the target value (≤7 µg m-3) for the 24 h averaged data (Table S7). Increasing the averaging interval had a similar effect, but this alone was insufficient to bring RMSE within the target range. Linearity was well below the target value (R2>0.7) for all averaging intervals, and RH correction did little to improve performance for this metric. For this data set, changes in bias and linearity appeared driven by averaging interval. For example, the OPC-N2 RH-corrected 1 min data met the target for slope and intercept, but the 1 and 24 h averaged data met neither of these targets. Particularly for the 24 h averaged data, the small sample was leveraged by a few points, which drove metric values (Fig. 3c); however, close 1:1 agreement between the instruments was observed for 4 of the 7 colocation days. These results highlight the value of longer and more representative colocations. At least two 30 d colocations would be needed, during the hot and dry (September to October) and warm and wet (November to April) seasons, to characterize this specific site.

Even after RH correction, the OPC-N2 overestimated mass concentrations compared to the nephelometer when RH was ≥70 %. Conversely, the OPC-N2 often underestimated mass when RH was ≤30 %. These effects were most noticeable at higher time resolutions (Fig. 3a–b). The effects of RH were tempered by a longer averaging interval; however, for a particularly humid day at this site, the 24 h mass concentration was overestimated by a factor of 3 (Fig. 3c). Notably, the moderate RH outliers in the 24 h average scatterplot suggest that other factors in addition to RH were contributing to error in the OPC-N2 observations.

To explore other contributors to variable OPC-N2 performance, Fig. 4 shows performance for RH-corrected data stratified by environmental conditions (wind direction, ambient concentration, and RH). Wind direction and concentration (Fig. 4a–b) were selected to explore the possible effect of nearby cookstove emissions, while Fig. 4c highlights the remaining effect of RH even after correction. We hypothesized that ambient concentration and wind direction might impact OPC-N2 performance given that the site was periodically exposed to cookstove emissions from the Village 2 site household kitchen (within 15 m to NW) and from adjacent residences (within 50 m to the SSW in Fig. 1d). Figure 4 shows that wind direction was associated with performance variation, although to a lesser degree than RH. Slightly increased performance was observed for northerly winds. Nearby cookstove use potentially explained the decreased performance associated with southerly winds. Four of the five morning cooking periods observed in the time series data were associated with wind blowing from the SE–S–SW (Fig. S14). Figure 4b shows that ambient concentration had a modest impact on OPC-N2 performance metrics. Linearity was expected to increase with concentration, particularly given that the high-concentration bin (20–105 µg m-3) spanned a larger interval than the other bins. Precision within each concentration bin was low. The CV values were well beyond the recommended target value (CV <30 %). The OPC-N2 frequently underestimated the ambient mass concentration compared to the MicroPEM, particularly during higher concentration periods dominated by near-field biomass burning (i.e., slope = 0.4 for measurements between 20 and 105 µg m-3). During periods of cookstove influence, the size distribution, hygroscopicity, and optical properties of the measured aerosol were likely altered. Assumptions about the source aerosol (density and hygroscopicity) used in the RH correction were found to affect inferred OPC-N2 performance compared to the MicroPEM (though not in a predictable fashion). For example, higher linearity and lower RMSE were observed when the particle composition was assumed to be highly hygroscopic (κ=1), yet the least bias was observed at the lowest hygroscopicity assessed (κ=0.15). Further, when the aerosol was assumed to be characteristic of wildfire (rather than ammonium nitrate, dust, or background in origin), the bias between the OPC-N2 and MicroPEM disappeared (slope = 1.02), yet the error metric was among the highest in the four aerosol categories and was above the target value (Table S10). These findings suggest more research is warranted to explore how changing aerosol characteristics (both assumed and actual) impact optical particle sensor performance. Summary statistics for each performance assessment metric are given in Tables S8–S10 in Sect. 8 of the Supplement.

In this deployment site, the OPC-N2 performed the best compared to the MicroPEM during dry conditions (20 % to 40 % RH) and when measuring background aerosol rather than source emissions (Fig. S14, presumed based on time series data). However, this latter result might be partially due to the coincident effects of high RH in this environment (Fig. 7). Figure 4c shows OPC-N2 behavior was affected by changes in ambient RH. In general, performance decreased with increasing RH, and this effect remained even after RH correction. For RH of 20 % to 40 %, RH-corrected OPC-N2 performance approached or exceeded the target values for the linearity, error, and precision metrics (Table S8). After RH increased past 70 %, the R2 value approached zero and the RMSE increased beyond the target value. Unfortunately, the inset histogram of Fig. 4c shows that an RH range of 60 % to 80 % was typical for this site during colocation.

We found that the OPC-N2 at this specific site underestimated mass concentration compared to the MicroPEM, based on less than unity slope values. The performance was variable at low ambient concentrations and largely dependent on RH (Fig. S13). However, outside of very humid (RH >70 %) conditions, the RH-corrected OPC-N2 could estimate the PM2.5 mass concentration within about 10 µg m-3 of the MicroPEM value for real-time, hourly, and daily monitoring purposes (based on RMSE in Table S7). The findings from this section highlight the importance of quality assurance for low-cost optical particle sensor mass concentration measurements, especially those made in environments with highly variable meteorology and nearby ultrafine aerosol sources. For this site, contextual information on meteorology and emissions sources and their diurnal patterns helped interpret and evaluate the measurements.

Given that RH, T, dew point (DP), and differential voltage were inputs to the calibration models, the ranges of these values during colocation in NC should mimic the ranges expected during deployment in Malawi. Otherwise, the model is required to extrapolate beyond its training bounds, which could lead to non-physical results (e.g., negative concentration values). Further, the performance assessment statistics derived from the colocation cannot be expected to hold for conditions far beyond those experienced during the performance characterization. Overall, the colocation and deployment settings exhibited a similar range of environmental conditions (Figs. S15–S16), but T and RH ranges in NC (15 to 40 ∘C and 20 % to 80 %) were less extreme than in Malawi (10 to 45 ∘C and 10 % to 95 %). In Malawi, the ARISense experienced more time at lower temperatures (T<25 ∘C), lower gaseous concentrations (other than CO), and lower ambient pressure (5 to 15 kPa lower depending on the location). Although the ARISense were deployed at a higher elevation in Malawi than during the colocation in North Carolina (625 m versus 120 m a.s.l.), all models were built using the differential voltages (WE-AE) of each electrochemical gas sensor. Therefore, the pressure-related shifts in the WE and AE baseline were not expected to pose an issue for the calibrated Malawi data. The variation in pressure was within the operating range given on the sensor specification sheets (80 to 120 kPa) and was stated not to have long-term impacts by the manufacturer (Alphasense FAQs, 2021). Further, others have shown no statistically significant change in electrochemical sensor sensitivity due to changes in pressure (Popoola et al., 2016). Even so, we did not have the laboratory chamber data to investigate this potential issue.

To evaluate the long-term performance of the OPC-N2 during deployment in Malawi, we examined the representativeness of the colocation conditions for the full year of conditions experienced during deployment. Figures S20–S21 show normalized histograms of the T, RH, and PM2.5 mass concentration observed during the colocation and the full-year deployment in Malawi, suggesting the two data sets spanned a similar range of environmental conditions. However, the colocation occurred during the cool, dry season, and RH minima and maxima (regimes associated with deficient performance during colocation; see Sect. 3.2) were more extreme during the 1-year deployment in Malawi.

Figure 7 shows the annual diurnal trend of the mean PM2.5 mass concentration, with 1-sigma prediction intervals, using 1 h averaged, RH-corrected data from each deployment location. Peak PM2.5 concentrations were observed around 06:00 LT at all sites, when morning biomass cookstove activity coincided with high RH (and more atmospherically stable) conditions. Figure 6 shows that the diurnal trends of ambient CO (another pollutant emitted by biomass burning) were similar to the PM2.5 diurnal trends at each site. Again, the largest peaks were observed at the more densely populated ARI014 Village 1 site. The prediction intervals were widest between 05:00 and 07:00 LT, indicating overall low confidence in OPC-N2 measurements during this period. Afternoon and overnight means, coinciding with drier conditions, were similar across all three sites, and prediction intervals were narrowest during afternoons. Data from the more remote locations (ARI013 and ARI015) suggest background concentrations of PM2.5 in rural Malawi were low (5 to 15 µg m-3), but the OPC-N2 could not reliably quantify peak concentrations that were high and variable, depending on the timing and presence of nearby sources and changes in ambient meteorology (especially RH). Despite this, qualitative data from the OPC-N2 sensors was sufficient to identify nearby source activity and indicate periods when ambient concentrations were likely high enough to be harmful to human health (and at least partially driven by cooking activities associated with higher exposure concentrations).

Given the absence of additional in situ surface data, we rely on satellites and models to estimate surface air quality for comparison of our results. To contribute to the literature on surface-to-satellite comparisons over Africa, we compared calibrated ARISense CO observations to a satellite observation (MOPITT) and a model estimate (MERRA-2) in our study region. We confirmed that all three data sets reported similar annual qualitative trends, although they disagreed in magnitude. This analysis was limited to CO, given that the calibrated CO observations were the most dependable of the ARISense gas data and that NASA remote sensing data products were more readily available for CO compared to O3 or NOx.

Figure 8 shows the mean monthly CO from the University (ARI015) and Village Mean (average of ARI013 and ARI014) sites compared to that from two area-averaged remote sensing products: CO surface mixing ratio from MOPITT and CO surface concentration from MERRA-2. All three data sets were compared from July 2017 to July 2018, focusing on differences between the peak agricultural burning (September to October) and non-burning (December to July) seasons. November and August were excluded from either description (peak burning or non-burning) for the following reasons: (a) a review of fire studies in the region consistently reported September and October as the dominant months of the burning season (Nieman et al., 2021), (b) August and November mark the beginning and end of the fire season, respectively, and therefore cannot be considered non-burning months, (c) the exclusion of August and November better captures strong seasonal differences, providing a measurable benchmark to compare the satellite and surface data, and (d) ARISense data for the Village sites was unavailable for November 2017 (see Sect. 3.7 for more on the difficulties of deployment). The MERRA-2 data set was complete for the full year of interest, but MOPITT was missing data for the Village Mean region in February and March 2018. The NASA data sets were more similar to one another at the Village Mean site compared to the University site. At both sites, MOPITT reported higher CO concentrations than MERRA-2, especially in the peak burning season.

All three data sets (MOPITT, MERRA-2, and ARISense) indicated that annual mean CO concentrations were slightly higher overall at the University site than at the Village site, although this was less pronounced in MERRA-2. Similarly, all three data sets showed increased ambient concentrations during the peak burning season compared to the non-burning season at both sites. For ARISense, MOPITT, and MERRA-2 observations, respectively, peak season means were larger than non-burning season means by 160, 130, 60 ppb (Village Mean) and 190, 115, 50 ppb (University). Although the ARISense indicated larger absolute differences between seasons, the relative increase at both sites was only about 50 % of the non-burning season mean, while MOPITT and MERRA-2 reported increases of 125 % and 75 %, respectively. This could be explained by ARISense proximity to small-scale combustion activity not resolved by satellite imaging. Satellite-based observations approximate ambient background concentrations, which increased during the peak season due to regional agricultural burning. Meanwhile, the ARISense were exposed to ambient background concentrations as well as nearby biomass cookstove emissions, which presumably remained consistent throughout the year, showing a lower relative seasonal increase during the peak burning season. Quantitative disagreement between surface and remote CO observations was highest during the burning season, especially at the University site (Fig. 8). Remote sensing data suggested higher CO concentrations at the University compared to the Village Mean during non-burning periods, but during the peak burning season this difference shrank and similar concentrations were observed across both sites. Conversely, differences between ARISense observations grew by about 6 % during the peak season. MERRA-2 and MOPITT concentrations were highest in September, consistent with ARISense data at the University site but not the Village Mean site, which peaked in October. However, 90 % of the October CO data were missing for the Village site.

Monthly mean CO ARISense values were 2 to 4 times higher than those reported by MOPITT and MERRA-2. We found differences of 175 % to 200 % between the annual mean CO concentration from ARISense and MOPITT, depending on the site, and even larger differences (up to 360 %) with MERRA-2. Differences between MOPITT and MERRA-2 were smaller (30 % to 35 %). There are few comparable studies available to explain these differences, which are greater than previously reported in the literature available for SSA. One study in South Africa reported relative differences of ± 40 % between ground-based CO measurements and Aura satellite observations at Cape Point station (Toihir et al., 2015). Many studies found good agreement (within 10 %–20 % bias) between ground measurements and MOPITT observations, but this was for total column CO, and the observations were not limited to comparisons over Africa (Buchholz et al., 2017; Emmons et al., 2009, 2004; Yurganov et al., 2008, 2010). However, these studies found negative satellite bias when intense biomass plumes affected observations, when CO levels were low in the Southern Hemisphere, or when atmospheric CO levels changed rapidly (Buchholz et al., 2017; Emmons et al., 2004; Yurganov et al., 2008, 2010). Each of these conditions could be expected to occur in the southern African troposphere, potentially explaining differences observed between the ARISense and remote sensing observations in this study.

This comparison of low-cost sensor surface data, satellite observations, and model estimates in Malawi suggests each of these resources can give consistent information on qualitative, long-term trends in a region lacking ground-based reference monitoring. However, because of inherent differences in spatial and temporal resolution, each observation will disagree in magnitude. Satellite retrievals and real-time surface measurements do not result in directly comparable quantities. Satellite data are collected as a once-daily flyover observation, averaged over a ∼ 12 000 km2 area (corresponding to 1∘ spatial resolution). In contrast, the ARISense data were 1 min resolution, fixed-site, long-term point measurements at the surface. Further, the ARISense data were collected near visually identified biomass emission sources and were not representative of background conditions. Meanwhile, the satellite observations provide an estimate of regional background conditions. Despite these differences, the MOPITT, MERRA-2, and ARISense data sets agreed on the long-term seasonal trends present in this region and even corroborated site-to-site differences (e.g., higher mean CO at University compared to Village Mean site). These findings suggest the ARISense captured synoptic-scale variation in CO, but comparison to remote sensing data does not allow for a quantitative assessment of data collected at higher temporal resolutions.

The annual median (July 2017 to July 2018) surface concentrations in Malawi estimated by the ARISense sensors were 9 to 11 ppb for NOx, 4 to 15 ppb for O3, and 240 to 330 ppb for CO, depending on the site. Surface concentrations and diurnal trends of ARISense CO and PM in Malawi were comparable to studies in Kenya, Rwanda, Ethiopia, Uganda, and South Africa (Delmas et al., 1999; DeWitt et al., 2019; Laakso et al., 2008; McFarlane et al., 2021; Nthusi, 2017; Scheel et al., 1998; Subramanian et al., 2020; Toihir et al., 2015). However, comparison of O3 concentrations suggested the calibrated ARISense observations underestimated actual concentrations. ARISense NOx observations were similar to two other studies (Delmas et al., 1999; Laakso et al., 2008), but overall there was little comparable data available to assess NOx concentrations in Africa.

ARISense CO observations were similar to regional CO concentrations in central Africa (measured by aircraft), found to be in the range of 250–400 ppb (Delmas et al., 1999). A long-term ambient study at the Rwanda Climate Observatory found a mean CO concentration of 215 ppb from May 2015 to January 2017 (DeWitt et al., 2019), only slightly lower than our findings in Malawi. Another LCS study in Kigali, Rwanda, observed a range in ambient CO concentrations, from 225 to 500 ppb at their rural and urban sites (Subramanian et al., 2020), spanning the concentration range we observed at our rural and semi-urban sites in Malawi.

Both studies in Rwanda found mean ambient O3 concentrations of 30 to 40 ppb (DeWitt et al., 2019; Subramanian et al., 2020). For a “relatively clean background site located in dry savannah in South Africa the annual median (July 2006 to July 2007) trace gas concentrations were equal to 1.4 ppb for NOx, 36 ppb for O3 and 105 ppb for CO” (Laakso et al., 2008). Background levels of NOx and CO at this site were lower than the ARISense annual means, yet background O3 was in line with the Rwandan studies. This suggests regional ozone concentrations in central and southern Africa are presently about 30–40 ppb. The annual mean ARISense O3 values were up to a factor of 10 lower; however, we identified quality-assurance issues in the calibrated O3 values, particularly for the second half of the deployment data. Therefore, the ARISense data are likely to be an underestimate of the true ambient values.

The relatively clean background site in South Africa (Laakso et al., 2008) had NOx concentrations up to a factor of 10 lower (1.4 ppb) than ARISense measurements in Malawi, but aerial measurements made during intense savanna fire activity in central Africa found NOy present in the range of 4–10 ppb (Delmas et al., 1999). Together, these studies suggest that the ARISense NOx concentrations (9–11 ppb) may be reasonable for our non-background, biomass-emission-influenced sites in Malawi.

Conversely, the annual median PM1, PM2.5, and PM10 concentrations (9.0, 10.5 and 18.8 µg m-3, respectively) at the background site in South Africa (Laakso et al., 2008) were comparable to ARISense observations in Malawi. The annual median ARISense RH-corrected PM1, PM2.5 and PM10 concentrations were between 4 and 7, 6 and 10, and 13 and 20 µg m-3, respectively, across all three sites. It is possible that actual concentrations of fine PM were higher at the sites in Malawi, considering that concentrations of gaseous emission tracer species (i.e., CO, NOx) were higher compared to regional background levels found by other studies. However, given the high minimum cut-off diameter of the OPC-N2, this particle sensor would have been unable to detect ultrafine particles emitted from biomass burning. Average ambient PM2.5 concentrations (measured with an Alphasense OPC-N2) were found to be between 11 and 24 µg m-3 at various sites in Kenya, with higher pollution episode concentrations ranging from 35 to 51 µg m-3 (Nthusi, 2017). Median ARISense PM2.5 concentrations were also comparable to US embassy measurements in Ethiopia and Uganda (DeWitt et al., 2019). Taken together, these comparisons suggest PM levels in rural Malawi are comparable to regional measurements made across SSA, but localized impacts from biomass cookstoves can result in higher concentrations of fine PM, which are difficult to accurately quantify with the OPC-N2. In all, although these comparisons are not a substitute for quantitative evaluation of the ARISense in Malawi, they provide a benchmark for comparison and suggest that the CO, NOx, and PM ARISense observations are reasonable for this region. At the same time, they cement our conclusion that ARISense O3 observations are likely erroneous for this environment.

Total data recovery for the 1-year deployment varied by site, season, and sensor, with rates ranging from 30 % to 80 % (Fig. S22). Average recovery for the 1-year deployment was around 60 %, with the highest recovery at the University site (80 %) and lowest at the Village 1 site (40 %). Data across all sites had the highest completeness (>70 %) in the cool and dry (June–July–August) and the cool and wet season (March–April–May). Data losses were mostly explained by power outages, software failures, and sensor equilibration times required after a power outage (Fig. S23). Power outages were common in the warm and wet season (December–January–February) due to insufficient solar intensity resulting from extended periods of heavy cloud cover. At the ARI014 site, insufficient power led to an unanticipated diurnal cycle wherein the monitor would shut off in the early morning hours and require a few hours of solar power before turning on again. This daily cycle, coupled with the 8 h long NO sensor re-equilibration time, led to almost 0 % NO data recovery in the second half of the deployment for Village 1. In all, nearly 50 % of data losses at the ARI014 site were due to insufficient power or failure to write data to file. Corrupt USB storage devices, which we were slow to replace due to ongoing civil unrest (The Guardian, 2017), resulted in significant data losses in the hot and dry season (September–October–November) at the two Village sites. Individual sensor failure was rare, but 2 months of ARI014 Ox data were lost to electrochemical sensor drift and one OPC-N2 (ARI013) failed in the last 3 months of deployment due to an insect nest clogging the OPC-N2 inlet. In all, we recorded 6992 h of data at the University site (ARI015), 5860 h at Village 2 (ARI013), and 4720 h at Village 1 (ARI014). Future deployments should include insect screens over all sensor inlets and improved battery storage and power systems that run at a longer duty cycle in the case of insufficient solar (e.g., power on only once battery is fully charged) to minimize the impact of sensor equilibration times on data recovery.

Since the monitors were deployed to their sites for >1 year, there was observation overlap in seasonally similar data collected 1 year apart. To gain insight into sensor stability, we compared the data collected in the first month (July 2017) to the final month (July 2018) of the deployment, given that ambient environmental conditions were similar in July of both years (additional details in Sect. 11 of the Supplement). It is not possible to know if the range of gas concentrations were significantly different between July 2017 and July 2018. We explored this analysis on the assumption that inter-annual variability in ambient concentrations was minimal. Bivariate distributions of the raw differential voltage readings from July 2017 and July 2018 showed that the most frequent observations (i.e., heaviest-shaded regions) were approximately the same in both years (Fig. S25). Observable differences in the voltage measurements could be partially explained by known environmental differences. For example, the Ox sensor voltages in July 2018 were lower on average than in 2017, but this was consistent with lower temperatures and higher RH in 2018 compared to 2017. However, there was potential evidence of slightly reduced or altered responses in individual sensors, particularly the NO sensors in ARI013 and ARI015 and the CO sensors in ARI013 and ARI014. For these sensors, the 2018 distributions had less spread than the 2017 distributions, suggesting either less variation in ambient concentrations in 2018 or decreased sensitivity in the sensors. Diurnal plots from both years showed that the raw mean voltages and trends were consistent (Fig. S26). However, again the most noticeable differences were in the individual CO and NO sensors identified from the bivariate distributions. For example, the CO peaks measured at mealtimes by ARI013 and ARI014 were about 50 mV lower in 2018 than 2017. These differences could be explained by lower concentrations in 2018 than 2017, changes in the raw sensor response over the 1-year period, or both. Without reference equipment, we were unable to investigate sensor drift and decay more rigorously. This qualitative analysis suggests individual sensor responses were altered during the 1-year deployment, but there was no unambiguous evidence for systematic deterioration within or across the electrochemical sensor groups used in the ARISense.

In general, the calibrated observations followed the trends identified from the raw sensor voltage readings. Calibrated CO data trends were consistent for both years, with the models responding as expected to the lower voltage readings in 2018 compared to 2017. For ARI013 and ARI014, the calibrated CO peaks at mealtimes were accordingly lower by about 100 ppb in 2018 (Fig. S27). However, although the raw Ox sensor trends in 2018 and 2017 were consistent for all the ARISense (Fig. S26), the kNN hybrid model calibrated O3 data were highly irregular between the 2 years (Fig. S27). For example, the calibrated O3 data for July 2017 showed the expected diurnal pattern (concentration increasing with solar intensity) with plateaus between 15 and 40 ppb depending on the site. However, in July 2018, although the raw Ox diurnal data looked similar to 2017, the calibrated data for ARI013 and ARI015 showed midday values between 0 and 5 ppb, and the diurnal trend for ARI013 showed a flat line (i.e., not correlated with solar activity). This finding, that raw Ox sensor voltages were similar year to year while the calibrated O3 values were not, provides further evidence that the lack of comparable T, RH, and ozone colocation data contributed to the non-physical O3 trends observed during the second half of the deployment at the ARI013 and ARI015 sites.

Before their return to NC, ARI013 and ARI014 were used for high-concentration emissions monitoring experiments after the 1-year ambient monitoring campaign was completed (Table 2). The reference monitor data from the post-deployment colocation in NC (August 2018 to May 2019) were intended to enable investigation of changes in ARI013 and ARI014 raw sensor response and model performance. However, the resulting data instead demonstrated that the sensors had been severely degraded during the high-concentration exposures. In the post-colocation data, the raw differential voltage gas sensor responses in ARI013 and ARI014 were well correlated with each other (R2=0.7 to 0.9) (excluding the ARI013 Ox sensor which was clearly degraded; see Fig. S28) but less correlated than during the pre-colocation comparison (R2=0.9 to 0.99). To facilitate comparison with the pre-colocation performance metrics shown in Fig. 2 and Tables S4–S6, the performance metrics for the post-deployment colocation are given in Tables S11 and S12. Despite showing inter-sensor consistency, the raw differential sensor voltages (other than CO) made by ARI013 and ARI014 were poorly correlated with reference measurements (Figs. S29–S30). Inspection of the time series showed that the ARISense NO sensors tracked some spikes in the time-aligned NO reference data, but the NO2 and Ox sensors did not track reference data trends (Figs. S31–S32). The time series of the differential voltage and temperature data suggest the gas sensors in ARI013 and ARI014 were responding similarly to changes in T and RH, but they were no longer sensitive to changes in the target gas (Fig. S31). This may explain why the sensors in ARI013 and ARI014 were still well correlated with each other and not correlated with reference measurements. The calibrated CO data were the only data still roughly correlated with CO reference measurements, although the calibrated CO data showed aberrant features (Figs. S33–S34). These ambient sensors (except for the CO sensor) were likely affected by high concentrations of PM and volatile gases (e.g., hydrocarbons, formaldehyde) co-emitted during the biomass burning experiments. Exceedingly high concentrations of emissions can chemically degrade or contaminate the sensors; for example, the catalyst or electrolyte can be affected or depleted by repeated interactions with high concentrations of non-target species emissions. Further, if there were high concentrations of fine semivolatile PM permeating the inlet and flow line, it could condense and block or attenuate the sample flow rate. The Ox, NO, NO2 sensors were permanently altered by the biomass burning emission experiments in Malawi, leading to poor performance during post-deployment colocations with reference instruments in NC. Given these dramatic changes in sensor responses, the models were unable to generate reasonable concentration values from sensor signals, and consequently we were unable to use the post-deployment colocation data set to quantitatively assess long-term model performance. The partial exception to this was for the kNN hybrid calibrated CO data, which were correlated with the reference data (R2=0.5), suggesting that the CO sensors might retain some function after additional colocation and recalibration.