Fig 1.
Reference and low-cost PM sensor (LCS) networks were used in this study.
(a) Spatial distribution of monitoring nodes within the InfoAer LCS network (blue dots) and the Romanian National Air Quality Monitoring Network (RNMCA) reference stations (green squares) in Bucharest. Major transportation corridors are indicated in red lines, and minor roadways of all types are noted in grey lines. 1.25 km radius proximity zones centred on each RNMCA station are shown for co-location analysis, along with the ID of the RNMCA station for each cluster (B1 − B30). The base map data is obtained from OpenStreeMap (OSM, https://www.openstreetmap.org). OSM data was downloaded as an sp object using the osmdata R package, by querying the bounding box for ‘Bucharest’. The OSM data is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. (b) Intercomparison of daily PM2.5 concentrations (μg m−3) measured at RNMCA stations using gravimetric reference methods and Derenda AMP2 automated monitoring systems.
Table 1.
Performance statistics (mean ± standard deviation of R2) for calibration models fitted independently to each sensor cluster. The humidity correction functions f(RH) and k(RH) are implemented according to Eqs. (2) and (3), respectively. Predictor variables raised to the 0th power indicate exclusion from the regression model. The highest-performing model is marked in bold, while the second and third-ranked models are shown in italics.
Fig 2.
Calibration model performance evaluation.
(a) Comparison of calibrated (red and green lines) and uncalibrated (light blue line) LCS measurements versus reference data (black line) for a representative cluster (the B23 cluster is shown), demonstrating the behavior of the LCS and reference PM2.5 during dry and humid periods, and the improvement over the raw LCS achieved through calibration. Note that the calibrated LCS PM2.5 fully overlaps the reference PM2.5. (b) Box plot analysis of the accuracy of the LCS PM2.5 calibrated with the three models (M1, M2, and M3) across all nine clusters. R2 distribution statistics are shown separately for the dry and humid seasonal periods. On the boxplots, the thick line represents the median, the box lower and upper limits are the Q1 (25%) and Q3 (75%) quartiles, while the whiskers extend to 1.5 times the Q1 − Q3 interquartile range.
Table 2.
Statistical indicators of accuracy for calibration models over the identified sensor clusters. 95% confidence intervals are shown in paranthesis. CI is estimated by resampling 1000 times the calibrated daily PM2.5 values for each station for the whole year, then calculating the statistical indicators for each sample and extracting the 2.5% and 97.5% quantile values from the obtained distribution.
Fig 3.
Spatial dependency analysis of calibration model performance.
(a) Scatter plot showing the relationship between the accuracy of the calibration models (R2 values for models M1 − M3) and the inter-sensor separation distance for each cluster. Distance is measured between the corresponding InfoAer sensors and the RNMCA reference stations. (b) Box plot comparison of model M2 calibration performance (R2 distribution) across sensor clusters stratified by RNMCA station classification categories.
Fig 4.
Annual exceedances of daily PM2.5 thresholds recorded by InfoAer sensors in 2024:
(a) EU and (b) WHO. The frequency of exceedances for calibrated and uncalibrated data is represented by circles and diamonds, respectively. Connecting lines between paired data points show the magnitude of the difference attributable to sensor calibration..
Fig 5.
Spatial and temporal variability of PM2.5 concentrations across the InfoAer and RNMCA monitoring networks.
(a) Deviation from the network-mean number of EU daily PM2.5 threshold exceedances for stations from both monitoring networks. σ represents the standard deviation of annual exceedance frequencies across all InfoAer stations in 2024. δ denotes the deviation of individual InfoAer and RNMCA site exceedance counts from the network-wide mean. The administrative boundaries of Bucharest’s six districts are delineated by purple lines. The base map data was obtained from OpenStreetMap as described in the caption of Fig 1. (b) Monthly-averaged PM2.5 concentrations across the combined dataset. The shaded regions represent the ± 0.5 (dark orange) and ±1.5 (light orange) standard deviation confidence bands around the monthly means.