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Characterizing the chemical composition of ambient particulate matter (PM) provides valuable information on the concentration of secondary species and toxic metals and assists in the validation of abatement techniques. The chemical components of PM can be measured by sampling on filters and analyzing them in the laboratory or using real-time measurements of the species. It is important for the accuracy of the PM monitoring networks that measurements from the offline and online methods are comparable and biases are known. The concentrations of water-soluble inorganic ions (NO3−, SO42−, NH4+, and Cl−) in PM2.5 measured from 24 h filter samples using ion chromatography (IC) were compared with the online measurements of inorganics from an aerosol mass spectrometer (AMS) with a frequency of 2 min. Also, the concentrations of heavy and trace elements determined from 24 h filter samples using inductively coupled plasma mass spectroscopy (ICP-MS) were compared with the online measurements of half-hourly heavy and trace metal concentrations from an Xact 625i ambient metal mass monitor. The comparison was performed over two seasons (summer and winter) and at two sites (Indian Institute of Technology Delhi (IITD) and Indian Institute of Tropical Meteorology, Delhi (IITMD)) which are located in the Delhi National Capital Region (NCR), India, one of the most heavily polluted urban areas in the world. Collocated deployments of the instruments helped to quantify the differences between online and offline measurements and evaluate the possible reasons for positive and negative biases. The slopes for SO42− and NH4+ were closer to the 1:1 line during winter and decreased during summer at both sites. The higher concentrations on the filters were due to the formation of particulate (NH4)2SO4. Filter-based NO3− measurements were lower than online NO3− during summer at IITD and winter at IITMD due to the volatile nature of NO3− from the filter substrate. Offline-measured Cl− was consistently higher than AMS-derived Cl− during summer and winter at both sites. Based on their comparability characteristics, elements were grouped into three categories. The online element data were highly correlated (R2>0.8) with the offline measurements for Al, K, Ca, Ti, Zn, Mn, Fe, Ba, and Pb during summer at IITD and winter at both the sites. The higher correlation coefficient demonstrated the precision of the measurements of these elements by both the Xact 625i and ICP-MS. Some of these elements showed higher Xact 625i elemental concentrations than ICP-MS measurements by an average of 10 %–40 % depending on the season and site. The reasons for the differences in the concentration of the elements could be the distance between two inlets for the two methods, line interference between two elements in Xact measurements, the sampling strategy, variable concentrations of elements in blank filters, and the digestion protocol for ICP-MS measurements.

Low-cost sensors offer an attractive solution to the challenge of establishing affordable and dense spatio-temporal air quality monitoring networks with greater mobility and lower maintenance costs. These low-cost sensors offer reasonably consistent measurements but require in-field calibration to improve agreement with regulatory instruments. In this paper, we report the results of a deployment and calibration study on a network of six air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed in two phases over a period of 3 months at sites situated within two megacities with diverse geographical, meteorological and air quality parameters. A unique feature of our deployment is a swap-out experiment wherein three of these sensors were relocated to different sites in the two phases. This gives us a unique opportunity to study the effect of seasonal, as well as geographical, variations on calibration performance. We report an extensive study of more than a dozen parametric and non-parametric calibration algorithms. We propose a novel local non-parametric calibration algorithm based on metric learning that offers, across deployment sites and phases, an R2 coefficient of up to 0.923 with respect to reference values for O3 calibration and up to 0.819 for NO2 calibration. This represents a 4–20 percentage point increase in terms of R2 values offered by classical non-parametric methods. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature into calibration models; (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature-weighing or metric-learning technique; (3) using local calibration techniques such as k nearest neighbors (KNN); (4) performing temporal smoothing over raw time series data but being careful not to do so too aggressively; and (5) making all efforts to ensure that data with enough diversity are demonstrated in the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors and offer an encouraging opportunity to use them to supplement and densify compliance regulatory monitoring networks.

Characterizing the chemical composition of ambient particulate matter (PM) provides valuable information on the concentration of secondary species, toxic metals and assists in the validation of abatement techniques. The chemical components of PM can be measured by sampling on filters and analysing them in the laboratory or using real-time measurements of the species. It is important for the accuracy of the PM monitoring networks that measurements from the offline and online methods are comparable and biases are known. The concentrations of water-soluble inorganic ions (NO3−, SO42−, NH4+ and Cl−) in PM2.5 measured from the 24 hrs filter samples using ion chromatography (IC) were compared with the online measurements of inorganics from aerosol mass spectrometer (AMS) with a frequency of 2 mins. Also, the concentrations of heavy and trace elements determined from the 24 hrs filter samples using inductively coupled plasma mass spectroscopy (ICP-MS) were compared with the online measurements of half-hourly heavy and trace metal’s concentrations from Xact 625i ambient metal mass monitor. The comparison was performed over two seasons (summer and winter) characterized by their different metrological conditions at IITD and during winter at IITMD, two sites located in Delhi, NCR, India, one of the heavily polluted urban areas in the world. Collocated deployments of the instruments helped to quantify the differences between online and offline measurements and evaluate the possible reasons for positive and negative biases. The slopes for SO42− and NH4+ were closer to 1:1 line during winter and decreased during summer at both sites. The higher concentrations on the filters were due to the formation of particulate (NH4)2SO4. Filter-based NO3− measurements were lower than online NO3− during summer at IITD and winter at IITMD due to the volatile nature of NO3− from the filter substrate. Offline measured Cl− was consistently higher than AMS derived Cl− during summer and winter at both sites. Based on their comparability characteristics, elements were grouped under 3 categories. The online element data were highly correlated (R2 > 0.8) with the offline measurements for Al, K, Ca, Ti, Zn, Mn, Fe, Ba, and Pb during summer at IITD and winter at both the sites. The higher correlation coefficient demonstrated the precision of the measurements of these elements by both Xact 625i and ICP-MS. Some of these elements showed higher Xact 625i elemental concentrations than ICP-MS measurements by an average of 10–40 % depending on the season and site. The reasons for the differences in the concentration of the elements could be the distance between two inlets for the two methods, line interference between two elements in Xact measurements, sampling strategy, variable concentrations of elements in blank filters and digestion protocol for ICP measurements.