Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plantower model PMS3003) for measuring PM _2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM _2.5 (mean ± SD) of 9±9 and 10±3 µg m ⁻³ , respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM _2.5 of 36±17 and 116±57 µg m ⁻³ during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental β attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM _2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM _2.5 increased. The precision of reference instruments (T640: ±0.5 µg m ⁻³ for 1 h; SHARP: ±2 µg m ⁻³ for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and β -attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27 % for 1 h to 9 % for 24 h) than in Durham (from 201 % to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35 % to 11 %); and (3) the higher T640–PMS3003 correlations ( R ² ≥0.63) than SHARP–PMS3003 ( R ² ≥0.25). A major RH influence was found in RTP (1 h RH =64±22 %) due to the relatively high precision of the T640 measurements that can explain up to ∼30 % of the variance in 1 min to 6 h PMS3003 PM _2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM _2.5 concentrations within ∼10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM _2.5 exceeded ∼125 µg m ⁻³ and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM _2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization.

Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM10 and PM2.5 (particulate matter <10 μm and < 2.5 μm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM10 mass (271 μg m-3 and 300 μg m-3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM10 mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.