Abstract. 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 meteorologi...

No abstract

Abstract. 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 PM2.5 at 1 min, 1 h, 6 h, 12 h and 24 h integration times. We tested the PMS3003s in both low concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM2.5 (mean ± Std.Dev) 15 of 9 ± 9 µg m -3 and 10 ± 3 µg m -3 respectively, and a high concentration urban location (Kanpur, India) with 1 h PM2.5 of 36 ± 17 µg m -3 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 b-attenuation monitor (E-BAM)) to determine how these sensors perform across a range of PM2.5 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 20 Model T640s and a ThermoScientific Model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease of 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201%) to Kanpur monsoon (46%) and to post-monsoon (35%) season showed that PMS3003 performance generally improved as ambient PM2.5 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 b-attenuation-based monitors 25 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 optical-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 Kanpur post-monsoon season (from 35% to 11%); 3) the higher T640-PMS3003s correlations (R 2 ³ 0.63) than SHARP-PMS3003s (R 2 ³ 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 30 PMS3003 PM2.5 measurements. When proper RH corrections are made by empirical non-linear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003s can measure PM2.5 concentrations within ~10% of ambient values. We observed that PMS3003s appeared to exhibit a non-linear response when ambient PM2.5

No abstract

Abstract. Air quality is a growing public concern in both developed and developing countries, as is the public interest in having information on air pollutant concentrations within their communities. Quantifying the spatial and temporal variability of ambient fine particulate matter (PM2.5) is of particular importance due to the well-defined health impacts associated with PM2.5. This work evaluates a number of select PM sensors (Shinyei: models PPD42NS, PPD20V, PPD60PV) under a variety of ambient conditions and locations including urban background and roadside sites in Atlanta, GA, as well as a location with substantially higher ambient concentrations in Hyderabad, India. Low cost sensor measurements were compared against reference monitors at all locations. On-road emissions factors were calculated at the Atlanta site by pairing PM2.5 and separately determined black carbon (BC) and carbon dioxide (CO2) measurements. On-road emission factors can vary in different locations and over time for a number of reasons, including vehicle fleet composition and driving patterns and behaviors, and current environmental policy. Emission factors can provide valuable information to inform researchers, citizens, and policy makers. The PPD20V sensors had the highest correlation with the reference environmental beta attenuation monitor (E-BAM) with R2 values above 0.80 at the India site while at the urban background site, the PPD60PV had the highest correlation with the tapered element oscillating microbalance (TEOM) with an R2 value of 0.30. At the roadside site, only the PPD20V was used, with an R2 value against the TEOM of 0.18. Emissions factors at the roadside site were calculated as 0.39 ± 0.10 g PM2.5 per kg fuel and 0.11 ± 0.01g BC per kg fuel, which compare well with other studies and estimates based on other instruments. The results of this work show the potential usefulness of these sensors for high concentration applications in developing countries and for their use in generating emissions factors.

No abstract

Air pollution has become a pressing issue in today's society because of its significant effects on humans, animals, plants, air quality, climate and the wider environment. Most urban areas are associated with one or more air pollutants which are emitted from local or regional pollution sources including vehicle exhausts, fossil fuels using in energy production, emissions from industrial and mining activities, agricultural and construction operations, household usage of chemicals and materials and natural causes. Most personal exposure studies are focused on local environments and shortterm periods. Previous controlled experiments and studies were done in a small number of designated areas in cities. Our research study used timebased activity data; 3 main and 17 sub-microenvironments were applied over 37 days-long research while traveling through Southeast Asian countries. In this study, personal exposure of PM 2.5 for a traveler was monitored using an assembled low-cost monitor with Plantower PMS 3003 PM 2.5 sensor which has a light-scattering principle. All time-based activity data was recorded with a smartphone whenever microenvironments changed during the study period. The goal of this study was to understand more about the personal exposure to PM 2.5 related air pollution in the global travel environment as a traveler and to understand how an individual's activity and location impact PM 2.5 exposure. According to the results from the Southeast Asia study, the personal PM 2.5 exposure varied in the categorized microenvironments. Port/Station (outdoor) and Café/Pub/Restaurant (indoor-outdoor) were the most polluted microenvironments with 32.8 and 29.6 µg/m 3 1-h mean PM 2.5 concentration, respectively. Market/Shopping Mall (indoor), Street (outdoor) and Cable Car/Metro/Tram (vehicle) were also concerning microenvironments with 19.3, 19.3 and 18.9 µg/m 1-h mean PM 2.5 concentrations, respectively. Passenger Car microenvironment had the lowest 1-h mean PM 2.5 concentration of 2.3 µg/m 3 which agrees with some other studies on transportation microenvironments in the literature.

No abstract