Beta Attenuation Research Articles

A case study on the effect of contaminated inlet tubes on the accuracy of mid-cost optical particle counters for the ambient air monitoring of fine particles

This study investigates the impact of the long-term use of inlet-heated tubes on the performance of mid-cost optical particle counters (OPCs) for ambient air monitoring of fine particles (PM2.5). Two OPCs, equipped with inlet-heated tubes, were deployed over a 6-month period, with a beta attenuation monitor (BAM) serving as the reference device. The performance of the OPCs using the same inlet tubes for the first 3 months was compared to their performance after the frequent replacement of clean tubes during the final 3 months. The correlation coefficients (r2) for the 1 h and 24 h average PM2.5 concentrations between the OPCs and the BAM were lower with long-term contaminated tubes (0.82 < r2 < 0.93) compared to clean tubes (r2 > 0.93). The relative mean errors and biases significantly increased over time with contaminated tubes. Temperature, humidity, precipitation, and wind speed were found to have an insignificant effect (r2 < 0.1) on the performance of the two OPCs with inlet-heated tubes over the 6-month period. The relative average PM2.5 error when using clean tubes was less than 4%. These findings highlight the importance of inlet-heated tubes in improving OPC performance, particularly for mitigating humidity effects.Graphical

Low-Cost Hourly Ambient Black Carbon Measurements at Multiple Cities in Africa.

There is a notable lack of continuous monitoring of air pollutants in the Global South, especially for measuring chemical composition, due to the high cost of regulatory monitors. Using our previously developed low-cost method to quantify black carbon (BC) in fine particulate matter (PM2.5) by analyzing reflected red light from ambient particle deposits on glass fiber filters, we estimated hourly ambient BC concentrations with filter tapes from beta attenuation monitors (BAMs). BC measurements obtained through this method were validated against a reference aethalometer between August 2 and 23, 2023 in Addis Ababa, Ethiopia, demonstrating a very strong agreement (R2 = 0.95 and slope = 0.97). We present hourly BC for three cities in sub-Saharan Africa (SSA) and one in North America: Abidjan (Côte d'Ivoire), Accra (Ghana), Addis Ababa (Ethiopia), and Pittsburgh (USA). The average BC concentrations for the measurement period at the Abidjan, Accra, Addis Ababa Central summer, Addis Ababa Central winter, Addis Ababa Jacros winter, and Pittsburgh sites were 3.85 μg/m3, 5.33 μg/m3, 5.63 μg/m3, 3.89 μg/m3, 9.14 μg/m3, and 0.52 μg/m3, respectively. BC made up 14-20% of PM2.5 mass in the SSA cities compared to only 5.6% in Pittsburgh. The hourly BC data at all sites (SSA and North America) show a pronounced diurnal pattern with prominent peaks during the morning and evening rush hours on workdays. A comparison between our measurements and the Goddard Earth Observing System Composition Forecast (GEOS-CF) estimates shows that the model performs well in predicting PM2.5 for most sites but struggles to predict BC at an hourly resolution. Adding more ground measurements could help evaluate and improve the performance of chemical transport models. Our method can potentially use existing BAM networks, such as BAMs at U.S. Embassies around the globe, to measure hourly BC concentrations. The PM2.5 composition data, thus acquired, can be crucial in identifying emission sources and help in effective policymaking in SSA.

Air quality and attributable mortality among city dwellers in Kampala, Uganda: results from 4 years of continuous PM2.5 concentration monitoring using BAM 1022 reference instrument.

Air pollution is a known risk factor for non-communicable diseases that causes substantial premature death globally. Rapid urban growth, burning of biomass and solid waste, unpaved sections of the road network, rising numbers of vehicles, some with highly polluting engines, contribute to the poor air quality in Kampala. To provide evidence-based estimates of air pollution attributable mortality in Kampala city, with focus on ambient fine particulate matter (PM2.5). We utilized a time series design and prospectively collected data on daily ambient PM2.5 concentration levels in micrograms per cubic meter (μg/m3) using a Beta Attenuation Monitor (BAM-1022) in Kampala city, Uganda. We combined the PM2.5 data with all-cause mortality data obtained from the Uganda Bureau of Statistics and the Ministry of Health in Kampala. We calculated attributable risk estimates for mortality using the WHO AirQ+ tools. Overall, the annual average concentration for PM2.5 for the period of 4 years, 2018-2021, was 39 μg/m3. There was seasonal variation, with the rainy season months (March-June and October-December) having lower values. PM2.5 concentrations tend to be highest in the morning (09.00 h) and in the evening (21.00 h.) likely due to increased vehicular emissions as well as the influence of weather patterns (atmospheric temperature, relative humidity and wind). Saturday has the most pollution (daily average over 4 years of 41.2 μg/m3). Regarding attributable risk, we found that of all the deaths in Kampala, 2777 (19.3%), 2136 (17.9%), 1281 (17.9%) and 1063 (19.8%) were attributable to long-term exposure to air pollution (i.e., exposure to PM2.5 concentrations above the WHO annual guideline of 5 μg/m3) from 2018 to 2021, respectively. For the 4 years and considering the WHO annual guideline as the reference, there were 7257 air pollution-related deaths in Kampala city. Our study is the first to estimate air pollution attributable deaths in Kampala city considering the target as the WHO annual guideline value for PM2.5 of 5 μg/m3. Our monitoring data show that fine particulate matter air pollution in Kampala is above the WHO Air Quality Guideline value, likely resulting in substantial adverse health effects and premature death. While further monitoring is necessary, there is a clear need for control measures to improve air quality in Kampala city.

Task-related brain activity in upper limb dystonia revealed by simultaneous fNIRS and EEG

ObjectiveThe aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks. MethodsPatients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model. ResultsPatients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task. ConclusionsCortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation. SignificanceSimultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

A case study evaluating the performance of a cost-effective optical particle counter coupled with a humidity compensation approach for ambient air monitoring of particulate matter

The spatial monitoring of submicron particles has become an essential issue due to their negative effects on human health. However, the use of high-cost and high-grade measurement instruments is a challenging investment cost. Thus, a cost-effective optical particle counter (OPC), which is improved measurement quality, has become a good candidate. In this study, two cost-effective OPCs, coupled with a heated inlet tube to reduce the effect of humidity on its measurements, were applied in the field to measure ambient PM10, PM2.5, and PM1 over 1 month. Their 1-h-average and 24-h-average data were compared with those obtained from a reference-grade OPC and a beta attenuation monitor (BAM). In particular, the correlations between the measurement data obtained from them, the differences in the mean values of these data, and the relative errors were evaluated. The PM2.5 data obtained from cost-effective OPCs were comparable to those from BAM at even under high humidity conditions, except for rainy days. The data obtained from the cost-effective OPCs also showed good correlations and low relative errors (i.e., < 7%) compared to the reference-grade OPC, with no significant difference in mean values in terms of the PM2.5 and PM1 data. Although the measurement of PM10 by the cost-effective OPCs showed lower quality than PM2.5 and PM1, the relative errors were still acceptable (i.e., < 18%) compared with those in other studies. Therefore, the cost-effective OPC coupled with a heated inlet tube has the potential to serve as a real-time monitoring instrument for ambient PM2.5 and PM1.

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Abstract. Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.5) air pollution but has sparse regulatory air monitoring. Challenges for low-cost PM2.5 sensors in India include high-aerosol mass concentrations and pronounced regional and seasonal gradients in aerosol composition. Here, we report on a detailed long-time performance evaluation of a popular sensor, the Purple Air PA-II, at multiple sites in India. We established three distinct sites in India across land use categories and population density extremes (in urban Delhi and rural Hamirpur in north India and urban Bengaluru in south India), where we collocated the PA-II model with reference beta attenuation monitors. We evaluated the performance of uncalibrated sensor data, and then developed, optimized, and evaluated calibration models using a comprehensive feature selection process with a view to reproducibility in the Indian context. We assessed the seasonal and spatial transferability of sensor calibration schemes, which is especially important in India because of the paucity of reference instrumentation. Without calibration, the PA-II was moderately correlated with the reference signal (R2 = 0.55–0.74) but was inaccurate (NRMSE ≥ 40 %). Relative to uncalibrated data, parsimonious annual calibration models improved the PurpleAir (PA) model performance at all sites (cross-validated NRMSE 20 %–30 %; R2 = 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed among our sites, suggesting that local calibrations are desirable when possible. Using a moving-window calibration, we found that using seasonally specific information improves performance relative to a static annual calibration model, while a short-term calibration model generally does not transfer reliably to other seasons. Overall, we find that the PA-II model can provide reliable PM2.5 data with better than ±25 % precision and accuracy when paired with a rigorous calibration scheme that accounts for seasonality and local aerosol composition.

Detection and identification of shape, size, and concentration of particulate matter in ambient air using bright field microscopy-based system

Currently, air pollution monitoring for supplemental information is performed using optical sensors based on light scattering by particulate matter. These devices provide results in the form of particle counts and the concentration of particulate matter is calculated and demonstrated in real-time. We have developed a bright field (BF) microscopy-based system for air quality monitoring (AQM) in real-time, named SENSurAIR, which provides not only just counts but shape, size as well as concentration. The system comprises a mini vacuum pump, cyclone separator, LED light source (λ = 480 nm), and bright field microscopic unit. The pump drives the ambient air toward the sampling media so that PM2.5 is impacted and deposited. The light rays from the source are incident on the sampling media, and microscopic unit records images of particulate matter. The custom-developed algorithm then processes the recorded images to identify the shape, size distribution, and concentration of PM2.5. The size and shape of PM2.5 are the principal factors that help identify the sources contributing to PM2.5 in ambient air. The developed device is evaluated by collocating with the reference grade Beta Attenuation Monitoring (BAM) system. The system is fully automatic, compact, low-cost, and can be integrated with a wireless network for data communication. The simultaneous monitoring of shape, size, and concentration using the developed device is unique and which will be highly useful to alarm the health hazards due to the existing pollution.

Wildfire Smoke Monitoring for Agricultural Safety and Health in Rural Washington

ABSTRACT Objectives This study aimed to evaluate the performance of a low-cost smoke sampling platform relative to environmental and occupational exposure monitoring methods in a rural agricultural region in central Washington state. Methods We co-located the Thingy AQ sampling platform alongside cyclone-based gravimetric samplers, a nephelometer, and an environmental beta attenuation mass (E-BAM) monitor during August and September of 2020. Ambient particulate matter concentrations were collected during a smoke and non-smoke period and measurements were compared across sampling methods. Results We found reasonable agreement between observations from two particle sensors within the Thingy AQ platform and the nephelometer and E-BAM measurements throughout the study period, though the measurement range of the sensors was greater during the smoke period compared to the non-smoke period. Occupational gravimetric sampling methods did not correlate with PM2.5 data collected during smoke periods, likely due to their capture of larger particle sizes than those typically measured by PM2.5 ambient air quality instruments during wildfire events. Conclusion Data collected before and during an intense wildfire smoke episode in September 2020 indicated that the low-cost smoke sampling platform provides a strategy to increase access to real-time air quality information in rural areas where regulatory monitoring networks are sparse if sensor performance characteristics under wildfire smoke conditions are understood. Improving access to spatially resolved air quality information could help agricultural employers protect both worker and crop health as wildfire smoke exposure increases due to the impacts of climate change. Such information can also assist employers with meeting new workplace wildfire smoke health and safety rules.

Elemental chemical characterization and morphology of PM10 using SEM/EDS in air quality monitoring station filters in Poza Rica, Veracruz, Mexico

Air quality has been a global problem for several decades and has had several approaches for its mitigation, analysis and quantification. There are several ways and methods to quantify primary and secondary air pollutants. Therefore, the objective of this research was to determine, through SEM-EDS analysis, the elemental chemical characterization and morphology of the compounds present in the tapes of PM10 BAM (Beta Attenuation Mass Monitor) equipment from air quality monitoring campaigns in the city of Poza. Rica, Veracruz in Mexico. The methodology consisted of collecting filter tapes from the air quality monitoring stations used in the evaluation period, sampling and analysis was performed by the SEM/EDS scanning electron microscopy technique. The results allowed identifying in the samples the elemental chemical characterization and morphology of the particles retained in them.

Estimation of hourly black carbon aerosol concentrations from glass fiber filter tapes using image reflectance-based method

Our method measures atmospheric BC concentrations by analyzing photos of particle deposits on glass-fiber filters. Post-analysis of beta attenuation monitor (BAM) tapes collected worldwide can be a valuable source for hourly BC data, particularly for Global South countries.

Comparison of PM2.5 Monitoring Data Using Light Scattering and Beta Attenuation Methods: A Case Study in Seoul Metro Subway

A study on monitoring PM2.5 concentrations at an underground subway station using light scattering and beta attenuation methods was conducted. Six optical particle counters (OPCs), which were representative of the light scattering method and had 16 size channels, were installed at different sampling sites in the waiting room and platforms to compare PM2.5 concentrations, determine PM2.5 /PM10 ratios, and characterize the size distribution based on particle number. In terms of the beta attenuation method, a beta-ray attenuation monitor (BAM) was set up at the platform to compare it with OPCs for one month. It was found that 1h-average PM2.5 concentrations varied at different sampling sites depending on PM composition, while 24h-average PM2.5 concentrations were independent of the sampling sites. There was a significant difference between the means of 1h-average PM2.5 concentrations observed by the BAM and OPC when PM2.5 contributed to less than 50% or higher than 80% of PM10. In contrast, the 24h-average PM2.5 concentrations observed by the BAM and OPC showed the same patterns as each other with an insignificant difference. Therefore, an OPC can be used to monitor the 24h-average PM2.5 concentration in an underground subway station. However, the use of OPC to measure the 1h-average PM2.5 concentration should consider PM composition and other factors. In addition, more frequent calibration is needed on a regular basis.

Small contributions of dust to PM2.5 and PM10 concentrations measured downwind of Oceano Dunes

The Oceano Dunes State Vehicular Recreation Area (ODSVRA) is a large natural source of wind-driven dust emissions that primarily consist of particulate matter (PM) for which most of the mass is greater than 1 μm. The San Luis Obispo County Air Pollution Control District (SLOAPCD) has targeted ODSVRA emissions to reduce exceedances of the state standard of 50 μg m−3 for PM smaller than 10 μm (PM10) at a coastal monitoring site located 1 mile downwind of the dune area. To evaluate the potential effectiveness of this abatement strategy, five sets of measurements were collected during the windy afternoons of high-wind months (May and October) from 2019 to 2021 to quantify the organic and elemental composition of PM10 and PM smaller than 2.5 μm (PM2.5) samples. The five-campaign average afternoon PM2.5 composition relative to Beta Attenuation Monitor (BAM) concentrations included 14 ± 12% dust, 9 ± 9% sea salt, 4 ± 5% non-sea salt sulfate, and 8 ± 8% organic components, with the difference between BAM and the measured components contributing the remaining 66 ± 16% unidentified components. The high afternoon unidentified contribution was partially attributable to water, consistent with the increasing relative humidity during warm afternoons, since there was a lower unidentified contribution for overnight samples. The remaining unidentified components are consistent with prior measurements of semivolatile contributions of ammonium nitrate and organic compounds constituting >40% of PM2.5 in central California. The organic functional group signature in Fourier Transform Infrared (FTIR) spectroscopy for PM2.5 was similar to previously reported ambient marine aerosol, consistent with a natural marine source for most of the organic mass concentration. For PM10, the dust fraction was 11 ± 8% of BAM PM10 concentration and increased to 14 ± 10% of BAM PM10 concentration during days in which hourly BAM PM10 concentration exceeded 140 μg m−3. There was no statistically significant difference (p»0.05) between dust, sea salt, sulfate, or organic PM2.5 fractions between weekend and weekday concentrations, similar to prior findings for the region. These results are consistent with national and state assessments of the good air quality of the region and suggest that semivolatile components may cause differences between BAM and gravimetric mass concentrations for sampling times shorter than 24 h. In addition, the results demonstrate that the regulated dust abatement implemented at ODSVRA is misapplied and unlikely to improve downwind air quality significantly, since ODSVRA and other nearby sandy areas contribute only 14% of BAM PM10 and have not been shown to include toxic components.

Intelligent PM 2.5 mass concentration analyzer using deep learning algorithm and improved density measurement chip for high-accuracy airborne particle sensor network

The concentration of ultrafine airborne particles is continuously increasing, and research has shown that it has adverse effects when inhaled into the human body. Accordingly, there is a growing demand for a measurement network utilizing sensors to evaluate an individual's exposure to these airborne particles. However, the current low-cost sensors have the limitation of low accuracy. To solve this, we devised a method to calibrate a low-cost mass concentration sensor accurately in real time. In a previous study, we developed an analyzer that could measure the effective density and nanoparticles that cause the low accuracy of the mass concentration sensor. However, it had a hardware stability problem when used to monitor the outside air for a long period. This has been improved by modifying the shape of the Micro-electromechanical system (MEMS)-based chip integrated with the electrical/inertial analysis technology. Hence, the device can now operate with sufficient stability in the outdoor air. In addition, the retrieval algorithm used to convert the measured current values into the effective density and nanoparticle size distribution was prone to errors. It was modified to a deep learning-based physical parameter conversion algorithm to minimize the errors. Thus, we developed a standalone analyzer that integrates the improved nanoparticle and effective density analyzer, temperature and humidity sensor, and low-cost mass concentration sensor. In addition, we developed a technique to calibrate the mass concentration sensor data accurately in real time based on the data measured by each sensor and analyzer and a deep learning-based mass concentration calibration algorithm. The calibration precision was confirmed through comparative evaluation of the analyzer with the results of a beta attenuation mass monitor. In the future, this analyzer can be utilized to build a sensor network that precisely monitors the mass concentration in a large area through multi-point deployment. Alternatively, it can be used for monitoring the mass concentration in an indoor private space, where it is difficult to place expensive and large equipment.

Seasonal changes and respiratory deposition flux of PM2.5 and PM10 bound metals in Dhaka, Bangladesh

Due to rapid urbanization and fast economic development, aerosol pollution is a serious environmental issue, especially in Bangladesh. Based on bioaccessibility and respiratory deposition doses (RDD), health risks of PM2.5 and PM10 bound 15 (fifteen) metals were investigated at fourteen urban sites (roadside, marketplace, industrial, and commercial areas). Sampling campaigns were conducted over four seasons (winter, summer, rainy, and autumn) from December 2020 to November 2021. A beta attenuation mass analyzer measured particulate matter concentrations in ambient air. The metals in PM fractions were analyzed by X-ray fluorescence spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS). The airborne trace metals (Cd, As, Zn, Pb, Cr, Cu, Ni) with high enrichment factors indicate anthropogenic sources. The positive matrix factorization (PMF) categorized these elements as originating from automobile exhaust, industrial emissions, and solid waste/coal combustion, whereas the geologic elements came from earth crust/soil dust. During the winter, most of the air mass trajectories arrived from India across the land (82%) and Indo Gangetic Plain (IGP) region to the sampling sites, which may have aided in the transport of pollutants. The deposition flux of metals illustrated that compared to PM2.5, PM10 deposited a higher amount of metals in the upper airways (81.96%).In comparison, PM2.5 accumulates more elevated amounts of metals in alveolar regions (11.77%), due to the ability of fine particles to penetrate deeper into the lower pulmonary region. Among age groups, an adult inhales a higher amount of metals than a child, on average 0.103 mg and 0.08 mg of metals per day via PM2.5, respectively. Acute health impacts are caused by the deposited cancer-causing metals in alveolar tissue, which circulates through the bloodstream and affects several organs. Prolonged exposure to these carcinogenic metals poses significant health risks.

Attenuation of beta radiation in granular matrices: implications for trapped-charge dating

Abstract. Mineral grains within sediment or rock absorb a radiation dose from the decay of radionuclides in the host matrix. For the beta dose component, the estimated dose rate must be adjusted for the attenuation of beta particles within the mineral grains. Standard calculations, originally designed for thermoluminescence dating of pottery, assume that the grain is embedded in a homogenous medium. However, most current applications of trapped-charge dating concern sand- or silt-sized dosimeters embedded in granular sediment. In such cases, the radionuclide sources are not homogeneous but are localised in discrete grains or held on grain surfaces. We show here that the mean dose rate to dosimeter grains in a granular matrix is dependent on the grain-size distributions of the source grains and of the bulk sediment, in addition to the grain size of the dosimeters. We further argue that U and Th sources are likely to be held primarily on grain surfaces, which causes the dose rate to dosimeter grains to be significantly higher than for sources distributed uniformly throughout grains. For a typical well-sorted medium sand, the beta dose rates derived from surface U and Th sources are higher by ∼ 20 % and ∼ 30 %, respectively, compared to a homogenous distribution of sources. We account for these effects using an expanded model of beta attenuation – including the effect of moisture – and validate the model against Monte Carlo radiation transport simulations within a geometry of packed spheres.

Applying machine learning for large scale field calibration of low‐cost PM2.5and PM10air pollution sensors

AbstractLow‐cost air quality monitoring networks can potentially increase the availability of high‐resolution monitoring to inform analytic and evidence‐informed approaches to better manage air quality. This is particularly relevant in low and middle‐income settings where access to traditional reference‐grade monitoring networks remains a challenge. However, low‐cost air quality sensors are impacted by ambient conditions which could lead to over‐ or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large‐scale calibration of AirQo sensors, low‐cost PM sensors custom‐designed for and deployed in Sub‐Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM usingk‐nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5and PM10calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m3to 7.2 μg/m3with an average BAM PM2.5concentration of 37.8 μg/m3while the lasso regression model decreased RMSE from 13.4 μg/m3to 7.9 μg/m3with an average BAM PM10concentration of 51.1 μg/m3. We validate our models through cross‐unit and cross‐site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large‐scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.

Bias in PM2.5 measurements using collocated reference-grade and optical instruments.

Optical PM2.5 measurements are sensitive to aerosol properties that can vary with space and time. Here, we compared PM2.5 measurements from collocated reference-grade (beta attenuation monitors, BAMs) and optical instruments (two DustTrak II and two DustTrak DRX) over 6months. We performed inter-model (two different models), intra-model (two units of the same model), and inter-type (two different device types: optical vs. reference-grade) comparisons under ambient conditions. Averaged over our study period, PM2.5 measured concentrations were 46.0 and 45.5μgm-3 for the two DustTrak II units, 29.8 and 38.4μgm-3 for DRX units, and 18.3 and 19.0μgm-3 for BAMs. The normalized root square difference (NRMSD; compares PM2.5 measurements from paired instruments of the same type) was ~ 5% (DustTrak II), ~ 27% (DRX), and ~ 15% (BAM). The normalized root mean square error (NRMSE; compares PM2.5 measurements from optical instruments against a reference instrument) was ~ 165% for DustTrak II, ~ 74% after applying literature-based humidity correction and ~ 27% after applying both the humidity and BAM corrections. Although optical instruments are highly precise in their PM2.5 measurements, they tend to be strongly biased relative to reference-grade devices. We also explored two different methods to compensate for relative humidity bias and found that the results differed by ~ 50% between the two methods. This study highlights the limitations of adopting a literature-derived calibration equation and the need for conducting local model-specific calibration. Moreover, this is one of the few studies to perform an intra-model comparison of collocated reference-grade devices.

EVALUATION OF ALPHASENSE OPC-N2 SENSOR FOR PM10 MEASUREMENT IN THE NORTH JAKARTA

Spatial and temporal data of particulate matter (PM) are limited in Indonesia; hence cost-effective and robust instruments to monitor PM concentration could complete data coverage. The low-cost sensor (LCS) Alphasense OPC-N2 provides real-time PM concentration data and is relatively simple to install and deploy. This paper presents data from an OPC-N2 sensor collocated with a PM10 Beta Attenuation Monitor (BAM). The study was carried out at an air quality monitoring site in North Jakarta belonging to the provincial government. The location is considered suitable for evaluating the performance of OPC-N2 micro sensor as it is a representative of a typical Indonesian urbanized area with a range of pollutant sources, including sea-sourced aerosols. At the same site, a filter-based Gent Sampler (GS) measuring both PM10 and PM2.5 was also deployed. The study showed that 30-minutely and daily average concentrations data for PM10 measured by OPC-N2 were lower than that of BAM measurements in both averaged durations by approximately 50%. The comparison between OPC-N2 and GS for PM10 showed that OPC-N2 measurement was underestimated but it was overestimated for PM2.5. Nonetheless, correlations of OPC-N2 and BAM were 0.530 and 0.607 for PM10 and PM2.5, respectively. These results were comparable to other low-cost sensor evaluation studies in different countries, suggesting that the sensor can represent temporal variation of the reference measurement.

Development, verification and application of a versatile aerosol calibration system for online aerosol instruments

Traditional calibration methods mostly focus on the calibration of detection systems while the calibration from the sampling and pre-condition systems to the detection system is usually ignored. In this regard, a Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) is developed for the whole-process calibration of time-resolved aerosol measurement instruments. PAMAS is composed of a particle generation chamber, an ultrasonic atomizer, a dilution system, and a syringe pump. It is designed to steadily generate standard aerosol particles of known concentrations (≤250 μg/m3), chemical compositions, and stable particle size distributions. Monodispersed aerosol can be generated in the size range of hundreds of nanometers to several micrometers with a narrow size distribution. The generated particles with different compositions generated by PAMAS have been well verified by the filter-based gravimetric method, yielding accuracy and R2 of more than 95% and 0.999 in a wide concentration range. The response time by changing the target concentration of reference particles is 1–2 min. PAMAS has been applied to various types of time-resolved aerosol measurement instruments, including particle mass concentration monitors (Beta Attenuation and Tapered Element Oscillating Microbalance), online Ion Chromatograph, and semi-continuous OCEC carbon aerosol analyzer. Very consistent results between PAMAS and calibrated instruments can be obtained if the instruments are functioning well. As for instruments with certain technical issues, PAMAS can serve as a good tool for performance evaluation and quality assurance of the instruments and the accuracy of the measurement data can be adjusted based on the calibration results.

Chitosan-Polyvinyl Alcohol blend as beta-ray attenuator

Living organism exposure to radiation beyond certain limit is dangerous, while handling the radioactive materials. High Z- materials give proper shielding to this radiation, but the associated production of external bremsstrahlung, high cost of materials, hazardous nature of materials like lead (Pb) make us to choose low Z polymer as shielding material. Present work investigating on the beta attenuation properties of Chitosan-Polyvinyl Alcohol blend films. The films of individual polymers and their blends in different ratios having different thickness were prepared and attenuation study has been carried out with the help of G M Counter and Tl-204 beta active radiation source. FTIR, XRD, DSC, TGA, SEM characterisation shows that blend with 10–90(CH-PVA) and 30–70(CH-PVA) ratios have good attenuation property because of good miscibility of polymers in the blend. Miscibility of blend reduces as chitosan content increases and hence attenuation values also decreases. These results suggest the feasibility of using CH-PVA blend as low energetic beta attenuator.