scholarly journals Feasibility of Low-Cost Particle Sensor Types in Long-Term Indoor Air Pollution Health Studies After Repeated Calibration Over a 2-Year Timeframe

2021 ◽  
Author(s):  
Elle Anastasiou ◽  
M. J. Ruzmyn Vilcassim ◽  
John Adragna ◽  
Emily Gill ◽  
Albert Tovar ◽  
...  
Keyword(s):  
Air Pollution ◽  
Failure Rate ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Low Cost ◽  
Calibration Coefficient ◽  
Data Output ◽  
Term Performance ◽  
Over Time

Abstract Background Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Objective Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. Methods 51 low-cost particle sensors (Airbeam 1 N=29; Airbeam 2 N=22) were calibrated four times over a 2-year timeframe between 2019-2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Results Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to <1 at final calibration timepoint [Airbeam 1 Mean (SD)= 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N=27, failure rate 48.2%) than Airbeam 2 (N=2, failure rate 16.7%) due to electronics, battery, or data output issues. Conclusions Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

How low-cost air pollution sensors could make homes smarter?

2021 ◽  
Author(s):  
Hamid Omidvarborna ◽  
Prashant Kumar
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Air Pollutants ◽  
Indoor Air Pollution ◽  
Prediction Models ◽  
Disease Risk ◽  
Low Cost ◽  
Mortality And Morbidity ◽  
Spatio Temporal ◽  
Indoor Air Pollutants

&lt;p&gt;The majority of people spend most of their time indoors, where they are exposed to indoor air pollutants. Indoor air pollution is ranked among the top ten largest global burden of a disease risk factor as well as the top five environmental public health risks, which could result in mortality and morbidity worldwide. The spent time in indoor environments has been recently elevated due to coronavirus disease 2019 (COVID-19) outbreak when the public are advised to stay in their place for longer hours per day to protect lives. This opens an opportunity to low-cost air pollution sensors in the real-time Spatio-temporal mapping of IAQ and monitors their concentration/exposure levels indoors. However, the optimum selection of low-cost sensors (LCSs) for certain indoor application is challenging due to diversity in the air pollution sensing device technologies. Making affordable sensing units composed of individual sensors capable of measuring indoor environmental parameters and pollutant concentration for indoor applications requires a diverse scientific and engineering knowledge, which is not yet established. The study aims to gather all these methodologies and technologies in one place, where it allows transforming typical homes into smart homes by specifically focusing on IAQ. This approach addresses the following questions: 1) which and what sensors are suitable for indoor networked application by considering their specifications and limitation, 2) where to deploy sensors to better capture Spatio-temporal mapping of indoor air pollutants, while the operation is optimum, 3) how to treat the collected data from the sensor network and make them ready for the subsequent analysis and 4) how to feed data to prediction models, and which models are best suited for indoors.&lt;/p&gt;

scholarly journals Low-cost assessment of a stove improvement program in Peru

2021 ◽  
Author(s):  
Boris Reiss ◽  
Isaac Jogues Roa ◽  
Raleigh Bacharach ◽  
Igor Burstyn ◽  
Eyal Oren
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Low Cost ◽  
Skills Training ◽  
Improvement Program ◽  
Reduction Measure ◽  
Improvement Programs ◽  
Risk Reduction Measure ◽  
Co Concentrations

Background: Approximately 3 billion people worldwide, including more than 20 million people in Peru alone, depend on biomass fuels (BMF) for heating and boiling water, cooking, and lighting their homes. Burning BMF on poorly ventilated stoves results in household air pollution (HAP), which includes carbon monoxide (CO). Stove improvement programs aim to reduce indoor air pollution and are included in various organizations’ education visits to Peru. The programs do not allow sufficient time, or provide equipment or skills training to determine if the stove improvements have been successful.Objective: To develop and implement a rapid, low-cost method to measure CO concentrations in rural Peruvian households in order to assess the success of stove improvements. Methods: CO concentrations were measured 159 times in 14 households (with 7 clean, 6 traditional, and 1 gas stove) near Cusco, Peru. CO concentrations were measured in one-minute intervals with MSA Altair 4X multigas monitors. The likelihood of the 95th percentile exceeding the NIOSH CO ceiling limit of 200 ppm for each stove type was calculated.Results: The probability of overexposure was almost three times lower with “clean” stoves than with traditional stoves, i.e., 24% vs 9%.Conclusion: A promising and pragmatic method to evaluate the increased effectiveness of clean stoves over traditional ones was developed and tested. This method supports the implementation of clean stoves as a risk reduction measure.

Author response for "Long Term Exposure to Indoor Air Pollution and Risk of Tuberculosis"

2020 ◽  
Author(s):  
Vidhiben Patel ◽  
Andrew Foster ◽  
Alison Salem ◽  
Amit Kumar ◽  
Vineet Kumar ◽  
...  
Keyword(s):  
Air Pollution ◽  
Indoor Air ◽  
Indoor Air Pollution ◽  
Author Response
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