Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments

Lara P. Clark; V. Sreekanth; Bujin Bekbulat; Michael Baum; Songlin Yang; Pao Baylon; Timothy R. Gould; Timothy V. Larson; Edmund Y. W. Seto; Chris D. Space; Julian D. Marshall

doi:10.3390/s20123417

Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments

Sensors ◽

10.3390/s20123417 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3417

Author(s):

Lara P. Clark ◽

V. Sreekanth ◽

Bujin Bekbulat ◽

Michael Baum ◽

Songlin Yang ◽

...

Keyword(s):

Air Pollution ◽

Gold Standard ◽

Low Cost ◽

Passive Samplers ◽

Solid Fuels ◽

Indoor Environments ◽

Prior Work ◽

Limits Of Detection ◽

Passive Method

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1–3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving “gold standard” measurements is needed to investigate accuracy.

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Air Pollution Measurements and Land-Use Regression in Urban Sub-Saharan Africa Using Low-Cost Sensors—Possibilities and Pitfalls

Atmosphere ◽

10.3390/atmos11121357 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1357

Author(s):

Asmamaw Abera ◽

Kristoffer Mattisson ◽

Axel Eriksson ◽

Erik Ahlberg ◽

Geremew Sahilu ◽

...

Keyword(s):

Land Use ◽

Air Pollution ◽

Low Cost ◽

Pollution Monitoring ◽

Sub Saharan Africa ◽

Land Use Regression ◽

Rapid Urbanization ◽

Final Model ◽

Sub Saharan

Air pollution is recognized as the most important environmental factor that adversely affects human and societal wellbeing. Due to rapid urbanization, air pollution levels are increasing in the Sub-Saharan region, but there is a shortage of air pollution monitoring. Hence, exposure data to use as a base for exposure modelling and health effect assessments is also lacking. In this study, low-cost sensors were used to assess PM2.5 (particulate matter) levels in the city of Adama, Ethiopia. The measurements were conducted during two separate 1-week periods. The measurements were used to develop a land-use regression (LUR) model. The developed LUR model explained 33.4% of the variance in the concentrations of PM2.5. Two predictor variables were included in the final model, of which both were related to emissions from traffic sources. Some concern regarding influential observations remained in the final model. Long-term PM2.5 and wind direction data were obtained from the city’s meteorological station, which should be used to validate the representativeness of our sensor measurements. The PM2.5 long-term data were however not reliable. Means of obtaining good reference data combined with longer sensor measurements would be a good way forward to develop a stronger LUR model which, together with improved knowledge, can be applied towards improving the quality of health. A health impact assessment, based on the mean level of PM2.5 (23 µg/m3), presented the attributable burden of disease and showed the importance of addressing causes of these high ambient levels in the area.

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A Light Visual Mapping and Navigation Framework for Low-Cost Robots

Journal of Intelligent Systems ◽

10.1515/jisys-2014-0116 ◽

2015 ◽

Vol 24 (4) ◽

pp. 505-524 ◽

Cited By ~ 1

Author(s):

Stephane Bazeille ◽

Emmanuel Battesti ◽

David Filliat

Keyword(s):

Large Range ◽

Low Cost ◽

Indoor Environments ◽

Noise Levels ◽

Visual Sensor ◽

Loop Closure ◽

Loop Closure Detection ◽

Localization And Mapping ◽

Computational Resources

AbstractWe address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels.

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

10.21203/rs.3.rs-996536/v1 ◽

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.

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MOPGA/Improving Air Quality in West Africa: Low-cost sensors as a solution to improve the understanding of spatial and temporal variability in urban air pollution

10.5194/egusphere-egu21-8210 ◽

2021 ◽

Author(s):

Julien Bahino ◽

Michael Giordano ◽

Véronique Yoboué ◽

Arsène Ochou ◽

Corinne Galy-Lacaux ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

West Africa ◽

Low Cost ◽

West African ◽

Passive Samplers ◽

Quality Monitoring ◽

Spatial And Temporal Variability ◽

Air Quality Monitoring ◽

Calibration Models

This study was carried out within the framework of the Improving Air Quality in West Africa&#160;(IAQWA) project funded by the Make Our Planet Great Again (MOPGA) program. In recent years, West African countries have experienced an economic upturn driven by GDP growth of nearly 3.7% in 2019 (AfDB, 2020). This economic boom is mainly felt in the cities where it promotes the construction of highway infrastructure, real estate development, and industry. All these activities are sources of air pollution. Unfortunately, there is almost no air quality monitoring in these cities partly due to the high cost of monitoring instruments. Low-cost air quality monitoring instruments can help improve the spatial and temporal resolution of measurements at relatively lower cost. However, the installation of these instruments in West African environments characterized by high relative humidity requires their calibration through collocation with reference instruments. The IAQWA project aims to improve our understanding of air pollutants such as fine particulate matter mass (PM2.5), ozone (O3), nitrogen oxides (NOx), sulfur dioxide (SO2), and carbon monoxide (CO) in Abidjan and Accra, two major West African capitals, through the deployment of Real-time Affordable Multi-Pollutant (RAMP) monitors.Since February 2020, five RAMPs have been installed and are operating continuously at various sites&#160;in Abidjan and Lamto in Cote d'Ivoire, and four RAMPs have been operating in Accra, Ghana. Some of the RAMPs have been collocated with PM and/or NOx reference instruments. At other sites the RAMPs have been collocated with INDAAF passive samplers and passive aerosol collectors. These collocations have allowed for the development of calibration models for these low-cost sensors. The performance of these calibration models is presented here along with the diurnal and seasonal variations of air pollution at the different sites in Abidjan and Accra. These results will eventually be used to improve our understanding of the drivers of air pollution in these major West African cities, which is essential to choosing sustainable development pathways in the future.

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The Use of a New Passive Sampler for Ozone and Nitrogen Oxides Monitoring in Ecological Effects Research

The Scientific World JOURNAL ◽

10.1100/tsw.2001.81 ◽

2001 ◽

Vol 1 ◽

pp. 475-482 ◽

Cited By ~ 7

Author(s):

Franco De Santis ◽

Tuncay Dogeroglu ◽

Sabrina Menichelli ◽

Caterina Vazzana ◽

Ivo Allegrini

Keyword(s):

Nitrogen Oxides ◽

Low Cost ◽

Field Tests ◽

Cost Effective ◽

Ecological Effects ◽

Passive Samplers ◽

Diffusion Tubes ◽

Diffusive Sampler ◽

Nitrate And Nitrite

A simple, cost-effective diffusive sampler is described that is suitable for measuring parts per billion (ppb) levels of ozone and nitrogen oxides. The diffusive sampler makes use of nitrite for ozone determination whereas for nitrogen oxides and nitrogen dioxide an active carbon tissue impregnated with sodium carbonate is used. Nitrate and nitrite, the formation of which is proportional to the pollutant concentration and sampling duration, are the two species analysed, respectively. Diffusion tubes have the advantage of being a low- cost, convenient way of mapping spatial distributions and investigating long-term trends of ozone and nitrogen oxides. The method is extremely useful for assessing long-term concentrations such as the annual mean for nitrogen oxides, as required by the Daughter Directive 1999/30/EC. Field tests to validate the method have been carried out at an urban background location with co-located passive samplers and continuous measurements of O3and NOx. An application in ecological effects monitoring for ozone is also presented.

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Using low cost open-face passive samplers to sample PM concentration and elemental composition in childcare facilities

Environmental Science Processes & Impacts ◽

10.1039/d0em00075b ◽

2020 ◽

Vol 22 (7) ◽

pp. 1502-1513

Author(s):

Zhong-Min Wang ◽

Yixin Zhou ◽

Fraser W. Gaspar ◽

Asa Bradman

Keyword(s):

Particulate Matter ◽

Elemental Composition ◽

Low Cost ◽

Airborne Particulate Matter ◽

Low Noise ◽

Passive Samplers ◽

Indoor Environments ◽

Airborne Particulate ◽

Open Face

Effective, low noise and low-cost samplers for airborne particulate matter (PM) in indoor environments are needed.

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Low-Cost Air Quality Sensing towards Smart Homes

Atmosphere ◽

10.3390/atmos12040453 ◽

2021 ◽

Vol 12 (4) ◽

pp. 453

Author(s):

Hamid Omidvarborna ◽

Prashant Kumar ◽

Joe Hayward ◽

Manik Gupta ◽

Erick Giovani Sperandio Nascimento

Keyword(s):

Air Pollution ◽

Air Quality ◽

Data Processing ◽

Low Cost ◽

Smart Homes ◽

Monitoring And Control ◽

Spatio Temporal ◽

Key Steps ◽

And Control

The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.

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