Abstract
Sharp GP2Y1010 dust sensors are increasingly being used within distributed sensing networks and for personal monitoring of exposure to particulate matter (PM) pollution. These dust sensors offer an easy-to-use solution at an excellent price point; however, the sensors are known to offer limited dynamic range and poor limits of detection (L.O.D.), often >15 μg m−3. The latter figure of merit precludes the use of this inexpensive line of dust sensors for monitoring PM2.5 levels in environments within which particulate pollution levels are low. This manuscript presents a description of the fabrication and circuit used in the Sharp GP2Y1010 dust sensor and reports several effective strategies to minimize noise and maximize limits of detection for PM. It was found that measurement noise is primarily introduced within the photodiode detection circuitry, and that electromagnetic interference can influence dust sensor signals dramatically. Through optimization of the external capacitor and resistor used in the LED drive circuit—and the inter-pulse delay, electromagnetic shielding, and data acquisition strategy—noise was reduced approximately tenfold, leading to a projected noise equivalent limit of detection of 3.1 μg m−3. Strategies developed within this manuscript will allow improved limits of detection for these inexpensive sensors, and further enable research toward unraveling the spatial and temporal distribution of PM within buildings and urban centers—as well as an improved understanding of effect of PM on human health.
Highlights
The internet of things (IoT) offers substantial promise concerning efforts to integrate sensors and electronic control devices to improve the quality of human life and wellbeing [1,2]
Baseline noise was evaluated by collecting data for an experiment in which a dust sensor was sealed within a closed container with clean, particle-free air present
Observed, a wide variety of output signals and σblank were observed for the three sensors tested. This suggests that the use of a single calibration equation which relates sensor signsaiglntoalPtMo PmMasms caosns cceonntcreantitornat(isoonm(esotimmeetsimadevsoacdavteodcaintetdheinlittherealtiuterera) twuares)nwotaas bneosttapbraecs-t tpicrea,catisceth, aesbtahcekbgarockugnrdouatnzdearot zPeMrocPoMncceonntrcaetniotrnatvioarnievdarsiiegdnisfiigcnanifitlcyanatmlyoanmg odnegvidceesv.ices
Summary
The internet of things (IoT) offers substantial promise concerning efforts to integrate sensors and electronic control devices to improve the quality of human life and wellbeing [1,2]. Continued growth of the internet of things (IoT) requires further development and characterization of low-cost and low-power solid state sensing devices to pair with the many wireless communication devices developed and marketed within the past decade. Reaching the full potential of the IoT will require the rapid advent and implementation of the sensing devices desired by consumers. Such sensors are desired for use in monitoring a wide variety of conditions, ranging from human and animal health, irrigation, air quality, cleanliness, temperature, and weather conditions—and in the home, to monitor the status of interior lighting, door locks, water use, or even which products to purchase from the market
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