Abstract
There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.
Highlights
There has been a growing interest in air quality monitoring in recent years with a large number of epidemiological studies demonstrating a link between human health diseases and air pollution (e.g., [1,2,3,4])
To quantify the improvement of the correction method, a statistical analysis was performed, and the following parameters were calculated for PM1 and PM2.5 data: mean value of measurements, 3.1
A recent study has focused on the effects of relative humidity on measurements [16]
Summary
There has been a growing interest in air quality monitoring in recent years with a large number of epidemiological studies demonstrating a link between human health diseases and air pollution (e.g., [1,2,3,4]). Concentrations of particulate mass are generally highly structured both spatially and temporally, and personal exposure to air pollution can differ significantly even on the street scale [9,10]. For this reason, there have been numerous attempts at producing low-cost portable PM sensors to create monitoring networks with much higher spatial resolution [11,12,13] or for personal monitoring [14]. There are standard limits for exposure to particle with a mean aerodynamic diameter less than 10 μm (PM10 ) and 2.5 μm (PM2.5 ) [5,6], some studies highlight the importance of exposure to smaller particles (e.g., PM1 ) [7,8].
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