mmaqshiny_run

<p>Mobile-monitoring or &lt;U+201C&gt;sensors on a mobile platform&lt;U+201D&gt;, is an increasingly
popular approach to measure high-resolution pollution data at the street level.
Coupled with location data, spatial visualisation of air-quality parameters
helps detect localized areas of high air-pollution, also called hotspots.
In this approach, portable sensors are mounted on a vehicle and driven on
predetermined routes to collect high frequency data (1 Hz). The package is
for analysing, visualising and spatial maps of high-resolution air-quality
data collected by specific devices installed on a moving platform.</p>

mmaqshiny

Mobile-monitoring or "sensors on a mobile platform", is an increasingly
popular approach to measure high-resolution pollution data at the street level.
Coupled with location data, spatial visualisation of air-quality parameters
helps detect localized areas of high air-pollution, also called hotspots.
In this approach, portable sensors are mounted on a vehicle and driven on
predetermined routes to collect high frequency data (1 Hz).
'mmaqshiny' is for analysing, visualising and spatial mapping of
high-resolution air-quality data collected by specific devices installed on
a moving platform. 1 Hz data of PM2.5 (mass concentrations of particulate
matter with size less than 2.5 microns), Black carbon mass concentrations
(BC), ultra-fine particle number concentrations, carbon dioxide along with
GPS coordinates and relative humidity (RH) data collected by popular
portable instruments (TSI DustTrak-8530, Aethlabs microAeth-AE51, TSI CPC3007,
LICOR Li-830, Garmin GPSMAP 64s, Omega USB RH probe respectively). It
incorporates device specific cleaning and correction algorithms. RH correction
is applied to DustTrak PM2.5 following the Chakrabarti et al., (2004)
<doi:10.1016/j.atmosenv.2004.03.007>. Provision is given to add linear
regression coefficients for correcting the PM2.5 data (if required). BC data
will be cleaned for the vibration generated noise, by adopting the statistical
procedure as explained in Apte et al., (2011) <doi:10.1016/j.atmosenv.2011.05.028>,
followed by a loading correction as suggested by Ban-Weiss et al., (2009)
<doi:10.1021/es8021039>. For the number concentration data, provision is
given for dilution correction factor (if a diluter is used with CPC3007;
default value is 1). The package joins the raw, cleaned and corrected data
from the above said instruments and outputs as a downloadable csv file.

mmaqshiny_run: mmaqshiny: Explore Air-Quality Mobile-Monitoring Data

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