Explore Air-Quality Mobile-Monitoring Data
Overview
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.
Install
Health
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespaces in Imports field not imported from: ‘Cairo’ ‘DT’ ‘XML’ ‘caTools’ ‘data.table’ ‘dplyr’ ‘ggplot2’ ‘htmltools’ ‘leaflet’ ‘lubridate’ ‘plotly’ ‘shinyjs’ ‘stringr’ ‘xts’ ‘zoo’ All declared Imports should be used.
- NOTE11 OK, 2 NOTE2026-06-09
- ERROR10 OK, 2 NOTE, 1 ERROR2026-06-08
- NOTE9 OK, 5 NOTE2026-03-10
Documentation
- Help pages
- 2 (1 internal)includes a package overview page
- Examples that run
- 100%1 run in a plain check (under a condition)
- Exported functions with examples
- 100%
- Documented parameters
- not tracked
- Return-value docs
- 0%
- Help pages citing references
- 0%
Downloads
Repository
- PRs merged (all time)
- 50%
- Median days to close, 50 most recently updated closed issues
- 6
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
Issues over time
PRs over time
6 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/meenakshi-kushwaha/mmaqshiny on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
Dependencies
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.0.0Latest2020-06-26 · current release
- RR 4.0.0 released · 2020-04-24
Package metadata
- First published
- 2020-06-26
- Total releases
- 1 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 4.7 MB
Cite
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