openair is an R package primarily developed for the analysis of air pollution measurement data but which is also of more general use in the atmospheric sciences. The package consists of many tools for importing and manipulating data, and undertaking a wide range of analyses to enhance understanding of air pollution data. In this paper we consider the development of the package with the purpose of showing how air pollution data can be analysed in more insightful ways. Examples are provided of importing data from UK air pollution networks, source identification and characterisation using bivariate polar plots, quantitative trend estimates and the use of functions for model evaluation purposes. We demonstrate how air pollution data can be analysed quickly and efficiently and in an interactive way, freeing time to consider the problem at hand. One of the central themes of openair is the use of conditioning plots and analyses, which greatly enhance inference possibilities. Finally, some consideration is given to future developments.

openair - An R package for air quality data analysis

. We present a new data set of annual historical (1750–2014) anthropogenic chemically reactive gases (CO, CH4, NH3, NOx, SO2, NMVOCs), carbonaceous aerosols (black carbon – BC, and organic carbon – OC), and CO2 developed with the Community Emissions Data System (CEDS). We improve upon existing inventories with a more consistent and reproducible methodology applied to all emission species, updated emission factors, and recent estimates through 2014. The data system relies on existing energy consumption data sets and regional and country-specific inventories to produce trends over recent decades. All emission species are consistently estimated using the same activity data over all time periods. Emissions are provided on an annual basis at the level of country and sector and gridded with monthly seasonality. These estimates are comparable to, but generally slightly higher than, existing global inventories. Emissions over the most recent years are more uncertain, particularly in low- and middle-income regions where country-specific emission inventories are less available. Future work will involve refining and updating these emission estimates, estimating emissions' uncertainty, and publication of the system as open-source software.

/pdf/historical-1750-2014-anthropogenic-emissions-of-reactive-4l2fje0yoy.pdf

Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS)

http://www.indiaenvironmentportal.org.in/files/urban%20land%20transport.pdf

Public health benefits of strategies to reduce greenhouse-gas emissions: urban land transport

Transport and climate change: a review

https://research.birmingham.ac.uk/portal/files/11961623/Estimation_of_the_Contribution_of_Road_Traffic_Emissions_V3_PostPrint.pdf

Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review

Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions

Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles

https://www.londonair.org.uk/london/reports/CCS2005.pdf

The impact of congestion charging on vehicle emissions in London

In this paper a new receptor modelling method is developed to identify and characterise emission sources. The method is an extension of the commonly used conditional probability function (CPF). The CPF approach is extended to the bivariate case to produce a conditional bivariate probability function (CBPF) plot using wind speed as a third variable plotted on the radial axis. The bivariate case provides more information on the type of sources being identified by providing important dispersion characteristic information. By considering intervals of concentration, considerably more source information can be revealed that is absent in the basic CPF or CBPF. We demonstrate the application of the approach by considering an area of high source complexity, where many new sources can be identified and characterised compared with currently used techniques. Dispersion model simulations are undertaken to verify the approach. The technique has been made available through the openair R package.

http://www.openair-project.org/PDF/OpenAir_bivariate_polar_plot_technique.pdf

David C. Carslaw

Papers

openair - An R package for air quality data analysis

Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions

Recent evidence concerning higher NOx emissions from passenger cars and light duty vehicles

The impact of congestion charging on vehicle emissions in London

Conditional bivariate probability function for source identification