English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The main objective of this chapter is to introduce a mathematical method for enhancing the correctness of the output results of air pollution dispersion models via the calibration of input background concentrations. For developing this method, an air pollution model was set up in ADMS‐Roads for a study area in the City of Nottingham in the UK. The method was applied iteratively to the input background concentrations, which effectively reduced the error between calculated and monitored air pollution concen‐ trations on both the annual mean and hourly levels. The inclusion of the traffic flow profiles of the modeled road network reduced further the error between the hourly, but not the annual mean, calculated and monitored concentrations. The application of the calibration approach to the model in ADMS‐Roads was compared to the use of grid air pollution sources for the same model in ADMS‐Urban. In terms of the accuracy of the model results, the calibration approach was better than using grid sources on the annual mean level and was much better on the hourly level. Compared to the use of grid sources in ADMS‐Urban, the use of the calibration approach in either ADMS‐Roads or ADMS‐ Urban can significantly reduce the air pollution model runtime.

A Mathematical Approach to Enhance the Performance of Air Pollution Models