The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments

Authors
Citation
S. Sillman, The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments, ATMOS ENVIR, 33(12), 1999, pp. 1821-1845
Citations number
122
Categorie Soggetti
Environment/Ecology,"Earth Sciences
Journal title
ATMOSPHERIC ENVIRONMENT
ISSN journal
13522310 → ACNP
Volume
33
Issue
12
Year of publication
1999
Pages
1821 - 1845
Database
ISI
SICI code
1352-2310(199906)33:12<1821:TRBONA>2.0.ZU;2-Z
Abstract
Research over the past ten years has created a more detailed and coherent v iew of the relation between O-3 and its major anthropogenic precursors, vol atile organic compounds (VOC) and oxides of nitrogen (NOx). This article pr esents a review of insights derived from photochemical models and field mea surements. The ozone-precursor relationship can be understood in terms of a fundamental split into a NOx-senstive and VOC-sensitive (or NOx-saturated) chemical regimes. These regimes are associated with the chemistry of odd h ydrogen radicals and appear in different forms in studies of urbanized regi ons, power plant plumes and the remote troposphere. Factors that affect the split into NO, sensitive and VOC-sensitive chemistry include: VOC/NOx rati os, VOC reactivity, biogenic hydrocarbons, photochemical aging, and rates o f meteorological dispersion. Analyses of ozone-NOx-VOC sensitivity from 3D photochemical models show a consistent pattern, but predictions for the imp act of reduced NOx and VOC in indivdual locations are often very uncertain. This uncertainty can be identified by comparing predictions from different model scenarios that reflect uncertainties in meteorology, anthropogenic a nd biogenic emissions. Several observation-based approaches have been propo sed that seek to evaluate ozone-NOx-VOC sensitivity directly from ambient m easurements (including ambient VOC, reactive nitrogen, and peroxides). Obse rvation-based approaches have also been used to evaluate emission rates, oz one production efficiency, and removal rates of chemically active species. Use of these methods in combination with models can significantly reduce th e uncertainty associated with model predictions. (C) 1999 Elsevier Science Ltd. All rights reserved.