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The relation between ozone,NOx and hydrocarbons in urban and polluted rural environments
Affiliation:1. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong;2. Environment Research Institute, Shandong University, Ji''nan, Shandong, China;3. School of Energy and Environment, City University of Hong Kong, Hong Kong;4. Shanghai Academy of Environmental Sciences, Shanghai, China;1. Indian Institute of Tropical Meteorology (Branch), Prof Ram NathVij Marg, New Delhi 110060, India;2. TERI University, New Delhi 110070, India
Abstract:Research over the past ten years has created a more detailed and coherent view of the relation between O3 and its major anthropogenic precursors, volatile organic compounds (VOC) and oxides of nitrogen (NOx). This article presents a review of insights derived from photochemical models and field measurements. 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 hydrogen radicals and appear in different forms in studies of urbanized regions, power plant plumes and the remote troposphere. Factors that affect the split into NOx-sensitive and VOC-sensitive chemistry include: VOC/NOx ratios, VOC reactivity, biogenic hydrocarbons, photochemical aging, and rates of meteorological dispersion. Analyses of ozone–NOx–VOC sensitivity from 3D photochemical models show a consistent pattern, but predictions for the impact 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 and biogenic emissions. Several observation-based approaches have been proposed that seek to evaluate ozone–NOx–VOC sensitivity directly from ambient measurements (including ambient VOC, reactive nitrogen, and peroxides). Observation-based approaches have also been used to evaluate emission rates, ozone production efficiency, and removal rates of chemically active species. Use of these methods in combination with models can significantly reduce the uncertainty associated with model predictions.
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