Abstract
Abstract. Statistical response surface methodology (RSM) is successfully applied for a Community Multi-scale Air Quality model (CMAQ) analysis of ozone sensitivity studies. Prediction performance has been demonstrated through cross validation, out-of-sample validation and isopleth validation. Sample methods and key parameters, including the maximum numbers of variables involved in statistical interpolation and training samples have been tested and selected through computational experiments. Overall impacts from individual source categories which include local/regional NOx and VOC emission sources and NOx emissions from power plants for three megacities – Beijing, Shanghai and Guangzhou – were evaluated using an RSM analysis of a July 2005 modeling study. NOx control appears to be beneficial for ozone reduction in the downwind areas which usually experience high ozone levels, and NOx control is likely to be more effective than anthropogenic VOC control during periods of heavy photochemical pollution. Regional NOx source categories are strong contributors to surface ozone mixing ratios in three megacities. Local NOx emission control without regional involvement may raise the risk of increasing urban ozone levels due to the VOC-limited conditions. However, local NOx control provides considerable reduction of ozone in upper layers (up to 1 km where the ozone chemistry is NOx-limited) and helps improve regional air quality in downwind areas. Stricter NOx emission control has a substantial effect on ozone reduction because of the shift from VOC-limited to NOx-limited chemistry. Therefore, NOx emission control should be significantly enhanced to reduce ozone pollution in China.
Highlights
Tropospheric ozone is a key air pollutant that affects human health, crop productivity and natural ecosystems, and a greenhouse gas that impacts global climate
The results of the response surface methodology (RSM) modeling case LHS1 30 were used as a “quasi-response” in preliminary experiments
A strong linear relationship (y = x) between Community Multi-scale Air Quality model (CMAQ) and RSM datasets were found in all areas for both cases, with the R-square values larger than 0.99
Summary
Tropospheric ozone is a key air pollutant that affects human health, crop productivity and natural ecosystems, and a greenhouse gas that impacts global climate. During the past two decades, rapid economic growth in China has resulted in a significant increase in the emissions of ozone precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOC) (Ohara et al, 2007; Wei et al, 2008; Zhang et al, 2009a). High ozone concentrations over 200 μg m−3 (approximately 103 ppb, the 1-h maximal concentration defined by National Ambient Air Quality Standard of China, Class II) have been frequently observed by in-situ monitoring in east China Wang et al, 2006; Zhang et al, 2008; Tang et al, 2009; Ran et al, 2009; Shao et al, 2009) High ozone concentrations over 200 μg m−3 (approximately 103 ppb, the 1-h maximal concentration defined by National Ambient Air Quality Standard of China, Class II) have been frequently observed by in-situ monitoring in east China (H. Wang et al, 2006; T. Wang et al, 2006; Z. Wang et al, 2006; Zhang et al, 2008; Tang et al, 2009; Ran et al, 2009; Shao et al, 2009)
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