摘要: |
总结了国内外研究中常用的基于外场观测的臭氧污染成因分析方法。区域传输和本地生成的相对贡献以及臭氧与前体物的非线性关系是研究臭氧污染和制定控制对策的两个关键科学问题。基于对观测数据的分析,常见的量化区域传输和本地生成贡献的方法包括背景点测量法、TCEQ区域背景臭氧估算法和主成分分析区域背景臭氧估算法;用于诊断臭氧光化学生成机制的方法包括光化学指示剂比值法和基于观测的化学模型。本文对上述方法的原理和应用情况进行了总结,并对其优缺点和适用条件进行了评述,以期为环境监测资料的深入科学分析提供参考和借鉴。 |
关键词: 臭氧污染 外场观测 区域背景臭氧 臭氧前体物关系 光化学指示剂 臭氧生成效率 基于观测的模型 空气质量监测 |
DOI:10.7515/JEE201706001 |
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基金项目:国家自然科学基金项目(41675118) |
英文基金项目:National Natural Science Foundation of China (41675118) |
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Review on the observation-based methods for ozone air pollution research |
WU Lin, XUE Likun, WANG Wenxing
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Environment Research Institute, Shandong University, Jinan 250100, China
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Abstract: |
Background, aim, and scope Ozone (O3) is among the most important trace gases in the atmosphere. It plays a central role in the tropospheric chemistry as a major precursor of the hydroxyl radial, and is also a potent greenhouse gas and an important air pollutant. In the troposphere, ozone is mainly produced by photochemical reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of sunlight. Ozone air pollution was first recognized in the Los Angles smog in 1950s, and has since then become a major challenge of air quality management in developed countries. It is fundamental to quantify the relative contributions of both regional transport and local production and to diagnose the non-linear O3-precursor relationships, to formulate the science-based control strategies. For decades, a large body of research has been conducted worldwide to understand the ozone pollution, from which dozens of effective methods based on field observations have been developed. In recent years, China has suffered from widespread and worsening O3 air pollution, and has strengthened the monitoring of O3 and its precursors nationwide since 2013. Therefore, it is necessary to summarize the commonly used observation-based methods for ozone pollution research, which would provide reference for the future analysis of air quality monitoring data and better understanding the causes of ozone pollution in China. Materials and methods We systematically reviewed the available open literatures that utilized the observation-based approaches to understand O3 pollution. Based on this, we choose several most commonly used methods for detailed description in this article. Results We introduced two kinds of observation-based approaches, including seven specific methods, corresponding to the above mentioned two fundamental scientific questions. Three methods, namely, “Observations at regional background sites”, “TCEQ (Texas Commission on Environmental Quality) method” and “Principal Component Analysis (PCA) method”, have been proved useful to determine the regional background ozone in a given area. Photochemical indicators and observation-based model (OBM) are effective tools for diagnosing the O3 formation regimes, and the commonly used photochemical indicators include ozone production efficiency (OPE), H2O2 / NOz (or H2O2 / HNO3) ratios, and satellite retrieved HCHO/NO2 ratios. The principles and application cases of these methods are described in detail in this article. Discussion We also commented on the advantages, disadvantages and potential applications of these methods in the future research of O3 air pollution in China. All of these methods have been proved useful and efficient by previous observation studies, and also have their own unique characteristics. Considering that China has developed an excellent national air quality monitoring network, the TCEQ and PCA methods can be easily adopted to quantify both regional background O3 and locally produced O3 for any given city/region, and the OPE method has prosperous applications to estimate the O3 formation regimes in national/regional scales. Photochemical indicators based on satellite retrievals have evident advantages of wider spatial coverage and high continuous time resolution, but have inherent shortcomings of high uncertainties for satellite data. The OBM is the most sophisticated tool for diagnosing O3 formation regimes as it can provide detailed information on the sensitivity of O3 production to the variety of VOC precursors. Conclusions We reviewed the observation-based methods that have been widely applied to understand ozone air pollution previously. Seven common methods were documented in this paper, which can be combined with air quality monitoring data to quantify the relative contributions of both regional background and local production and to examine the non-linear relationships between O3 and its precursors. In view of the worsening prospect of O3 pollution and the fast development of air quality monitoring network in China, these methods have broad applications for O3 air pollution research in the future and hence support the formulation of effective control measures. Recommendations and perspectives Based on the above analysis, the following specific recommendations can be made to support the future O3 pollution control in China. First, TCEQ and/or PCA methods can be applied to analyze air quality monitoring data to quantify regional and local contributions of ozone pollution in major cities or city clusters, with which the regional coordinated control strategies can be established. Second, we suggest adding NOy measurements to the current air quality monitoring network to facilitate fast examination of O3 formation regimes in regional scale through OPE analysis. Third, real-time measurements of ozone precursors, especially VOCs, should be performed to support OBM analysis, which can provide the most comprehensive information on O3 formation regimes and key O3 precursor species. Last but not least, the satellite-based photochemical indicators can provide information on O3 formation regimes at higher spatial and temporal resolutions. It is thus recommended to further develop the capacity of satellite remote sensing of atmospheric constituents, particularly NO2, formaldehyde and glyoxal. |
Key words: ozone pollution field observations regional background ozone ozone-precursor relationship photochemical indicators ozone production efficiency observation-based model air quality monitoring |