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Multi-year objective analyses of warm season ground-level ozone and PM_(2.5) over North America using real-time observations and Canadian operational air quality models

机译:利用实时观测和加拿大运营空气质量模型,北美温暖季节臭氧和PM_(2.5)的多年客观分析

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Multi-year objective analyses (OA) on a high spatiotemporal resolution for the warm season period (1 May to 31 October) for ground-level ozone and for fine particulate matter (diameter less than 2.5 microns (PM_(2.5))) are presented. The OA used in this study combines model outputs from the Canadian air quality forecast suite with US and Canadian observations from various air quality surface monitoring networks. The analyses are based on an optimal interpolation (OI) with capabilities for adaptive error statistics for ozone and PM_(2.5) and an explicit bias correction scheme for the PM_(2.5) analyses. The estimation of error statistics has been computed using a modified version of the Hollingsworth-L?nnberg (H-L) method. The error statistics are "tuned" using a x~2 (chi-square) diagnostic, a semiempirical procedure that provides significantly better verification than without tuning. Successful cross-validation experiments were performed with an OA setup using 90% of data observations to build the objective analyses and with the remainder left out as an independent set of data for verification purposes. Furthermore, comparisons with other external sources of information (global models and PM_(2.5) satellite surface-derived or ground-based measurements) show reasonable agreement. The multi-year analyses obtained provide relatively high precision with an absolute yearly averaged systematic error of less than 0.6 ppbv (parts per billion by volume) and 0.7 μgm~(-3) (micrograms per cubic meter) for ozone and PM_(2.5), respectively, and a random error generally less than 9 ppbv for ozone and under 12 μgm~(-3) for PM_(2.5). This paper focuses on two applications: (1) presenting longterm averages of OA and analysis increments as a form of summer climatology; and (2) analyzing long-term (decadal) trends and inter-annual fluctuations using OA outputs. The results show that high percentiles of ozone and PM_(2.5) were both following a general decreasing trend in North America, with the eastern part of the United States showing the most widespread decrease, likely due to more effective pollution controls. Some locations, however, exhibited an increasing trend in the mean ozone and PM_(2.5), such as the northwestern part of North America (northwest US and Alberta). Conversely, the low percentiles are generally rising for ozone, which may be linked to the intercontinental transport of increased emissions from emerging countries. After removing the decadal trend, the inter-annual fluctuations of the high percentiles are largely explained by the temperature fluctuations for ozone and to a lesser extent by precipitation fluctuations for PM_(2.5). More interesting is the economic short-term change (as expressed by the variation of the US gross domestic product growth rate), which explains 37% of the total variance of inter-annual fluctuations of PM_(2.5) and 15% in the case of ozone.
机译:为温暖季节期(10月3日至10月31日)的高时的客观分析(OA)用于地面臭氧和细颗粒物质(直径小于2.5微米(PM_(2.5))) 。本研究中使用的OA结合了来自各种空气质量表面监测网络的美国和加拿大观测的加拿大空气质量预测套件的模型输出。分析基于最佳插值(OI),具有用于臭氧和PM_(2.5)的自适应误差统计的功能以及PM_(2.5)分析的显式偏置校正方案。使用Hollingsworth-L?Nnberg(H-L)方法的修改版本来计算误差统计的估计。错误统计数据是使用x〜2(chi-square)诊断的“调谐”,一个半级过程,可提供明显更好的验证而不是调谐。使用90%的数据观测进行了成功的交叉验证实验,以使用90%的数据观察来构建目标分析,并且随后被遗漏为独立的一组数据,以进行验证。此外,与其他外部信息来源的比较(全球模型和PM_(2.5)卫星表面导出或地面测量)显示合理的协议。获得的多年分析提供了相对高的精度,具有绝对的年平均系统误差小于0.6 ppbv(亿亿卢比的零件)和0.7μgm〜(-3)(每立方米的微克),用于臭氧和PM_(2.5)分别和随机误差通常小于9ppbv,用于臭氧和12μgm〜(-3)的PM_(2.5)。本文重点介绍了两种应用:(1)呈现OA的长期平均值和分析增量作为夏季气候学的形式; (2)使用OA产出分析长期(Decadal)趋势和年间波动。结果表明,臭氧和PM_(2.5)的高百分位数均在北美一般下降趋势之后,美国东部显示最广泛的减少,可能由于更有效的污染管制。然而,某些地方在平均臭氧和PM_(2.5)中呈现出越来越大的趋势,例如北美西北部(美国西北部和艾伯塔省)。相反,低百合含量通常是臭氧的上升,这可能与来自新兴国家的增加的排放的洲际运输有关。在去除二道趋势之后,高百分位的年间波动主要由臭氧的温度波动和通过PM_(2.5)的降水波动较小的程度来解释。更有趣的是经济短期变化(如美国国内生产总值的变化所表达),这解释了PM_(2.5)的年度白年间波动总差异的37%,而15%臭氧。

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