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利用广义加性模型研究南加州大气颗粒物物种浓度的排放及气象影响。

Emissions and meteorological impacts on PM species concentrations in Southern California using generalized additive modeling.

机构信息

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA; Now at Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.

出版信息

Sci Total Environ. 2023 Sep 15;891:164464. doi: 10.1016/j.scitotenv.2023.164464. Epub 2023 May 27.

Abstract

The chemical composition of PM has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R = 0.6 to 0.7) and annual concentrations (R = 0.93 to 0.99). Precursor emissions most significantly affect PM species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM composition. In the future, these meteorological factors will become more significant in affecting PM speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM species will decrease in the future except for OC, which will become the largest contributor to PM.

摘要

PM 的化学成分对人类健康和空气质量有重大影响,准确了解其化学成分有助于识别主要的排放源。评估和量化各种因素(如排放、气象和大尺度气候模式)对主要 PM 化学组分的影响,可以为未来实施有效法规以改善空气质量提供指导。在本研究中,我们开发了广义加性模型(GAMs),以评估排放、气象因素和大尺度气候指数如何影响 2002 年至 2019 年南海岸大气流域(SoCAB)中的铵、硫酸盐、硝酸盐、元素碳和有机碳。研究了 SoCAB 三个站点的浓度趋势。统计结果表明,GAMs 可以捕捉这些物种日浓度(R=0.6 至 0.7)和年浓度(R=0.93 至 0.99)的可变性。尽管气象因素如最高温度、相对湿度、风速和边界层高度也会影响 PM 组成,但前体排放对 PM 物种生成的影响最大。未来,这些气象因素将在影响 PM 分馏方面变得更加重要,尽管排放仍将强烈影响其形成。结果表明,除 OC 外,大多数 PM 物种的组成将在未来减少,OC 将成为 PM 的最大贡献者。

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