通过分析2013—2017年海口市风向频率、地面PM_(2.5)浓度及海口市所处北部湾地理位置,确定12月为北部湾对海口市最不利风向时间段.利用中尺度气象模式(WRF,Weather Research Forecast)驱动空气质量模型(CMAQ,Community Multi-scale Air Quality),设置一系列数值模拟情景,深入分析北部湾人为源对海口市PM_(2.5)浓度影响.结果表明:WRF/CMAQ能很好地再现北部湾气象场和PM_(2.5)浓度的时空分布.2013年12月,北部湾人为源对海口市PM_(2.5)平均贡献率约为45.4%,其中约有90%来源于海口市自身人为源,约有10%来源于广东广西片区,海南片区除海口外其余市县贡献可忽略不计.污染时段,北部湾和海口市自身贡献率均下降,平均贡献率分别为40%和36%,表明污染时段海口市PM_(2.5)主要源区不仅来自北部湾.通过分析后向轨迹,发现污染时段均会经过一个关键区——珠三角区域,表明珠三角区域很有可能也是造成2013年12月海口市PM_(2.5)污染的主要源区.清洁时段,北部湾和海口市自身贡献率均上升,平均贡献率分别为52%和48%,表明北部湾对海口市PM_(2.5)浓度影响在清洁时段更显著.因此,北部湾未来产业规划值得关注,因为这些产业很有可能使目前海口市清洁时段变为污染时段,导致空气质量下降. 相似文献
The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems.
Environmental Science and Pollution Research - 2,4-Dichlorophenol (2,4-DCP) is a hazardous chlorinated organic chemical, so its removal is an important task to protect the whole ecosystem and human... 相似文献