Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes

Li, Xueke1; Seth, Anji1; Zhang, Chuanrong1; Feng, Ran2; Long, Xin3; Li, Weidong1; Liu, Kai4

2020

10.1016/j.atmosenv.2019.117181

Atmospheric Environment   影响因子和分区

18732844

1873-2844

Fine particulate matter (PM2.5, with aerodynamic diameters < 2.5 μm) pollution is one of the most pervasive air quality problems facing the world. Reliable air quality modeling of PM2.5 is essential to future air quality projection, which serves as a critical source of information for policy-making. Although various evaluation methods have been suggested to assess the capability of air quality models in reproducing PM2.5, most of studies were focused on the mean behaviors of air quality models, with little emphasis on extreme conditions, which may be more crucial for human health and climate change. To address this need, we proposed an evaluation framework in this study to characterize both mean and extreme conditions of PM2.5 and applied it to the WRF-CMAQ simulations over contiguous United States for the period of 2001–2010. Results from statistical, spatiotemporal, and extreme quantile evaluation methods show consistent good performance of the model in the Eastern U.S. However, PM2.5 mean variations and extreme trends in the western U.S. are not well represented by the model attributable to the existence of complex terrains and active fire activities. In addition, the magnitude of decreasing trends for extreme events is smaller than that for the mean PM2.5. Strong correspondence is found between PM2.5 extremes and meteorological extremes that are associated with a stagnant condition. More extreme PM2.5 pollution episodes are expected in a warming climate, with rural regions and the western U.S. suffering the most. Our results highlight the urgency for proper forest management and joint-control of air quality and carbon emissions in order to combat extreme air pollution events in the future.
© 2019 Elsevier Ltd

Pine particulate matter Extreme quantile analysis Model performance WRF-CMAQ

SCI ; EI

英语

其他

National Science Foundation[1056216]

Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences

Environmental Sciences ; Meteorology & Atmospheric Sciences

WOS:000510946800053

Elsevier Ltd

20194907791463

Air pollution control ; Air quality ; Climate change ; Climate models ; Particles (particulate matter) ; Petroleum reservoir evaluation ; Quality assurance

Meteorology:443 ; Atmospheric Properties:443.1 ; Air Pollution Control:451.2 ; Petroleum Deposits : Development Operations:512.1.2 ; Quality Assurance and Control:913.3 ; Mathematics:921 ; Materials Science:951

GEOSCIENCES

Web of Science

1.Department of Geography, University of Connecticut, Storrs; CT; 06269, United States
2.Department of Geosciences, University of Connecticut, Storrs; CT; 06269, United States
3.School of Environment Science and Engineering, Southern University of Science and Technology, Shenzhen; 518055, China
4.Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing; 100101, China

Li, Xueke,Seth, Anji,Zhang, Chuanrong,et al. Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes[J]. Atmospheric Environment,2020,222.

Li, Xueke.,Seth, Anji.,Zhang, Chuanrong.,Feng, Ran.,Long, Xin.,...&Liu, Kai.(2020).Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes.Atmospheric Environment,222.

Li, Xueke,et al."Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes".Atmospheric Environment 222(2020).