J Air Waste Manag Assoc. 2014 Apr;64(4):374-87. doi: 10.1080/10962247.2013.778919.
Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM2.5 sulfate (SO4(2-)), nitrate (NO3(-)) and ammonium, and gaseous nitric acid (HNO3) and ammonia (NH3). The model approximately reproduced PM2.5 SO4(2-) concentration, but clearly overestimated PM2.5 NO3(-) concentration, which was attributed to overestimation of production of ammonium nitrate (NH4NO3). This study conducted sensitivity analyses of factors associated with the model performance for PM2.5 NO3(-) concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH3 emission, HNO3 and NH3 dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH3 emission directly affected NH3 concentration, and substantially affected NH4NO3 concentration. Higher dry deposition velocities of HNO3 and NH3 led to substantial reductions of concentrations of the gaseous species and NH4NO3. Because uncertainties in NH3 emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM2.5 NO3(-).
The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.
为了能够设计有效的细颗粒物(PM2.5)控制策略,需要改进空气质量模型。本研究于 2010 年冬季和 2011 年夏季在日本大东京地区应用了多尺度空气质量模型系统。将模型结果与观测到的 PM2.5 硫酸盐(SO4(2-))、硝酸盐(NO3(-)) 和铵浓度,以及气态硝酸(HNO3)和氨(NH3)浓度进行了比较。该模型大致再现了 PM2.5 SO4(2-)浓度,但明显高估了 PM2.5 NO3(-)浓度,这归因于对硝酸铵(NH4NO3)生成的高估。本研究对与 PM2.5 NO3(-)浓度模型性能相关的因素进行了敏感性分析,包括温度和相对湿度、氮氧化物排放、氨排放的季节性变化、HNO3 和 NH3 干沉降速度以及五氧化二氮的非均相反应概率。氨排放的变化直接影响氨浓度,并对 NH4NO3 浓度有很大影响。HNO3 和 NH3 干沉降速度的增加导致气态物种和 NH4NO3 浓度的大幅降低。由于氨排放和干沉降过程的不确定性可能很大,因此这些过程可能是改进 PM2.5 NO3(-)模型性能的关键因素。
多尺度空气质量模型系统明显高估了日本大东京地区细颗粒硝酸盐的浓度,这归因于对硝酸铵生成的高估。对与硝酸盐模型性能相关的因素进行了敏感性分析。氨排放和硝酸及氨的干沉降可能是改进模型性能的关键因素。