2014—2016年海口市空气质量概况及预报效果检验

本文主要基于CUACE模式在海口市的预报产品，结合2014年3月—2017年2月海口市AQI、PM_2.5、PM₁₀和O₃的实况资料进行预报效果检验.结果表明，①近3年海口市空气质量等级主要以优和良为主，但仍有少部分天数以PM₁₀、PM_2.5和O₃为首要污染物，分别占所有首要污染物天数的27.6%、29.5%和42.9%，其中O₃上升幅度较快.②CUACE模式能较好的模拟出AQI和3类污染物浓度的变化特征，其中PM_2.5的预报值与实测值最为接近，而PM₁₀和O₃普遍偏低.③日平均浓度的预报效果检验表明，PM_2.5的标准误差（RMSE）最小，AQI和PM₁₀次之，O₃最大.3个时次预报平均偏差（MB）和归一化偏差（MNB）均为负值，表明CUACE模式预报的污染要素浓度均偏低于实测值.④海口市空气质量为优等级时，TS评分最高；无首要污染物时，首要污染物预报的TS评分最高，但首要污染物为PM_2.5、PM₁₀或O₃时，TS评分均偏低.

Verification of air quality forecasted by CUACE model in Haikou City during 2014 to 2016

Based on the forecast product of CUACE model output, this study verified the forecast efficiency of AQI, PM_2.5, PM₁₀ and O₃ in Haikou City by the observed data from March 2014 to February 2017. In observations, air quality in Haikou City was mainly kept at excellent and good in these three years and the primary pollutants were PM_2.5, PM₁₀ and O₃, which accounted for 27.6%, 29.5% and 42.9%, respectively. Basic features of AQI, PM_2.5, PM₁₀ and O₃ simulated by CUACE model were close to observations in Haikou City. The model reasonably simulates the concentration of PM_2.5 but relatively underestimate the concentration of PM₁₀ and O₃, which can also be reflected by the Mean Bias (MB) and Mean Normalized Bias (MNB) values shown by CUACE forecasted at the three times. In addition, the Root Mean Square Error (RMSE) is the smallest for the daily averaged concentration of PM_2.5, following AQI and PM₁₀, and is largest for O₃. As a further step, the model performs the best when the air quality is excellent or there is no primary pollutant in Haikou City, as measured by the TS score. The TS score is relatively low when the primary pollutants were PM_2.5, PM₁₀ or O₃.