CAJ | 학술논문

中国城市空气污染问题已经引起广泛关注。目前相关研究很多，但是以空间位置为拟合参数，对空气质量进行回归模拟的研究较少。以2010年中国地级以上城市SO2年均质量浓度为因变量，分别应用普通线性回归和地理加权回归（GWR）模型模拟SO2年均质量浓度，其中地理加权回归方法考虑了空间位置的影响并以此作为回归参数。回归的自变量指标体系包括气象要素（多年平均温度、光照、降水）、植被覆盖（NDVI）、地形要素（坡度、坡向、起伏度）、人为因素（GDP、能源消费）几个方面。由于各指标之间存在较强的相关性，用主成分分析方法计算得到温度、日照、降水、NDVI表征的气象植被综合指标，高程、坡度、起伏度表征的地形综合指标，和GDP、能源消费表征的人为因素综合指标。用3个综合指标值作为自变量进行回归模拟。普通回归结果较差，其r2为0.11，矫正的r2为0.10；GWR模型模拟结果相对较好，其拟合优度显著提高，r2为0.66，矫正的r2为0.47。因此，地理加权回归适合进行此类拟合，普通线性回归不适合。通过对比地理加权回归模拟的各个城市的拟合优度，发现年均质量浓度数值较高的地区拟合效果较差，这些地区主要集中在中国华北和南部部分地区。与基于机理的模型相比，GWR 模型和其各具优缺点，GWR 的优势主要表现在数据及其格式化要求低，计算机软硬件条件要求低，运算速度快等。
중국성시공기오염문제이경인기엄범관주。목전상관연구흔다，단시이공간위치위의합삼수，대공기질량진행회귀모의적연구교소。이2010년중국지급이상성시SO2년균질량농도위인변량，분별응용보통선성회귀화지리가권회귀（GWR）모형모의SO2년균질량농도，기중지리가권회귀방법고필료공간위치적영향병이차작위회귀삼수。회귀적자변량지표체계포괄기상요소（다년평균온도、광조、강수）、식피복개（NDVI）、지형요소（파도、파향、기복도）、인위인소（GDP、능원소비）궤개방면。유우각지표지간존재교강적상관성，용주성분분석방법계산득도온도、일조、강수、NDVI표정적기상식피종합지표，고정、파도、기복도표정적지형종합지표，화GDP、능원소비표정적인위인소종합지표。용3개종합지표치작위자변량진행회귀모의。보통회귀결과교차，기r2위0.11，교정적r2위0.10；GWR모형모의결과상대교호，기의합우도현저제고，r2위0.66，교정적r2위0.47。인차，지리가권회귀괄합진행차류의합，보통선성회귀불괄합。통과대비지리가권회귀모의적각개성시적의합우도，발현년균질량농도수치교고적지구의합효과교차，저사지구주요집중재중국화북화남부부분지구。여기우궤리적모형상비，GWR 모형화기각구우결점，GWR 적우세주요표현재수거급기격식화요구저，계산궤연경건조건요구저，운산속도쾌등。
The problems of city air pollution have attracted worldwide attention. There’re various kinds of researches on air pollution, while very few of them are on the air quality regression considering the space location as the fitting parameter. This research respectively applies ordinary linear regression and geographically weighted regression model (GWR) to simulate the annual average SO2 concentration of the prefecture-level cities in 2010 in China, with annual average SO2 concentration as the dependent variable. The effect of spatial location is considered and taken as a regression parameter in the GWR. The indicator system of independent variables in the research includes meteorological factors (annual average temperature, sunlight, rainfall), vegetation cover (NDVI), topography (slope, slope aspect e, relief) and human factors (GDP, energy consumption). As there is a strong correlation among the indicators, the principal component analysis method is adopted to calculate these comprehensive indexes: the meteorological &vegetation index represented by temperature, sunlight, precipitation and NDVI;the topographic index represented by the elevation, slope and relief;and the human factors index represented by GDP and energy consumption. The regression simulation is conducted with these three comprehensive indexes as independent variables. Compared with the ordinary regression model, whose r2 is 0.11 and corrected r2 is 0.10, the simulation result of GWR model is better with much improved fitting. Its r2 is 0.66, and corrected r2 is 0.47. Therefore, geographically weighted regression is suitable for this kind of fitting, while the ordinary linear regression is not. By comparing the fitting in each city, we found the cities with higher annual average SO2 concentration had poor fitting effects, which were mainly concentrated in North China and South China. Compared with the models based on the mechanism, the GWR model has its own advantages, such as lower requirements of data and formatting, low requirements of computer software and hardware conditions, and faster speed of operation, and so on.