| 引用本文: | 李卫朋，李 科，王 杰，文星跃，陈忠升，税攀恒.2017.川东北低山丘陵区细颗粒物主要相关因素和季节变化分析——以南充主城区为例[J].地球环境学报,8(5):439-450 |
| LI Weipeng, LI Ke , WANG Jie, Wen Xingyue, CHEN Zhongsheng, SHUI Panheng.2017.The main related factor and seasonal variation of fine particulate matter concentration in the typical low mountain terrain urban — a case in Nanchong in northeastern Sichuan[J].Journal of Earth Environment,8(5):439-450 |
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| 川东北低山丘陵区细颗粒物主要相关因素和季节变化分析——以南充主城区为例 |
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李卫朋，李 科，王 杰，文星跃，陈忠升，税攀恒
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1.西华师范大学 国土资源学院，南充 637009
2.西华师范大学 区域环境演变与保护研究中心，南充 637009
3.成都理工大学 环境与土木工程学院，成都 610059
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| 摘要: |
| 基于中国空气质量在线监测分析平台和全球天气精准预报网的大气质量和气象数据，以四川盆地东北低山丘陵区典型城市南充市主城区为例，检验了细颗粒物（PM2.5）浓度的概率密度分布，发现其接近对数正态分布，由相关分析确定了PM2.5浓度的主要相关因素为CO、NO2（相关系数r分别为0.76、0.55，P < 0.01），再通过对 2014 年1月— 2016 年 6 月的日数据的逐步回归筛选出最优的回归指标和模拟方程（决定系数R2adj为0.68，P < 0.05），2016年7月 — 2017年6月的数据验证表明模拟效果较好（拟合优度为0.64，相对误差15.48%）；最后根据时序插值、浓度和IAQI（PM2.5）的时段均值发现PM2.5浓度在年际上有降低趋势；在季节上由高到低依次为冬季、春季、秋季、夏季；PM2.5浓度在1月和6月分别呈现出年内的峰值和谷值，5、10月出现了阶段性峰值，尤其是5月；IAQI（PM2.5）的季节变化与浓度变化规律相似；且PM2.5与PM10比值的均值为0.67，表明现阶段南充市主城区大气污染物中细颗粒物占有较大比重。 |
| 关键词: 南充市 细颗粒物 概率密度 相关分析 逐步回归分析 季节变化 |
| DOI：10.7515/JEE201705007 |
| CSTR：32259.14.JEE201705007 |
| 分类号: |
| 基金项目:西华师范大学博士科研启动基金和基本科研业务项目（15E002，15E003，16C003）；国家自然科学基金项目（41671220）；大学生创新创业训练计划项目（201710638051，cxcy2016039） |
| 英文基金项目:Fundamental Research Funds of China West Normal University (15E002, 15E003, 16C003); National Natural Science Foundation of China (41671220); Undergraduate Training Program for Innovation and Entrepreneurship (201710638051, cxcy2016039) |
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| The main related factor and seasonal variation of fine particulate matter concentration in the typical low mountain terrain urban — a case in Nanchong in northeastern Sichuan |
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LI Weipeng, LI Ke , WANG Jie, Wen Xingyue, CHEN Zhongsheng, SHUI Panheng
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1. Land and Resources College, China West Normal University, Nanchong 637009, China
2. Research Center for Regional Environmental Evolution and Conservation, China West Normal University, Nanchong 637009, China
3. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
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| Abstract: |
| Background, aim, and scope Recently, the winter and spring haze with fine particulate matter (PM2.5) as the primary pollutant has spread to most cities in mainland China and focused public attention. Sichuan Basin is one of the main polluted area in southwestern China, in which many studies of haze were carried widely in the provincial capitals, such as Chengdu and Chongqing, while there is relatively little study in small and medium-sized cities. Nanchong city, a typical low mountainous and hilly city with a subtropical humid monsoon climate, is located at middle reach of Jialing River in northeastern corner of Sichuan Basin. The fog and haze has worried Nanchong more and more year by year, with the urbanized advancement, especially with an increasing number of private cars and development of the new district of colleges, industry, government, etc. This paper focused on the primary pollutant of main urban area of Nanchong—PM2.5, to investigate its main related factors, simulate its sequential variation and analysis its seasonal variation. Materials and methods Based on the daily concentration data of the main air pollutant: PM2.5 (μg ∙ m−3), PM10(μg ∙ m−3), SO2(μg ∙ m−3), NO2 (μg ∙ m−3), O3 (μg ∙ m−3), and CO (mg ∙ m−3) from online monitoring and analysis platforms for air quality of China (https://www.aqistudy.cn) and the daily weather data ranged from January 1st 2014 to June 30th 2017 from Weather Underground’s forecasts system (https://www.wunderground.com), including air temperature Ta (℃), relative humidity RH (%), rainfall R (mm), sea-level atmospheric pressure p (hPa), wind speed Sw (km ∙ h−1); probability density distribution test and linear correlation was utilized to investigate the main related factors of PM2.5 in the main urban area, and the stepwise regression was employed to simulated the sequential variation of the local PM2.5. And then IAQI (PM2.5) was calculated and compared at seasonal scale. Results PM2.5 concentration of Nanchong was verified to be distributed in an approximately log-normal distribution. CO and NO2 showed significant correlations with PM2.5 (r = 0.76, 0.55, respectively, P<0.01) as main related factors at the main urban area of Nanchong; based on the stepwise regression, an optimum equation was established with data of the former 912 days (with goodness of fit R2adj as 0.68, P<0.01), and was verified to be very well with data of the other 365 days (with goodness of fit as 0.64, relative error as 15.48%). PM2.5 tended to be decrease in general, and the seasonal average IAQI (PM2.5) in descending order were winter (114), spring (79), autumn (70) and summer (59), while the seasonal maximum IAQI (PM2.5) in a similar order: winter (259), spring (170), autumn (158) and summer (125). These results indicated that the PM2.5 were obviously related with CO and NO2, mainly from the fossil fuel emissions, especially from automotive exhaust. It was implied that the main related factors of PM2.5 were CO and NO2 from vehicle exhaust at the main urban area of Nanchong. In terms of the fine particulate pollution alone, averagely, the seasonal avg-IAQI (PM2.5) in winter (114) was higher than that in spring (79), both of two indicated mild air pollution for most time during these seasons, and both of the avg-IAQI (PM2.5) in summer (59) and autumn (70) indicated the air quality grade was fine in these two seasons on account of the subtropical humid climate and the developing orientation of Nanchong as a famous historical and cultural tourism city without large industrial emissions, while the max-IAQI (PM2.5) in winter, spring, autumn and summer was elevated accordingly to 259, 170, 158, 125, indicating the winter with heavily polluted air quality sometime, spring with moderately air pollution and autumn and summer with mild air pollution, occasionally. Discussion Linear interpolation of the short missing data inevitably produced slightly errors; monitoring stations for the air quality, relatively concentrated along the rivers in the main urban area, were limited representative. Conclusions The main related factor of PM2.5 concentration in Nanchong main urban area were CO and NO2 concentration, which had great potential to simulate as well as regulate sequential variation of the local PM2.5. Recommendations and perspectives More monitoring station sites and equipment should be applied in future to offer a more complete simulation for haze events. Besides the AQI app, air purifiers and wearable products, more measures should be taken to reduce the hazard of PM2.5, such as emission control technology, new energy vehicles technology, urban greening technology, and reasonable policies on vehicle restrictions and green travel. |
| Key words: Nanchong City fine particulate matter probability density distribution correlation analysis stepwise regression seasonal variation |
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