基于GAM模型的四川盆地臭氧时空分布特征及影响因素研究
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摘要
为研究四川盆地臭氧(O_3)时空分布特征及其气象成因,对四川盆地18个城市2015—2016年国控环境监测站点和气象台站数据进行了研究分析.结果表明:2015—2016年四川盆地O_3污染愈发严重,高值污染区呈扩张态势,污染区主要位于盆地西部成都、德阳、资阳、眉山、内江一带和以广安为中心的周边区域.O_3浓度有明显的季节变化特征:夏季(110.70±41.52)μg·m~(-3)>春季(95.24±41.23)μg·m~(-3)>秋季(67.58±39.55)μg·m~(-3)>冬季(47.17±41.15)μg·m~(-3).基于广义相加模型(GAM)分析发现O_3浓度与气压、气温、相对湿度、风速、日照时数、降水量间均呈非线性关系,其中日照时数、相对湿度以及气温对四川盆地O_3浓度影响较大,而风速、气压以及降水量对O_3浓度影响相对较小.通过构建GAM模型对四川盆地18个城市O_3污染的主导气象因子进行识别,并对2017年O_3浓度进行预测和检验,结果显示GAM模型能较为准确地预测四川盆地各城市O_3浓度的变化趋势.
In order to study the spatial and temporal distribution characteristics of O_3 and its meteorological cause over Sichuan Basin, the data of national control environment monitoring stations and meteorological stations located in 18 cities in Sichuan Basin from 2015 to 2016 were analyzed. The results showed that the O_3 pollution was becoming serious and the high-value area expanded significantly in Sichuan Basin from 2015 to 2016. The high-value area was mainly located in the western of the basin, including Chengdu, Deyang, Ziyang, Meishan, Neijiang and Guang′an. The concentration of O_3 had obvious seasonal variation characteristics: summer(110.70±41.52) μg·m~(-3)> spring(95.24±41.23) μg·m~(-3) > autumn(67.58±39.55) μg·m~(-3)> winter(47.17±41.15) μg·m~(-3). Based on the Generalized Additive Model(GAM), it was found that O_3 has a nonlinear relationship with air pressure, temperature, relative humidity, wind speed, sunshine duration and precipitation. The sunshine duration, relative humidity and temperature had a great influence on O_3 concentration in the Sichuan Basin. While, the effects of wind speed, pressure and precipitation on O_3 concentration are less significant. The GAM model was further used to identify the dominant meteorological factors in 18 cities in Sichuan Basin and to forecast the O_3 concentration in 2017. The results showed that GAM model could predict the trend of O_3 concentration in each city very well.
In order to study the spatial and temporal distribution characteristics of O_3 and its meteorological cause over Sichuan Basin, the data of national control environment monitoring stations and meteorological stations located in 18 cities in Sichuan Basin from 2015 to 2016 were analyzed. The results showed that the O_3 pollution was becoming serious and the high-value area expanded significantly in Sichuan Basin from 2015 to 2016. The high-value area was mainly located in the western of the basin, including Chengdu, Deyang, Ziyang, Meishan, Neijiang and Guang′an. The concentration of O_3 had obvious seasonal variation characteristics: summer(110.70±41.52) μg·m~(-3)> spring(95.24±41.23) μg·m~(-3) > autumn(67.58±39.55) μg·m~(-3)> winter(47.17±41.15) μg·m~(-3). Based on the Generalized Additive Model(GAM), it was found that O_3 has a nonlinear relationship with air pressure, temperature, relative humidity, wind speed, sunshine duration and precipitation. The sunshine duration, relative humidity and temperature had a great influence on O_3 concentration in the Sichuan Basin. While, the effects of wind speed, pressure and precipitation on O_3 concentration are less significant. The GAM model was further used to identify the dominant meteorological factors in 18 cities in Sichuan Basin and to forecast the O_3 concentration in 2017. The results showed that GAM model could predict the trend of O_3 concentration in each city very well.
引文
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吴锴, 康平, 王占山,等. 2017. 成都市臭氧污染特征及气象成因研究[J]. 环境科学学报, 37(11):4241-4252
易睿, 王亚林, 张殷俊,等. 2015. 长江三角洲地区城市臭氧污染特征与影响因素分析[J]. 环境科学学报, 35(8):2370-2377
吴瑞霞,浦一芬,张美根,等. 2005.北京市夏季臭氧变化特征的观测研究[J].南京气象学院学报, 28(5):690-694
Wolf K, Cyrys J, Harciníková T, et al. 2017.Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany[J]. Science of the Total Environment, 579: 1531-1540
Liao T, Wang S, Ai J, et al. 2017. Heavy pollution episodes, transport pathways and potential sources of PM2. 5 during the winter of 2013 in Chengdu (China)[J]. Science of the Total Environment, 584: 1056-1065
Mohd T L,Doreena D. et al. 2016. Corrigendum to Influence of atmospheric ozone, PM10 and meteorological factors on the concentration of airborne pollen and fungal spore [J]. Science of the Total Environment,573:494-504
Monks P S,Archibald A T,Colette A,et al. 2015. Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer [J]. Atmospheric Chemistry and Physics,15(15):8889-8973
肖钟湧, 江洪. 2011. 四川盆地大气NO2特征研究[J]. 中国环境科学, 31(11):1782-1788
张灿, 蒋昌潭, 罗财红,等. 2017. 气象因子对臭氧的影响及其在空气质量预报中的应用[J]. 中国环境监测, 33(4):221-228
张娟, 刘志红, 黄观,等. 2017. 川南城市群大气污染时空分布及气象因子分析[J]. 四川环境, 36(2):68-75
郑永光,陈炯,朱佩君. 2008.中国及周边地区夏季中尺度对流系统分布及其日变化特征[J].科学通报, 53(4):471-481
Belu■i■ A,Herceg-Buli c'I,Klaic Z B. 2015. Using a generalized additive model to quantify the influence of local meteorology on air quality in Zagreb[J]. Geofizika,32(1):47-77Camalier L, Cox W, Dolwick P. 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends[J]. Atmospheric Environment, 41(33): 7127-7137
Camalier L, Cox W, Dolwick P. 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends[J]. Atmospheric Environment, 41(33): 7127-7137
曹庭伟, 吴锴, 康平,等. 2018. 成渝城市群臭氧污染特征及影响因素分析[J]. 环境科学学报, 38(4):1275-1284
陈朝平, 杨康权, 冯良敏,等. 2015. 四川盆地一次持续性雾霾天气过程分析[J]. 高原山地气象研究, 35(3):73-77
陈仁杰, 陈秉衡, 阚海东. 2010. 上海市近地面臭氧污染的健康影响评价[J]. 中国环境科学, 30(5):603-608
谌均. 2016. 广安市大气污染现状及防治措施[J]. 绿色科技, (6):53-55
程麟钧, 王帅, 宫正宇,等. 2017. 中国臭氧浓度的时空变化特征及分区[J]. 中国环境科学, 37(11):4003-4012Gong X, Hong S, Jaffe D A. 2018.Ozone in China: Spatial distribution and leading meteorological factors controlling O3 in 16 Chinese cities[J]. Aerosol & Air Quality Research, 18: 2287-2300
Hastie T, Tibshirani R. 1990. Generalized Additive Models [M]. Chapman and Hall
何启超. 2014.四川盆地大气扩散输送与低风速特征[D].北京: 北京大学
贺祥, 林振山. 2017. 基于GAM模型分析影响因素交互作用对PM2.5浓度变化的影响[J]. 环境科学, 38(1):22-32
洪盛茂, 焦荔, 何曦,等. 2009. 杭州市区大气臭氧浓度变化及气象要素影响[J]. 应用气象学报, 20(5):602-611
黄卓尔, 徐丽莉. 2006. 环境监测数据线性回归分析中回归模型的比较[J]. 中国环境监测, 22(3):57-59
贾俊平, 何晓群, 金勇进. 2007.统计学.第3版[M]. 北京:中国人民大学出版社
李浩,李莉,黄成,等. 2015. 2013 年夏季典型光化学污染过程中长三角典型城市O3来源识别[J]. 环境科学,36(1): 1-10
李霄阳, 李思杰, 刘鹏飞,等. 2018. 2016年中国城市臭氧浓度的时空变化规律[J]. 环境科学学报, 38(4):1263-1274
沈路路, 王聿绚, 段雷. 2011. 神经网络模型在O3浓度预测中的应用[J]. 环境科学, 32(8):2231-2235
王玉云,刘子芳, 赵兵,等. 2014. 雅安市环境空气质量时空动态变化及改善途径分析[J]. 环境保护科学, 40(3):39-42
吴锴, 康平, 王占山,等. 2017. 成都市臭氧污染特征及气象成因研究[J]. 环境科学学报, 37(11):4241-4252
易睿, 王亚林, 张殷俊,等. 2015. 长江三角洲地区城市臭氧污染特征与影响因素分析[J]. 环境科学学报, 35(8):2370-2377
吴瑞霞,浦一芬,张美根,等. 2005.北京市夏季臭氧变化特征的观测研究[J].南京气象学院学报, 28(5):690-694
Wolf K, Cyrys J, Harciníková T, et al. 2017.Land use regression modeling of ultrafine particles, ozone, nitrogen oxides and markers of particulate matter pollution in Augsburg, Germany[J]. Science of the Total Environment, 579: 1531-1540
Liao T, Wang S, Ai J, et al. 2017. Heavy pollution episodes, transport pathways and potential sources of PM2. 5 during the winter of 2013 in Chengdu (China)[J]. Science of the Total Environment, 584: 1056-1065
Mohd T L,Doreena D. et al. 2016. Corrigendum to Influence of atmospheric ozone, PM10 and meteorological factors on the concentration of airborne pollen and fungal spore [J]. Science of the Total Environment,573:494-504
Monks P S,Archibald A T,Colette A,et al. 2015. Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer [J]. Atmospheric Chemistry and Physics,15(15):8889-8973
肖钟湧, 江洪. 2011. 四川盆地大气NO2特征研究[J]. 中国环境科学, 31(11):1782-1788
张灿, 蒋昌潭, 罗财红,等. 2017. 气象因子对臭氧的影响及其在空气质量预报中的应用[J]. 中国环境监测, 33(4):221-228
张娟, 刘志红, 黄观,等. 2017. 川南城市群大气污染时空分布及气象因子分析[J]. 四川环境, 36(2):68-75
郑永光,陈炯,朱佩君. 2008.中国及周边地区夏季中尺度对流系统分布及其日变化特征[J].科学通报, 53(4):471-481