山东省AQI、颗粒物和臭氧时空演化特征及关键影响因素识别分析

刘杰; 王明飞; 邓建明; 陈翔宇; 汤祥明; 曾蕾

doi:10.13198/j.issn.1001-6929.2022.11.19

山东省AQI、颗粒物和臭氧时空演化特征及关键影响因素识别分析

doi: 10.13198/j.issn.1001-6929.2022.11.19

刘杰^{1, 2,},
王明飞³,
邓建明^{1, 2, ,},
陈翔宇⁴,
汤祥明^{1, 2},
曾蕾⁵

1.
中国科学院南京地理与湖泊研究所，湖泊与环境国家重点实验室，江苏南京　210008
2.
中国科学院大学，北京　100049
3.
中国海洋大学，海洋环境与生态教育部重点实验室，山东青岛　266100
4.
福建农林大学林学院，福建福州　350002
5.
中国科学院大连化学物理研究所分离分析化学重点实验室，辽宁大连　116023

基金项目: 国家自然科学基金项目(No.41971146)；中国科学院南京地理与湖泊研究所青年科学家小组项目(No.E1SL002)

详细信息

作者简介:
刘杰（1998-），男，山东泰安人，liujie@niglas.ac.cn

通讯作者:
邓建明(1985-)，男，江西南昌人，副研究员，博士，主要从事气候变化与空气污染研究，jmdeng@niglas.ac.cn

中图分类号: X51
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- 被引次数: 0
出版历程
- 收稿日期: 2022-08-29
- 修回日期: 2022-11-14

Temporal and Spatial Evolution Patterns of AQI, Particulate Matter, and Ozone in Shandong Province and Key Influencing Factors

LIU Jie^{1, 2
,},
WANG Mingfei³,
DENG Jianming^{1, 2
, ,},
CHEN Xiangyu⁴,
TANG Xiangming^{1, 2},
ZENG Lei⁵

1.
State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China
4.
Forestry College, Fujian Agriculture and Forestry University, Fuzhou 350002, China
5.
CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China

Funds: National Natural Science Foundation of China (No.41971146); Young Scientists Group Project of Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (No.E1SL002)

摘要

摘要: 山东省空气质量存在明显的时空差异，并受气象和社会经济因子等的综合影响. 为了解山东省空气质量指数(AQI)以及颗粒物和臭氧(O₃)浓度等时空演化特征，探究颗粒物与O₃浓度之间的协同关系，基于山东省16个地级市2013年12月—2021年12月的AQI和空气污染物(SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀)浓度数据与同期的气象数据、工业及生活污染物(氨氮、SO₂、氮氧化物和烟尘、粉尘等)排放量和社会经济数据，运用R语言和ArcGIS对各地区AQI与PM_2.5和O₃浓度等的时间和空间变化特征及关键影响因素识别分析. 结果表明：①山东省AQI和空气污染物浓度具有明显的月际、季节和年际变化特征以及地区性差异. 鲁东地区各季节空气质量明显优于鲁西地区，呈现由东向西空气污染愈加严重的趋势. PM_2.5浓度呈鲁西地区最高，鲁中、鲁南和鲁北地区次之，鲁东地区最低的分布特征；鲁东地区与其他地区夏季O₃浓度差异较大，而其他时期各地区间O₃浓度差异较小. ②日照时长、风速、逆温强度、人均GDP、第二产业占比、城镇化率以及氨氮和SO₂排放量对AQI和PM_2.5浓度年际变化均贡献较大，而气温、相对湿度、万元GDP能耗以及氮氧化物和SO₂排放量对O₃浓度年际变化贡献均较大. ③气象因子中日照时长和风速对空气污染物浓度整体变化的解释率分别为35.01%和23.35%，工业及生活污染物排放因子中氮氧化物和SO₂排放量对空气污染物浓度整体变化的解释率分别为25.07%和49.01%，社会经济因子中第二产业占比和万元GDP能耗对空气污染物浓度整体变化的解释率分别为33.54%和25.90%. 研究显示，近年来山东省PM_2.5浓度等大幅下降，但O₃污染仍较严重，目前空气污染防治工作需进入“季节—全年”“城市—区域”等协同新局面.
- 山东省 /
- AQI /
- 颗粒物 /
- O₃ /
- 时空演化
Abstract: Air quality in Shandong Province had significant temporal and spatial differences, which are influenced by a combination of meteorological and socio-economic factors. The temporal and spatial evolution patterns of the air quality index (AQI), particulate matter concentrations, and ozone (O₃) concentrations, as well as the synergistic interaction between particulate matter and O₃ concentrations in Shandong Province were investigated in the present study. We carried out related research based on the AQI and air pollutant concentration (SO₂, NO₂, CO, O₃, PM_2.5 and PM₁₀) data of 16 prefecture-level cities in Shandong Province from December 2013 to December 2021. The meteorological, industrial and domestic pollutant (ammonia nitrogen, SO₂, nitrogen oxide, soot and dust, etc.) emissions, socio-economic data, the temporal and spatial variation patterns and key influencing factors of AQI, PM_2.5 concentrations, and O₃ concentrations were analyzed using R programming language and ArcGIS. The results showed that: (1) AQI and air pollutant concentrations in Shandong Province showed monthly, seasonal, and inter-annual variation patterns and regional differences. The air quality in east Shandong was better than that in west Shandong, indicating a more serious air pollution trend from east to west. PM_2.5 concentration was highest in the western part of Shandong Province, followed by the central, southern and northern parts of Shandong Province, and the lowest PM_2.5 concentration was in the eastern part of Shandong Province. The difference in O₃ concentration between the eastern part of Shandong Province and other regions was large in summer, while the difference among all regions was small in other periods. (2) Sunshine duration, wind speed, inversion intensity, per capita GDP, the proportion of secondary industry, urbanization rate, ammonia nitrogen emissions, and SO₂ emissions were significant contributors to the inter-annual variation of AQI and PM_2.5 concentrations. While air temperature, relative humidity, energy consumption per 10,000-yuan GDP, nitrogen oxide emissions, and SO₂ emissions were significant contributors to the inter-annual variation of O₃ concentration. (3) As for meteorological factors, sunshine duration and wind speed accounted for 35.01% and 23.35% of the overall changes in air pollutant concentrations, respectively. Concerning industrial and domestic pollutant emission factors, nitrogen oxide and SO₂ emissions accounted for 25.07% and 49.01% of the overall change in air pollutant concentrations, respectively. While for socio-economic factors, the proportion of secondary industry and energy consumption per 10,000-yuan GDP accounted for 33.54% and 25.90% of the overall change in air pollutant concentrations, respectively. This study suggested that the PM_2.5 concentration decreased significantly in Shandong Province in recent years, but O₃ pollution was still serious. The current air pollution prevention work needs to enter a ‘season-year’, ‘city-region’, and other synergistic new situations.
- Shandong Province /
- AQI /
- particulate matter /
- O₃ /
- temporal and spatial evolution

HTML全文

图 1 山东省AQI时空分布情况

Figure 1. Spatial and temporal distribution of AQI in Shandong Province

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图 2 山东省各地区AQI长期变化趋势

Figure 2. Long-term trends of AQI in different regions of Shandong Province

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图 3 山东省各地区空气污染物浓度月变化趋势

Figure 3. The monthly trends of air pollutant concentrations in different regions of Shandong Province

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图 4 2014—2021年山东省各地区空气污染物浓度年均值长期变化趋势

注：实线为GAM模型估计的长期趋势，阴影区域为估计的标准误差，*表示P<0.05，**表示P<0.01. 下同.

Figure 4. Long-term trends in the annual average concentrations of air pollutants in different regions of Shandong Province from 2014 to 2021

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图 5 2014—2021年山东省各地区PM_2.5和O₃浓度季均值长期变化趋势

Figure 5. Long-term trends in the seasonal average concentrations of PM_2.5 and O₃ in different regions of Shandong Province from 2014 to 2021

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图 6 各影响因子与AQI和空气污染物浓度之间的相关性分析

注：*表示P<0.05，**表示P<0.01.

Figure 6. Analysis of the correlation between various influencing factors with AQI and air pollutant concentrations

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图 7 各影响因子对AQI与空气污染物浓度的贡献率

Figure 7. Contribution percentages of various influencing factors to AQI and air pollutant concentrations

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图 8 气象因子、工业及生活污染物排放和社会经济因子对空气污染物浓度整体变化的解释率

Figure 8. Explanation percentages of the overall changes in air pollutant concentrations by meteorological, industrial and domestic pollutant emissions, and socio-economic factors, respectively

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