引用本文:蔡哲,钟声,周德荣,张伟,王诗琦,陈佳豪,覃栎,江飞.南京温室气体监测点位规划方法研究[J].环境监控与预警,2023,15(5):100-105
CAI Zhe,ZHONG Sheng,ZHOU Derong,ZHANG Wei,WANG Shiqi,CHEN Jiahao,QIN Li,JIANG Fei.Research on the Setting Method of Greenhouse Gas Monitoring Sites in Nanjing[J].Environmental Monitoring and Forewarning,2023,15(5):100-105
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南京温室气体监测点位规划方法研究
蔡哲1,钟声2*,周德荣3,张伟4,王诗琦1,陈佳豪1,覃栎5,江飞6
1.南京创蓝科技有限公司,江苏 南京 211135;2.江苏省环境监测中心,江苏 南京 210019;3.南京大学,大气科学学院,江苏 南京 210046;4.江苏省南京环境监测中心,江苏 南京 210013;5.北京大学,环境科学与工程学院,北京 100871;6.南京大学,国际地球系统科学研究所,江苏 南京 210046
摘要:
以南京市为温室气体监测区域,以2021年1月和7月作为冬、夏季的代表,利用拉格朗日粒子扩散模型(LPDM)和K均值(K-Means)聚类算法对南京市已有的高塔点位进行筛选,研究温室气体监测点位布设最优位置。通过计算不同点位的城市CO2贡献度,最终确定了冬季和夏季各8个温室气体监测建议点位。结果表明,通过聚类算法筛选出的监测站点选址能对南京市大部分城区的温室气体排放具有相对最大的覆盖范围和较高的敏感性,能够有效反映出南京市CO2排放量的梯度变化,对南京市不同时空尺度上的温室气体排放达到相对最好的监测效果。
关键词:  温室气体  监测点位  碳中和  拉格朗日粒子扩散模型  K均值聚类算法  南京
DOI:103969/jissn1674-6732202305015
分类号:X831
基金项目:江苏省碳达峰碳中和科技创新专项(BE2022612);国家重点研发计划项目(2020YFA0607504);江苏省环境监测科研基金项目(2213)
Research on the Setting Method of Greenhouse Gas Monitoring Sites in Nanjing
CAI Zhe1, ZHONG Sheng2*, ZHOU Derong3, ZHANG Wei4, WANG Shiqi1, CHEN Jiahao1, QIN Li5, JIANG Fei6
1.Nanjing Climblue Technology Co.,Ltd., Nanjing, Jiangsu 211135, China;2.Jiangsu Environmental Monitoring Center, Nanjing, Jiangsu 210019,China; 3.School of Atmospheric Sciences, Nanjing University, Nanjing, Jiangsu 210046, China;4.Jiangsu Nanjing Environmental Monitoring Center, Nanjing, Jiangsu 210013, China;5.College of Environmental Sciences and Engineering,Peking University, Beijing 100871, China; 6.International Institute for Earth System Science, Nanjing University,Nanjing, Jiangsu 210046, China
Abstract:
In this paper,Nanjing was taken as the research object of greenhouse gas(GHG) monitoring work, we selected January and July 2021 as representatives of winter and summer and chose the existing high tower sites as research objectives,by using Lagrangian Particle Dispersion Model and K-means clustering algorithm,to evaluate the optimal GHG monitoring sites and then establish the GHG monitoring network in Nanjing. We calculated the contributions to the urban's CO2 concentrations of the different tower sites, and 8 recommended GHG monitoring sites were finalized in winter and in summer, respectively. The results show that the monitoring site selection screened by the clustering algorithm are high sensitivity to the greenhouse gas emissions and can cover most of the Nanjing city, which can effectively reflect the gradient change of the city's CO2 emissions, and achieve the best monitoring effect on Nanjing's greenhouse gas emissions at different temporal and spatial scales.
Key words:  Greenhouse gas  Monitoring site  Carbon neutral  Lagrange Particle Diffusion Model(LPDM)  K-means clustering algorithm  Nanjing