| 江苏省二氧化碳减排政策对空气质量改善增益 |
| 摘要点击 2359 全文点击 796 投稿时间：2022-10-19 修订日期：2022-12-18 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 温室气体 排放清单 协同减排 数值模拟 空气质量改善 |
| 英文关键词 greenhouse gases emissions inventory co-benefits numerical modeling air quality improvement |
| 作者 | 单位 | E-mail | | 王松伟 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | wangsw623@163.com | | 汤克勤 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | | | 张皓然 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | | | 刘弯弯 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | | | 白露 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | | | 李楠 | 南京信息工程大学环境科学与工程学院, 江苏省大气环境监测与污染控制高技术研究重点实验室, 江苏省大气环境与装备技术协同创新中心, 南京 210044 | linan@nuist.edu.cn |
|
| 中文摘要 |
| 碳排放达峰和空气质量达标是当前大气环境研究的热点问题.利用排放因子法建立2010~2019年江苏省城市级温室气体排放清单，进一步结合温室气体-大气污染物协同关系分析和WRF-Chem空气质量模型模拟，量化不同碳减排情景下空气质量改善的协同增益.结果表明，2010~2019年江苏省CO2年均排放量为701.74~897.47 Mt，其中苏州、徐州和南京排放量最高（91.19~182.12 Mt ·a-1），扬州、宿迁和连云港排放量最低（13.19~32.54 Mt ·a-1），大部分城市CO2排放呈持续上升趋势.能源活动为CO2排放的主要来源，贡献率占比近90%，工业生产过程贡献率约10%.依据减排侧重点的不同设计3类CO2减排情景，分别为全部门协同减排、优先能源减排和优先工业减排，每类减排情景包含不同的CO2减排力度（10%、20%和40%）.情景模拟结果指出，以2017为基准年，全部门协同、优先能源和优先工业减排中PM2.5年均浓度下降幅度分别为6.7~21.1、3.1~12.0和3.4~14.3 μg ·m-3.全部门协同减排对PM2.5污染改善最为显著，在全部门减排情景为40%下，除徐州和宿迁外其它城市PM2.5年均浓度值均能达到国家Ⅱ级标准（35 μg ·m-3）.PM10、SO2、NO2和CO的变化响应与PM2.5类似，但O3污染在优先能源和优先工业情景下呈现出不同程度的上升趋势. |
| 英文摘要 |
| Carbon emission peaking and air quality improvement is an urgent issue in the research of the atmospheric environment. Here, the emission factor method was used to compile the city-level greenhouse gas emission inventory of Jiangsu Province from 2010 to 2019, which was then combined with greenhouse gas-air pollutant synergy analysis and WRF-Chem air quality model simulation to analyze the synergistic gain of air quality improvement under different carbon emission reduction scenarios. The results revealed that the annual mean CO2 emission in Jiangsu Province from 2010 to 2019 was 701.74-897.47 Mt. Suzhou, Xuzhou, and Nanjing had the highest emissions (91.19-182.12 Mt·a-1); Yangzhou, Suqian, and Lianyungang had the lowest emissions (13.19-32.54 Mt·a-1); and majority of the cities had a continuous upward trend in the CO2 emissions. Energy activities were the main source of CO2 emissions, accounting for nearly 90%, whereas industrial production processes contributed to the remaining 10%. This study designed three types of CO2 emission reduction conditions according to different emission reduction priorities, namely, sector-wide collaborative, energy priority, and industrial priority. Each type of emission reduction condition included a different intensity of CO2 emission reduction (10%, 20%, and 40%). The condition-based simulation results demonstrated that, taking 2017 as the base year, the average annual decrease in PM2.5 concentration in sector-wide collaborative, energy priority, and industrial priority emission reduction was 6.7-21.1, 3.1-12.0, and 3.4-14.3 μg·m-3, respectively. Sector-wide collaborative emission reduction had the most notable improvement in PM2.5 pollution. Under the condition of the sector-wide collaborative emission reduction of 40%, the average annual PM2.5 concentration of all cities, excluding Xuzhou and Suqian, met the national Ⅱ standard (35 μg·m-3). The change responses of PM10, SO2, NO2, and CO were similar to that of PM2.5, but O3 pollution increased under the conditions of energy and industrial priorities. |
|
|
|
