| 天津市“十三五”期间PM2.5减排效果评估 |
| 摘要点击 1774 全文点击 1435 投稿时间：2022-08-18 修订日期：2022-09-05 |
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| 中文关键词 天津 "十三五"期间 污染源排放 PM2.5 减排效果评估 |
| 英文关键词 Tianjin 13th Five-Year Period pollution source emission PM2.5 emission reduction effect assessment |
| 作者 | 单位 | E-mail | | 肖致美 | 天津市生态环境监测中心, 天津 300191 | xiaozhimei01@163.com | | 徐虹 | 天津市生态环境监测中心, 天津 300191 | | | 蔡子颖 | 天津市环境气象中心, 天津 300074 | 120078030@163.com | | 张裕芬 | 南开大学环境科学与工程学院, 国家环境保护城市空气颗粒物污染防治重点实验室, 天津 300071 | | | 刘茂辉 | 天津市生态环境监测中心, 天津 300191 | | | 孙猛 | 天津市生态环境监测中心, 天津 300191 | | | 李鹏 | 天津市生态环境监测中心, 天津 300191 | | | 杨宁 | 天津市生态环境监测中心, 天津 300191 | | | 戢运峰 | 天津市生态环境监测中心, 天津 300191 | yf_2100@163.com |
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| 中文摘要 |
| 为了解"十三五"期间天津市PM2.5减排效果,基于2015~2020年不同大气污染治理措施的减排量核算结果,利用空气质量模型和高时空分辨率PM2.5监测数据,对"十三五"期间天津市PM2.减排效果进行分析.结果表明,2015~2020年,天津市SO2、NOx、VOCs和PM2.5的排放量分别减少4.77×104、6.20×104、5.37×104和3.53×104t,其中工艺过程、散煤和电力治理对SO2的减排贡献大,工艺过程、电力和钢铁治理对NOx的减排贡献大,工艺过程对VOCs的减排贡献最大,工艺过程、散煤和钢铁治理对PM2.5的减排贡献大."十三五"期间天津市PM2.5浓度平均值、污染天数和重污染天数明显下降,分别较2015年下降31.4%、51.2%和60.0%;与前期(2015~2017年)相比,后期(2018~2020年)天津市PM2.5浓度平均值和污染天数下降幅度减缓,重污染天数基本保持在10 d左右.数值模拟结果表明,2015~2020天津市PM2.5浓度下降贡献中,气象因素占1/3,减排措施影响占2/3.其中工艺过程减排使ρ(PM2.5)降低2.66 μg·m-3,对PM2.5浓度改善贡献率为18.3%;散煤清零措施使ρ(PM2.5)降低2.18 μg·m-3,对PM2.5浓度改善贡献率为15.0%;钢铁行业减排使ρ(PM2.5)降低1.70 μg·m-3,对PM2.5浓度改善贡献率为11.7%;电力行业减排使ρ(PM2.5)降低0.51 μg·m-3,对PM2.5浓度改善贡献率为3.5%.为促进"十四五"期间PM2.5浓度的持续改善,在煤炭消费总量控制和"双碳"目标的约束下,天津应继续开展煤炭结构的优化调整,推进煤炭消费进一步向治污水平先进的电力行业集中;同时要进一步提升工业污染源全过程的排放绩效水平,以环境容量为约束,设计产业优化调整和转型升级的技术路线,优化配置环境容量资源,提出有限环境容量下的重点行业有序发展模式,引导企业清洁化提升改造、转型升级和绿色发展. |
| 英文摘要 |
| The emission reduction effect of major air pollution control measures on PM2.5 concentrations was assessed using air quality simulations based on the calculation data of emission reductions from different air pollution control measures and the high spatiotemporal resolution online monitoring data of PM2.5 during the 13th Five-Year Period in Tianjin. The results showed that the total emission reductions of SO2, NOx, VOCs, and PM2.5 from 2015 to 2020 were 4.77×104, 6.20×104, 5.37×104, and 3.53×104 t, respectively. SO2 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and thermal power. NOx emission reduction was mainly due to the prevention of process pollution, thermal power, and steel industry. VOCs emission reduction was mainly due to prevention of process pollution. PM2.5 emission reduction was mainly due to the prevention of process pollution, loose coal combustion, and the steel industry. The concentrations, pollution days, and heavy pollution days of PM2.5 decreased significantly from 2015 to 2020 by 31.4%, 51.2%, and 60.0% compared to those in 2015, respectively. The concentrations and pollution days of PM2.5 decreased slowly in the later stage (from 2018 to 2020)as compared with those in the early stage (from 2015 to 2017), and the days of heavy pollution remained for approximately 10 days. The results of air quality simulations showed that meteorological conditions contributed one-third to the reduction in PM2.5 concentrations, and the emission reductions of major air pollution control measures contributed two-thirds to the reduction in PM2.5 concentrations. For all air pollution control measures from 2015 to 2020, PM2.5 concentrations were reduced by the prevention of process pollution, loose coal combustion, the steel industry, and thermal power by 2.66, 2.18, 1.70, and 0.51 μg·m-3, respectively, accounting for 18.3%, 15.0%, 11.7%, and 3.5% of PM2.5 concentration reductions. In order to promote the continuous improvement in PM2.5 concentrations during the 14th Five-Year Plan period, under the total coal consumption control and the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," Tianjin should continue to optimize and adjust the coal structure and further promote the coal consumption to the power industry with an advanced pollution control level. At the same time, it is necessary to further improve the emission performance of industrial sources in the whole process, taking environmental capacity as the constraint; design the technical route for industrial optimization, adjustment, transformation, and upgrading; and optimize the allocation of environmental capacity resources. Additionally, the orderly development model for key industries with limited environmental capacity should be proposed, and clean upgrading, transformation, and green development should be guided for enterprises. |
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