摘要
利用潍坊市安装用电监控的工业企业在冬奥会前和会中的生产用电数据及部分企业大气污染物排放在线监测数据,分析企业生产用电量和排放量的变化特征及潜在的相关性,评估管控措施落实状况及减排效果。结果表明:管控期间,潍坊市工业企业用电量相比管控前平均下降57.96%;完全停产类企业用电量下降75%以上,大气污染物(SO_(2)、NO_(x)、烟粉尘)排放量下降100%;限产类企业用电量降幅呈现随管控措施加严而增大的趋势;延迟复产类企业延迟复产率为47.83%~96.00%,用电量下降50.76%。根据管控期间各行业企业的用电量变化状况,判定各企业基本落实了减排要求。可见,企业用电数据在一定情况下,可以替代或辅助工业企业排放数据,反映企业管控措施落实状况及减排效果。
In this study,the change characteristics and potential correlation of electricity consumption and emissions of enterprises were analyzed,and the implementation status of control measures and emission reduction effects were evaluated by using the electricity consumption data of industrial enterprises installing electricity monitoring facilities in Weifang before and during the Beijing Winter Olympics and the online monitoring data of atmospheric pollutant emissions of some enterprises.The results indicated that during the control period,the electricity consumption of industrial enterprises in Weifang decreased by 57.96%on average,compared with that before the control period.The electricity consumption of completely shutdown enterprises decreased by more than 75%,and the emissions of atmospheric pollutants(SO_(2),NO_(x),particulate matter)decreased by 100%.The decrease in electricity consumption of limited production enterprises increased with the strengthening of control measures.The delayed resumption rate of enterprises was from 47.83%to 96.00%,and the electricity consumption decreased by 50.76%.Based on the changes in electricity consumption of enterprises in various industries during the control period,it was determined that each enterprise basically met the emission reduction requirements.This indicated that electricity consumption data could replace or assist industrial enterprise emission data under certain circumstances,reflecting the implementation status of control measures and emission reduction effect.
作者
李政
陈建华
刘翰青
高健
杨艳
LI Zheng;CHEN Jianhua;LIU Hanqing;GAO Jian;YANG Yan(Atmospheric Environment Institute,Chinese Research Academy of Environmental Sciences,Beijing 100020,China)
出处
《环境监测管理与技术》
CSCD
北大核心
2024年第1期37-42,48,共7页
The Administration and Technique of Environmental Monitoring
基金
国家重点研发计划“大气与土壤、地下水综合治理重点专项——2022年度PM_(2.5)和O_(3)污染系统防控的工程化模式系统与支撑平台建设”基金资助项目(2022YFC3703000)。
关键词
工业污染源
用电量数据
管控效果评估
K-MEANS聚类算法
北京冬奥会期间
潍坊
Industrial pollution sources
Electricity consumption data
Evaluation of control effect
K-means clustering algorithm
The Beijing Winter Olympics period
Weifang