基于电力大数据的钢铁企业大气污染物排放核算模型构建及应用

Construction and Application of Air Pollutant Emission Accounting Model for Iron and Steel Enterprises Based on Power Big Data

近年来,各级生态环境部门与国家电网公司积极签署战略合作协议,促进电力大数据在生态环境管理信息化平台的应用.本研究通过梳理电力大数据在大气污染防治中的应用,以唐山市钢铁行业为例构建基于电力大数据的大气污染物高时间精度排放核算模型,进一步挖掘电力大数据在大气污染排放控制中的应用潜力.结果表明:①模型核算的2019年唐山市17家钢铁企业的大气污染物排放量与2019年唐山市大气污染物排放清单(简称“城市清单”)结果一致性较好,SO_(2)、NOx和PM_(2.5)排放量分别为1017.90、2047.75、1141.81t,误差介于-0.46%~4.27%之间.②基于工序而言,以PM_(2.5)为例,模型预测结果与城市清单结果的相对误差在-17.34%~10.60%之间.③唐山某钢铁企业2022年SO_(2)、NOx、PM_(2.5)月排放量受钢铁市场价格影响较大,1月和6月分别为最高和最低污染物排放月,而其日排放受行业特征影响较为平稳,小时排放可能受电价波动影响较大.研究显示,基于电力大数据的大气污染物核算模型阐明了电力大数据和污染排放的动态响应关系,一定程度上提升了排放核算的时间精细度,实证了基于电力大数据核算大气污染物排放的研究意义和可行性.

Over the past few years,the national/provincial/municipal Ministry of Ecology and Environment has actively signed strategic cooperation agreements with State Grid Corporation of China to promote the application of electric power big data in environmental management.In this study,based on the analysis of the application of electric power big data in air pollution prevention and control,taking the steel enterprises in Tangshan as an example,a high-temporal-resolution emission estimation model of air pollutants was established.The results showed that:(1)The estimated total air pollutant emissions in 2019 are well consistence with the traditional emission inventory in Tangshan,and the emissions of SO_(2),NOx and PM_(2.5) are 1017.90,2047.75,1141.81 t,respectively,with the relative errors ranging between-0.46%and 4.27%.(2)Based on the process,taking PM_(2.5) as an example,the relative error between the model prediction result and the Tangshan emission inventory is-17.34%-10.60%.(3)The characteristics of monthly fluctuating emissions(affected by the steel price),uniform daily emissions,and hourly differentiated emissions(affected by the electricity price)are revealed by estimating air pollutant emissions in 2022 from a typical steel enterprise based on the electric power big data in corresponding year,with the highest emission in January and the lowest emission in June.The results show that the accounting model based on electric power big data builds the dynamic response relationship between electric power big data and air pollutant emissions,which can partly improve the temporal resolution of emission accounting,reflecting the research significance and feasibility of accounting air pollutant emission based on electric power big data.