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绍兴纺织印染工业园区大气CO2、CH4和CO排放量估算及主要调控机制

Emission Characteristics and Influencing Factors of CO2, CH4, and CO in the Shaoxing Textile Printing and Dyeing Industrial Park

  • 摘要: 纺织印染是浙江省绍兴市特色和支柱产业,已形成集群效应,占全国总产能的40%以上,成为该行业践行“双碳”战略的重要挑战。为揭示纺织印染工业园区碳排放特征和主要影响因素,本研究于2024年10月29~30日对绍兴市柯桥区滨海工业园和越城区孙端工业园大气CO2、CH4和CO混合比及气象要素开展车载走航连续高精度观测。结果显示,工业园区内大气CO2、CH4和CO混合比均比同时期乡村自然背景区域高20%以上,其昼夜差异主要受区域风场和大气边界层高度影响。同时,燃油机动车排放也是工业园区内大气CO2和CO的重要排放源之一。基于质量守恒模型,初步估算两个工业园区的CO2日均总排放量约为4.67 t,CO日均总排放量约为28.67 kg。园区内污水处理厂和加油站等热点源的CH4排放速率约为9.46 t a−1和7.25 t a−1。本研究为进一步开展工业园区碳排放立体监测与精准核算奠定了技术和数据基础。

     

    Abstract: The textile printing and dyeing industry is a major economic pillar of Shaoxing City, Zhejiang Province, accounting for more than 40% of China’s total textile printing and dyeing production capacity. Consequently, reducing carbon emissions from this sector represents a critical challenge for implementing China’s “Dual Carbon” strategy. To characterize carbon emissions and identify the factors influencing them in textile printing and dyeing industrial parks, continuous mobile observations of atmospheric CO2, CH4, and CO mixing ratios, together with meteorological parameters, were conducted in Binhai Industrial Park (Keqiao District) and Sunduan Industrial Park (Yuecheng District) in Shaoxing City on October 29 and 30, 2024. The results showed that the atmospheric CO2, CH4, and CO mixing ratios in the industrial parks were approximately 20% higher than those in rural areas. The diurnal variations in atmospheric CO2, CH4, and CO mixing ratios were mainly influenced by regional wind patterns and the planetary boundary layer height. In addition, vehicle emissions were considered an important anthropogenic source of CO and CO2 in the industrial parks. Based on the mass balance model, the total daily CO2 emissions from the two industrial parks were estimated to be approximately 4.67 t a−1, while the CO emissions were approximately 28.67 kg d−1 CH4 emissions from key hotspot sources, including wastewater treatment plants and gas stations, were estimated to be 9.46 t a−1 and 7.25 t a−1, respectively. This study provides a basis for three-dimensional integrated observations and more accurate estimation of carbon emissions from industrial parks in the future.

     

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