State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
Sci Total Environ. 2021 Dec 1;798:149114. doi: 10.1016/j.scitotenv.2021.149114. Epub 2021 Jul 18.
The development of a refined fugitive dust emission inventory is vital for prevention and control of air pollution. In this study, a fugitive dust emission inventory of soil dust (SD), road dust (RD), and construction dust (CD) in Xiong'an New Area (XANA) for 2020 was developed by collecting activity data and combining remote sensing and field investigation data based on a popular compilation technology in China. The CALPUFF model was used to elucidate the contribution characteristics of dust sources to ambient particulate matter (PM), and the accuracy of the dust emission inventory compilation method was verified. The results show that the total emissions of PM and PM were 43,081.14 tons and 9701.69 tons, respectively. Meanwhile, RD and CD were the main emission sources, accounting for over 98.49% of the total emissions. The total contribution from the different types of dust sources to the ambient PM was 42.59 μg/m (29.38%), with the contribution of RD (32.63 μg/m, 22.51%) being approximately three times that of CD (9.78 μg/m, 6.74%). Roads were the main source of fugitive dust, but large-scale infrastructure construction was the main cause of the high emission and high contribution of RD. The results show that the emission inventory compilation method can be used to estimate the emissions of dust sources, while the method used to calculate the emission of SD may be more suitable for dry and semi-dry areas with less rainfall. It was also found that when the dust emissions stay stable, the contribution of dust sources to the ambient PM in different seasons can vary by 3-4 times. Therefore, under adverse meteorological conditions, it is necessary to strengthen the control of various dust sources and reduce the influence of human factors on them.
建立精细化的扬尘排放清单对于大气污染的防治至关重要。本研究采用国内常用的编制技术，收集活动数据并结合遥感和野外调查数据，编制了 2020 年雄安新区土壤尘（SD）、道路尘（RD）和建筑尘（CD）的扬尘排放清单。采用 CALPUFF 模型阐明了尘源对环境颗粒物（PM）的贡献特征，并验证了扬尘排放清单编制方法的准确性。结果表明，PM 和 PM 的总排放量分别为 43081.14 吨和 9701.69 吨。同时，RD 和 CD 是主要的排放源，占总排放量的 98.49%以上。不同类型扬尘源对环境 PM 的总贡献为 42.59μg/m（29.38%），其中 RD（32.63μg/m，22.51%）的贡献约为 CD（9.78μg/m，6.74%）的三倍。道路是扬尘的主要来源，但大规模基础设施建设是 RD 排放高和贡献高的主要原因。结果表明，排放清单编制方法可用于估算扬尘源的排放量，而 SD 排放量的计算方法可能更适用于降雨较少的干旱和半干旱地区。研究还发现，当扬尘排放量保持稳定时，不同季节尘源对环境 PM 的贡献可相差 3-4 倍。因此，在不利的气象条件下，有必要加强对各类扬尘源的控制，减少人为因素对其的影响。
