Zhang Yu-Xin, An Jun-Lin, Wang Jun-Xiu, Shi Yuan-Zhe, Liu Jing-da, Liang Jing-Shu
Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Huan Jing Ke Xue. 2018 Feb 8;39(2):502-510. doi: 10.13227/j.hjkx.201706216.
Ambient volatile organic compounds (VOCs) were continuously measured during the high ozone (O) periods from May 1 to May 31 and June 1 to July 16, 2015 at an industrial area in the north suburb of Nanjing. A positive matrix factorization (PMF) model and an observation-based model (OBM) were combined for the first time to investigate the contributions of VOC sources and species to local photochemical O formation. The average VOC concentrations in 2014 and 2015 were (36.47±33.44)×10 and (34.69±34.08)×10, respectively. The VOC sources identified by the PMF model for 2014 and 2015 belonged to 7 source categories, including vehicular emissions, liquefied petroleum gas usage, biogenic emissions, furniture manufacturing industry, chemical industry, chemical coating industry, and chemical materials industry emission sources. The OBM was modified to assess the O precursors' relationships. Generally, photochemical O production was VOC limited, with positive relative incremental reactivity (RIR) values for VOC species and a negative RIR value for NO. It can be seen that alkenes (1.20-1.79) and aromatics (1.42-1.48) presented higher RIR values and controlling O would be the most effective when the VOC emissions from alkenes were reduced by 80%. Vehicle emissions (1.01-1.11), LPG (0.74-0.82), biogenic emissions (0.34-0.42), and furniture manufacturing industry (0.32-0.49) sources were the top four VOC sources making significant contributions to photochemical O formation, which suggests that controlling vehicle emissions, biogenic emissions, LPG, and furniture manufacturing industry sources should be the most effective strategy to reduce photochemical O formation.
2015年5月1日至5月31日以及6月1日至7月16日高臭氧(O)时段,对南京北郊某工业区的环境挥发性有机化合物(VOCs)进行了连续监测。首次将正矩阵因子分解(PMF)模型和基于观测的模型(OBM)相结合,以研究VOCs来源和物种对当地光化学O形成的贡献。2014年和2015年的VOCs平均浓度分别为(36.47±33.44)×10和(34.69±34.08)×10。PMF模型识别出2014年和2015年的VOCs来源属于7个源类别,包括机动车排放、液化石油气使用、生物源排放、家具制造业、化学工业、化学涂料工业和化学材料工业排放源。对OBM进行了修正以评估O前体的关系。总体而言,光化学O生成受VOCs限制,VOCs物种的相对增量反应活性(RIR)值为正,NO的RIR值为负。可以看出,烯烃(1.20 - 1.79)和芳烃(1.42 - 1.48)呈现出较高的RIR值,当烯烃的VOCs排放量减少80%时,控制O将最为有效。机动车排放(1.01 - 1.11)、液化石油气(0.74 - 0.82)、生物源排放(0.34 - 0.42)和家具制造业(0.32 - 0.49)源是对光化学O形成有显著贡献的前四大VOCs来源,这表明控制机动车排放、生物源排放、液化石油气和家具制造业源应是减少光化学O形成的最有效策略。