1State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
2Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3College of Software, Nankai University, Tianjin 300350, China
4Department of Physics, University of Nevada Reno, Reno, Nevada, USA 89557
5Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332
1State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
2Chinese Research Academy of Environmental Sciences, Beijing 100012, China
3College of Software, Nankai University, Tianjin 300350, China
4Department of Physics, University of Nevada Reno, Reno, Nevada, USA 89557
5Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332
Received: 25 Oct 2017 – Accepted for review: 26 Feb 2018 – Discussion started: 07 Mar 2018
Abstract. Time series of pollutant concentrations consist of variations at different time scales that are attributable to many processes/sources (data noise, source intensities, meteorological conditions, climate, etc.). Improving the knowledge of the impact of multiple temporal-scale components on pollutant variations and pollution levels can provide useful information for suitable mitigation strategies for pollutant control during a high pollution episode. To investigate the source factors driving these variations, the Kolmogorov-Zurbenko (KZ) filter was used to decompose the time series of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and chemical species into intra-day, diurnal, synoptic, and baseline temporal-scale components (TS components). The synoptic TS component has the largest amplitude and relative contributions (about 50 %) to the total variance of SO42−, NH4+, and OC concentrations. The diurnal TS component has the largest relative contributions to the total variance of PM2.5, NO3−, EC, Ca, and Fe concentrations, ranging from 32 % to 47 %. To investigate the source impacts on PM2.5 from different TS components, four datasets RI (intra-day removed), RD (diurnal removed), RS (synoptic removed), and RBL (baseline removed) were created by respectively removing the intra-day, diurnal, synoptic, and baseline TS component from the original datasets. Multilinear Engine 2 (ME-2) and/or principal component analysis was applied to these four datasets as well as the original datasets for source apportionment. ME-2 solutions using the original and RI dataset identify crustal dust contributions. For the solutions from original, RI, RD, and RS datasets, the total primary source impacts are close, ranging from 35.1 to 40.4 μg m−3 during the entire sampling period. For the secondary source impacts, solutions from the original, RI and RD dataset give similar source impacts (about 30 μg m−3), which were higher than the impacts derived from the RS datasets (21.2 μg m−3).
A finding here is that source emission dominates the level of pollutants and short-term meteorological condition determines the variation of pollutants. Primary source impact levels are mainly influenced by source emissions, and secondary source impact levels are mainly influenced by synoptic scale fluctuations and source emissions. The implications of results are for source apportionment analyses conducted with data from different geographical locations and under various weather conditions.
A finding here is that source emission dominates the level of pollutants and short-term...
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