Source apportionment of atmospheric particle number concentrations with wide size range by nonnegative matrix factorization (NMF).

Quantifying the sources of atmospheric particles is essential to air quality control but remains challenging, especially for the source apportionment of particles based on number concentration with wide size range. Here, particle number concentrations (PNC) with size range 19-20,000 nm involving four modes Nucleation, Aitken, Accumulation, and Coarse are used to do source apportionment of PNC at the Guangdong Atmospheric Supersite (Heshan) during July-October 2015 by nonnegative matrix factorization (NMF) with 6 factors. For July 2015, separated source apportionments for three different size ranges from collocated instruments nano scanning mobility particle sizer (NSMPS), SMPS, and aerodynamic particle sizer (APS) and for two different size ranges (below and above 100 nm) show similar quantitative source information with that for the one whole size range. The mean absolute difference of contribution percentages of total particle number concentrations (TPNC) based on 5 unique apportioned sources is 5.6 % (4.3-7.6 %) for the instrument segregated apportionment and 4.2 % (0-5.3 %) for the size range segregated apportionment respectively, relative to the one whole apportionment. Moreover, the contribution percentages of TPNC are close to the weighted sum of contribution percentages of all size bins, with a mean absolute difference of 1.1 % (0-3.4 %). In both these two aspects, the consistency among different technical paths proves the matrix factorization by NMF is practically desirable and the simplicity of reducing some steps or calculations saves time. Besides, dust can be identified with the wide size range including larger than 3000 nm. Six apportioned sources in the 4 months are Accumulation (32.4 %), Nucleation (20.0 %), Aitken (15.2 %), traffic (14.6 %), dust (10.6 %), and Coarse (7.1 %). Therefore, NMF would serve as a promising tool for PNC source apportionment with wide size range and conducting the apportionment with the whole size range in one matrix factorization procedure and using the single TPNC contribution percentage are feasible.

Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations.

Number concentration is an important index to measure atmospheric particle pollution. However, tailored methods for data preprocessing and characteristic and source analyses of particle number concentrations (PNC) are rare and interpreting the data is time-consuming and inefficient. In this method-oriented study, we develop and investigate some techniques via flexible conditions, C++ optimized algorithms, and parallel computing in R (an open source software for statistics and graphics) to tackle these challenges. The data preprocessing methods include deletions of variables and observations, outlier removal, and interpolation for missing values (NA). They do better in cleaning data and keeping samples and generate no new outliers after interpolation, compared with previous methods. Besides, automatic division of PNC pollution events based on relative values suites PNC properties and highlights the pollution characteristics related to sources and mechanisms. Additionally, basic functions of k-means clustering, Principal Component Analysis (PCA), Factor Analysis (FA), Positive Matrix Factorization (PMF), and a newly-introduced model NMF (Non-negative Matrix Factorization) were tested and compared in analyzing PNC sources. Only PMF and NMF can identify coal heating and produce more explicable results, meanwhile NMF apportions more distinctly and runs 11-28 times faster than PMF. Traffic is interannually stable in non-heating periods and always dominant. Coal heating's contribution has decreased by 40%-86% in recent 5 heating periods, reflecting the effectiveness of coal burning control.

Assessment of regional air quality by a concentration-dependent Pollution Permeation Index.

Although air quality monitoring networks have been greatly improved, interpreting their expanding data in both simple and efficient ways remains challenging. Therefore, needed are new analytical methods. We developed such a method based on the comparison of pollutant concentrations between target and circum areas (circum comparison for short), and tested its applications by assessing the air pollution in Jing-Jin-Ji, Yangtze River Delta, Pearl River Delta and Cheng-Yu, China during 2015. We found the circum comparison can instantly judge whether a city is a pollution permeation donor or a pollution permeation receptor by a Pollution Permeation Index (PPI). Furthermore, a PPI-related estimated concentration (original concentration plus halved average concentration difference) can be used to identify some overestimations and underestimations. Besides, it can help explain pollution process (e.g., Beijing's PM2.5 maybe largely promoted by non-local SO2) though not aiming at it. Moreover, it is applicable to any region, easy-to-handle, and able to boost more new analytical methods. These advantages, despite its disadvantages in considering the whole process jointly influenced by complex physical and chemical factors, demonstrate that the PPI based circum comparison can be efficiently used in assessing air pollution by yielding instructive results, without the absolute need for complex operations.

Review on recent progress in observations, source identifications and countermeasures of PM2.5.

Recently, PM2.5 (atmospheric fine particulate matter with aerodynamic diameter ≤ 2.5 µm) have received so much attention that the observations, source appointment and countermeasures of it have been widely studied due to its harmful impacts on visibility, mood (mental health), physical health, traffic safety, construction, economy and nature, as well as its complex interaction with climate. A review on the PM2.5 related research is necessary. We start with summary of chemical composition and characteristics of PM2.5 that contains both macro and micro observation results and analysis, wherein the temporal variability of concentrations of PM2.5 and major components in many recent reports is embraced. This is closely followed by an overview of source appointment, including the composition and sources of PM2.5 in different countries in the six inhabitable continents based on the best available results. Besides summarizing PM2.5 pollution countermeasures by policy, planning, technology and ideology, the World Air Day is proposed to be established to inspire and promote the crucial social action in energy-saving and emission-reduction. Some updated knowledge of the important topics (such as formation and evolution mechanisms of hazes, secondary aerosols, aerosol mass spectrometer, organic tracers, radiocarbon, emissions, solutions for air pollution problems, etc.) is also included in the present review by logically synthesizing the studies. In addition, the key research challenges and future directions are put forward. Despite our efforts, our understanding of the recent reported observations, source identifications and countermeasures of PM2.5 is limited, and subsequent efforts both of the authors and readers are needed.