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

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.

Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R²) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

Amine-Functionalized Covalent Organic Framework for Efficient SO2 Capture with High Reversibility

Removing sulfur dioxide (SO2) from exhaust flue gases of fossil fuel power plants is an important issue given the toxicity of SO2 and subsequent environmental problems. To address this issue, we successfully developed a new series of imide-linked covalent organic frameworks (COFs) that have high mesoporosity with large surface areas to support gas flowing through channels; furthermore, we incorporated 4-[(dimethylamino)methyl]aniline (DMMA) as the modulator to the imide-linked COF. We observed that the functionalized COFs serving as SO2 adsorbents exhibit outstanding molar SO2 sorption capacity, i.e., PI-COF-m10 record 6.30 mmol SO2 g-1 (40 wt%). To our knowledge, it is firstly reported COF as SO2 sorbent to date. We also observed that the adsorbed SO2 is completely desorbed in a short time period with remarkable reversibility. These results suggest that channel-wall functional engineering could be a facile and powerful strategy for developing mesoporous COFs for high-performance reproducible gas storage and separation.