Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data

An iterative optimization scheme to accommodate inequality constraints in air quality geostatistical estimation of multivariate PM

The kriging-based estimation of the different types of atmospheric particulate matter (PM) pollutions defined in the air quality regulation raises some operational problems because the (co)kriging equations are obtained by minimizing a linear combination of the estimation variances subject to unbiasedness constraints. As a consequence, the estimation process can result in total PM₁₀ concentrations that are less than the PM_2.5 concentrations which would be physically impossible. In a previous publication, it was shown that a convenient external drift modeling can reduce the number of spatial locations where the inequality constraint is not satisfied, without completely solving the problem. In this work, the formulation of the cokriging system is modified, inspired by previous works focusing on positive kriging. The introduction of additional constraints on the cokriging weights are presented, leading to a unique and optimal solution to the problem of cokriging under inequality constraints between two variables. Some computational and algorithmic details are introduced. An evaluation of the penalized cokriging is provided by using the European PM monitoring sites dataset: some maps and performance scores are given to assess the relevance of our iterative optimization scheme.

Estimation of Ground PM2.5 Concentrations using a DEM-assisted Information Diffusion Algorithm: A Case Study in China

When estimating national PM_2.5 concentrations, the results of traditional interpolation algorithms are unreliable due to a lack of monitoring sites and heterogeneous spatial distributions. PM_2.5 spatial distribution is strongly correlated to elevation, and the information diffusion algorithm has been shown to be highly reliable when dealing with sparse data interpolation issues. Therefore, to overcome the disadvantages of traditional algorithms, we proposed a method combining elevation data with the information diffusion algorithm. Firstly, a digital elevation model (DEM) was used to segment the study area into multiple scales. Then, the information diffusion algorithm was applied in each region to estimate the ground PM_2.5 concentration, which was compared with estimation results using the Ordinary Kriging and Inverse Distance Weighted algorithms. The results showed that: (1) reliable estimate at local area was obtained using the DEM-assisted information diffusion algorithm; (2) the information diffusion algorithm was more applicable for estimating daily average PM_2.5 concentrations due to the advantage in noise data; (3) the information diffusion algorithm required less supplementary data and was suitable for simulating the diffusion of air pollutants. We still expect a new comprehensive model integrating more factors would be developed in the future to optimize the interpretation accuracy of short time observation data.

Highly Integrated Polysulfone/Polyacrylonitrile/Polyamide-6 Air Filter for Multilevel Physical Sieving Airborne Particles

Rational structural design involving controlled pore size, high porosity, and particle-targeted function is critical to the realization of highly efficient air filters, and the filter with absolute particle-screen ability has significant technological implications for applications including individual protection, industrial security, and environmental governance; however, it remains an ongoing challenge. In this study, we first report a facile and scalable strategy to fabricate the highly integrated polysulfone/polyacrylonitrile/polyamide-6 (PSU/PAN/PA-6) air filter for multilevel physical sieving airborne particles via sequential electrospinning. Our strategy causes the PSU microfiber (diameter of ∼1 μm) layer, PAN nanofiber (diameter of ∼200 nm) layer, and PA-6 nanonets (diameter of ∼20 nm) layer to orderly assemble into the integrated filter with gradually varied pore structures and high porosity and thus enables the filter to work efficiently by employing different layers to cut off penetration of particles with a certain size that exceeds the designed threshold level. By virtue of its elaborate gradient structure, robust hydrophobicity (WCA of ∼130°), and superior mechanical property (5.6 MPa), our PSU/PAN/PA-6 filter even can filtrate the 300 nm particles with a high removal efficiency of 99.992% and a low pressure drop of 118 Pa in the way of physical sieving manner, which completely gets rid of the negative impact from high airflow speed, electret failure, and high humidity. It is expected that our highly integrated filter has wider applications for filtration and separation and design of 3D functional structure in the future.