Information Theory Solution Approach to the Air Pollution Sensor Location–Allocation Problem

Abstract

Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location–allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations’ informativity. This entropy method is compared to two commonly used heuristics for solving the location–allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors’ network measurements.

Presenting air quality status through hot spot maps realized by using Kernel Density Estimation (KDE) - case study: Craiova, 2020/2021

“…Thus, we have comparatively analysed research findings referring to areas from different countries, namely: Israel [4], India [6], Slovenia [16], Kuwait [17], Spain [18], Romania [19], Italia [20] and China [5,[21][22][23].…”

“…Though KDE is a flexible approach for visualizing "hot spots" within a map [4], this technique is submitted to some limitations. As in thematic mapping, defining "hot spot" legend thresholds for KDE "hot spot" maps is a subjective process, which is affected by the experience of the researcher, therefore including a lot of trial and experimental error (as an alternative, standardization approaches could be used to develop more objective "hot spot" thresholds levels) [4].…”

“…It can be applied to both urban and rural settings extending to distances up to approximately 20 km from the sources. Based on the original model, several model versions have been developed over the years to treat different regulatory aspects, including assessment of air pollution [1,[4][5][6][7].…”

“…In the simulation area, the concentration in every grid is a direct proportion to the sum of the particles passing through this grid [1,[4][5][6][7].…”

Non-Contact, Continuous Sampling of Porous Surfaces for the Detection of Particulate and Adsorbed Organic Contaminations by Low-Temperature Plasma Coupled to Ion Mobility Spectrometer

“…One of the most significant drawbacks of using these techniques is that collecting samples from corrugated surfaces using swabs may be impractical in many cases [ 10 , 11 ]; the alternative approach of sampling the vapor, as demonstrated by Kendler et al, requires a highly sensitive and complicated sensing setup and applies only to volatile and semi-volatile compounds. Additionally, large efforts are carried out for developing spatial sampling and analysis in order to improve autonomous and unmanned sensor deployment and accurately determine source distribution [ 12 , 13 , 14 , 15 ].…”