13 Sep 2023
 | 13 Sep 2023
Status: this preprint is currently under review for the journal AMT.

Hybrid Instruments Network Optimization for Air Quality Monitoring

Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi

Abstract. The significance of air quality monitoring for analyzing the impact on public health is growing worldwide. A crucial part of smart city development includes deployment of suitable air pollution sensors at critical locations. Note that there are various air quality measurement instruments ranging from expensive reference stations that provide accurate data to low-cost sensors that provide reasonable air quality measurements. In this research, we use a combination of sensors and monitors, which we call as hybrid instruments and focus on optimal placement of such instruments across a region. The objective of the problem is to maximize a satisfaction function that quantifies the weighted closeness of different regions to the places where such hybrid instruments are placed (here weights for different regions are quantified in terms of the relative population density and relative PM2.5 concentration). Note that there can be several constraints such as those on budget, minimum number of reference stations to be placed, set of important regions where at least one sensor should be placed and so on. We develop two algorithms to solve this problem. The first one is a genetic algorithm that is a metaheuristic and works on the principles of evolution. The second one is a greedy algorithm that selects locally best choice in each iteration. We test these algorithms on different regions from India with varying sizes and other characteristics such as population distribution, PM2.5 concentration, budget available, etc. The insights obtained from this paper can be used to quantitatively place reference stations and sensors in large cities rather than using ad hoc procedures or rules of thumb.

Nishant Ajnoti et al.

Status: open (until 21 Oct 2023)

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  • RC1: 'Comment on amt-2023-173', Anonymous Referee #1, 20 Sep 2023 reply

Nishant Ajnoti et al.

Nishant Ajnoti et al.


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Short summary
This research focuses on the optimal placement of hybrid instruments (sensors and monitors) to maximize satisfaction function considering population, PM2.5 concentration, budget and other factors. Two algorithms are developed in this study: a genetic algorithm and a greedy algorithm. We tested these algorithms on various regions. The insights of this work aid in quantitative placement of air quality monitoring instruments in large cities, moving away from ad hoc approaches.