Optimization of air monitoring networks using chemical transport model and search algorithm

Abstract

Air monitoring network design is a critical issue because monitoring stations should be allocated properly so that they adequately represent the concentrations in the domain of interest. Although the optimization methods using observations from existing monitoring networks are often applied to a network with a considerable number of stations, they are difficult to be applied to a sparse network or a network under development: there are too few observations to define an optimization criterion and the high number of potential monitor location combinations cannot be tested exhaustively. This paper develops a hybrid of genetic algorithm and simulated annealing to combine their power to search a big space and to find local optima. The hybrid algorithm as well as the two single algorithms are applied to optimize an air monitoring network of PM2.5, NO2 and O3 respectively, by minimization of the mean kriging variance derived from simulated values of a chemical transport model instead of observations. The hybrid algorithm performs best among the algorithms: kriging variance is on average about 4% better than for GA and variability between trials is less than 30% compared to SA. The optimized networks for the three pollutants are similar and maps interpolated from the simulated values at these locations are close to the original simulations (RMSE below 9% relative to the range of the field). This also holds for hourly and daily values although the networks are optimized for annual values. It is demonstrated 3 that the method using the hybrid algorithm and the model simulated values for the calculation of the mean kriging variance is of benefit to the optimization of air monitoring networks.

Extreme Air Pollution in Global Megacities

“…Three primary, and complementary, methods to measure air pollution are ground-based monitors, satellite-based observations, and atmospheric models. First, ground-based data offers detailed air quality information at a point location; station locations should be optimized to best represent concentrations over a spatial domain [15]. Satellites can monitor air quality in areas outside of ground-based monitoring networks, such as in rural areas and/or developing countries [16].…”

Optimal Citizen-Centric Sensor Placement for Air Quality Monitoring: A Case Study of City of Cambridge, the United Kingdom

“…Some studies focused on the optimal design of air quality monitoring networks using air quality estimation models, such as physical models [10], [11] and learning-based models [12]. Reference [10] studied the optimal redistribution of air quality monitoring network using the commonly adopted atmospheric dispersion model and genetic algorithm.…”

“…Reference [10] studied the optimal redistribution of air quality monitoring network using the commonly adopted atmospheric dispersion model and genetic algorithm. Reference [11] studied the optimization of air quality network design using the chemical transport model and searching algorithm. Reference [12] proposed an entropy minimization model for recommending new station locations based on their proposed semi-supervised air quality inference model.…”

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

“…Optimal design of AQMN has been widely investigated in the literature [1][2][3][4][5][6][7][8][9][10][11]. It can be considered as an optimization problem searching for the best combination of the location and number of candidate monitors.…”

“…The AQMN arrangement is determined by different design criteria such as environmental, social, and economic objectives and the monitoring costs [4,12,13]. Many heuristic algorithms have been applied to solve the optimization problem for AQMN design at different scales [6,8,11,14,15]. For example, Henriquez et al in [15] employed a variational approach to compare the results of an optimal arrangement with the current AQMN for Santiago, Chile.…”