Optimization of ambient air quality monitoring networks

Modak, Prasad; Lohani, B. N.

doi:10.1007/bf00396391

Cited by 43 publications

(11 citation statements)

References 17 publications

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“…Well-known cases include those using an integrated MT3D ground water transport model and integer programming for ground water quality monitoring network design in Santa Barbara County, CA, USA [32], using QUAL2E simulation model and multiobjective programming for surface water quality monitoring network relocation and design in Pentung, Taiwan [26,54,55], using the Industrial Source Complex and Empirical Kinetic Modeling Analysis and deterministic compromise programming or genetic algorithms-based grey compromises programming for air quality monitoring network design in Kaohsiung, Taiwan [7,8,74]. In particular, Modak and Lohani [46][47][48] aggregated the Air Quality Index in support of a Minimum Spanning Tree optimization analysis for an air quality network rationalization in Taipei, Taiwan.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Chang

Makkeasorn

2009

Environ Model Assess

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Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are all intimately related with each other to form water balance dynamics on the surface of the Earth. To monitor change in hydrological systems with minimum effort, however, hydrological monitoring networks at the watershed scale should be deployed at critical locations to advance the monitoring and sensing capability. One of the science questions is how to develop an optimum arrangement/ distribution strategy of those monitoring platforms with respect to hydrological components subject to technical and resources constraints. While the complexities arise from the integration of highly heterogeneous data streams in the hydrological cycle under uncertainty, there is an acute need to develop a site screening and sequencing procedure permitting a cost-effective search for final site selection. This paper purports to develop such an approach to address the optimal site selection strategy by integrating satellite remote sensing images with a grey integer programming (GIP) model. The approach uses spatial information on the range of likely values temporally encountered for a number of biophysical descriptors in support of the optimization analysis under uncertainty. Practical implementation was assessed by a case study in a semi-arid watershed-the Choke Canyon Reservoir watershed, south Texas. GIS-based GIP modeling technique successfully supports the screening and sequencing mechanism based on the composite satellite images, which smoothly prioritizes the relative importance and provides the rank order scores across all candidate sites. With the aid of such a synergistic approach, seven locations out of 563 candidate sites were eventually selected and confirmed by a field investigation.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Chang

Makkeasorn

2009

Environ Model Assess

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“…Since not all violations have the same severity, a weighting factor has been used to characterize the violations to each range for the pollutants CO, SO 2 , and NO x . The segmented non‐linear weighting function has been chosen in this research:

{normalN}_{normalv}^{normali} = \sum_{normali = 1}^{T} true \sum_{k = 1}^{{normalN}_{normalt}} \frac{true({normalw}_{k + 1} - w_{normalk} true) true({normalx}_{normali} - x_{normalk} true) X}{true({normalx}_{k + 1} - x_{normalk} true)}

where

{normalN}_{normalv}^{normali}

is the violation score for the i th candidate location, w k is the weighing factor corresponding to threshold x k , x k is the k th threshold ( X = 0, if ( x i – x k ) ≤ 0 and X = 1, otherwise), N t is the total number of thresholds, and T is the total number of simulated observations.…”

Section: Air Quality Monitoring Network Designmentioning

confidence: 99%

A Surrogate‐Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network

et al. 2015

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A surrogate‐based optimization methodology was proposed for identifying and determining the optimal location and configuration of an air quality monitoring network (AQMN) in an industrial area for different pollutants such as sulfur dioxide (SO2), nitrogen oxide (NOx), and carbon monoxide (CO). Within the framework of the described methodology, an optimal AQMN design was proposed to assess the violation and pattern scores for each pollutant. For this purpose, a criterion for assessing the allocation of monitoring stations was developed by applying a utility function that could describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. An air dispersion model based on the multiple cell approach was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different sources. The data was used to develop the surrogate models. The proposed methodology was applied to a network of existing refinery stacks, and the locations of monitoring stations and their area coverage percentage were obtained. Results clearly indicated that the proposed methodology was successful in designing AQMNs and could be used for as many stations as required.

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“…The incorporation of multiple objectives is considered as extremely important to lay the foundation of the future practices towards AQMN optimization (Munn 1981;Naioto and Ochiai 1981). As yet, however, only a few methodologies exist in the literature that can accomplish the task of designing a network capable of fulfilling all of the objectives stated above (Modak and Lohani 1984a, b;TrujilloVentura and Hugh Ellis 1991;Smith and Egan 1979). Every moderate or large scale multi-pollutant AQMN adheres to a policy of retaining a maximum number of common sites, i.e., sites where a number of pollutants are simultaneously measured.…”

Section: Introductionmentioning

confidence: 98%

“…In fact, present multi-pollutant network optimization and design approaches focus on the combination of SO 2 with smoke (Modak and Lohani 1984a;Green 1966) due to their high degree of affinity. This is up to a certain point justifiable.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of air quality monitoring

Sarigiannis

Saisana

2007

Environ Monit Assess

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A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.

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Optimization of ambient air quality monitoring networks

Cited by 43 publications

References 17 publications

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

Optimal Site Selection of Watershed Hydrological Monitoring Stations Using Remote Sensing and Grey Integer Programming

A Surrogate‐Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network

Multi-objective optimization of air quality monitoring

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