Design of a sensitive air quality monitoring network using an integrated optimization approach

Benis, Khaled Zoroufchi; Fatehifar, Esmaeil; Shafiei, Sirous; Nahr, Fatemeh Keivani; Purfarhadi, Yaser

doi:10.1007/s00477-015-1189-z

Cited by 21 publications

(14 citation statements)

References 44 publications

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“…The MND can be determined by its placement criteria based on study objectives. The determination of spatial and temporal variations in the air pollutant concentration and its degradation are two widely used underline consideration for choosing the air quality MND [29]. MND selection can be considered as an optimisation problem, i.e., searching for the best combination of potential locations [30].…”

Section: Related Workmentioning

confidence: 99%

“…An heuristic approach such as spatial simulated annealing (SSA) can be helpful in solving the optimisation problems for location selection at different scales [31,32]. Many studies have extended the optimisation criterion for various other constraints, such as protecting sensitivity receptors (e.g., population districts and historic buildings), response to emission sources nearby, and minimising costs [29,33,34]. Because of these increasingly complex constraints, optimisation of MND for monitoring air pollution is considered a demanding task [31].…”

Section: Related Workmentioning

confidence: 99%

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Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

et al. 2018

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A very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic "hot spots", or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term exposure analysis. This paper introduces a systematic approach for identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing the approach over the study area has shown that it leads to a significant drop of the mean prediction error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of this work can guide the selection of sites while expanding or creating air quality monitoring networks for robust LUR estimations with minimal prediction errors.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

et al. 2018

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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. Benis et al in [8] designed a sensitive AQMN based on Ant Colony Optimization (ACO) and Genetic Algorithm (GA) respectively for an industrial district. Considering the capability of quick diagnosis of pollution episodes, the sensitivity of potential locations to sources was considered as a design objective as well as the coverage area and violation over ambient standards.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Liu

et al. 2019

Atmosphere

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Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H 2 S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (r d ) and source identification efficiency (r b ). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of r d and r b were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

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Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

Gruszecka‐Kosowska

2016

Stoch Environ Res Risk Assess

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Health risk associated with the exposure to the polluted atmospheric air inhalation was estimated for the residents of Kraków, Poland. The air pollution concentration data were obtained from the air-quality monitoring system of the city in 2007-2016. The carcinogenic risk of the studied subpopulations was not acceptable under the formula of C 6 H 6 [ BaP [ As(PM10) [ Cd(PM10) [ Pb(PM10) [ Ni(PM10). The total carcinogenic risk (Rt) amounted to 3.04E-04 for children, 2.22E-04 for infants, 1.45E-04 for women, and 1.22E-04 for men. The same risk was calculated for the top three locations of the monitoring stations in this respect, within the city of Kraków: Kurdwanów Housing Estate, Nowa Huta district, and Krasińskiego Av. Non-carcinogenic risk in the case of all six monitoring stations and in respect of all the studied subpopulations, resulting from the exposure to PM10 and for NO 2 for all stations in case of children and infants, as well as, for adults at Krasińskiego Av. and Dietla Str. stations was rated medium. For C 6 H 6 in the case of adults, children, and infants the risk was rated low. The total risk (HI) of non-carcinogenic pollution was rated medium and ranged as follows: 6.53 for children, 4.70 for infants, 3.19 for women, and 2.67 for men. That type of risk was decreasing at the station locations as follows: Krasińskiego Av. [ Dietla Str. [ Nowa Huta district [ Kurdwanów Housing Estate [ Złoty Róg Str. [ Piastów Housing Estate.

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Design of a sensitive air quality monitoring network using an integrated optimization approach

Cited by 21 publications

References 44 publications

Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models

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

Assessment of the Kraków inhabitants’ health risk caused by the exposure to inhalation of outdoor air contaminants

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