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

Gupta, Shivam; Pebesma, Edzer; Mateu, Jorge; Degbelo, Auriol

doi:10.3390/su10051442

Cited by 14 publications

(13 citation statements)

References 51 publications

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“…For the objective function, the manipulation of the set of locations leads to the modification of X matrix values. For further details about the above-mentioned objective function, we suggest referring to the work done by Gupta et al [27].…”

Section: Prediction Error Aspectmentioning

confidence: 99%

“…It is essential to have a constraint which can limit the clustering and enforce the selection of disparate locations. Hence, we extended the objective function developed by Gupta et al [27] to take into account the wide-spread distribution aspects of sensors in VGI-based monitoring network design.…”

Section: Widespread Distribution Aspectmentioning

confidence: 99%

“…We extended the optimisation method proposed by Gupta et al [27] by incorporating wide-spread distribution aspects (in addition to LUR's predictor error aspects) into the placement of low-cost citizen sensors for air quality monitoring.…”

mentioning

confidence: 99%

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Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

Gupta

Pebesma

Degbelo

et al. 2018

IJGI

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Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities.

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Section: Prediction Error Aspectmentioning

confidence: 99%

Section: Widespread Distribution Aspectmentioning

confidence: 99%

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Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

Gupta

Pebesma

Degbelo

et al. 2018

IJGI

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“…Involving a large number of housing companies in cities would help monitor air quality at a finer spatial granularity, thereby helping to address the completeness issue. By identifying optimal locations, the structure and extent of the air quality monitoring network could be defined (see Gupta, Pebesma, Mateu & Degbelo, 2018), and PS data handling and completeness could be managed more efficiently. Housing companies as campaign administrators could maintain PS infrastructures (one of the key issues at the moment), leading to more reliable nodes in the PS network and reduced chances of erroneous contributions from the PS nodes.…”

Section: Addressing Data Completeness Challengesmentioning

confidence: 99%

Connecting Citizens and Housing Companies for Fine-grained Air-Quality Sensing

Gupta¹,

Degbelo²,

Pebesma³

2018

giforum

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The complex nature of air quality suggests the need for fine-scale air-quality monitoring in cities. With 1 in 8 deaths worldwide being associated with air pollution in 2012, communities have started partnering with academic institutions and with state and federal agencies to assess local air quality and address these concerns. Participatory sensing has recently become one popular method for collecting air-quality information. It offers the prospect of collecting data at finer levels of granularity, but is subject to at least two significant challenges: data gaps (due, e.g., to the lack of calibration, maintenance and replacement of sensors), and citizens' concerns regarding privacy). We argue that including housing companies as stakeholders in participatory sensing frameworks may be beneficial in overcoming these challenges. A survey of housing companies suggests that they are willing to participate in air-quality monitoring for cities. The ideas presented here are pertinent to the design of more robust and privacy-aware participatory sensing frameworks.

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“…The maximization of coverage with minimum overlap and the ability to detect violations of standards were considered as the design objectives. Gupta et al in [16] developed a systematical method by combing Land use regression (LUR) and Spatial annealing simulating (SSA) to place AQMN for decreasing the spatial mean predictor error in highly-populated spaces. However, few have focused on the source reconstruction performance of AQMN in optimal design.…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 14 publications

References 51 publications

Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

Optimising Citizen-Driven Air Quality Monitoring Networks for Cities

Connecting Citizens and Housing Companies for Fine-grained Air-Quality Sensing

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

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