Urban meteorology and air quality in a rapidly growing city: Inter-parameter associations and intra-urban heterogeneity

Ulpiani, Giulia; Hart, Mary; Virgilio, Giovanni Di; Maharaj, Angela M.

doi:10.1016/j.scs.2021.103553

Cited by 15 publications

(5 citation statements)

References 47 publications

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“…MI analysis is effective at detecting versatile associations in the data [17] and contributes to the understanding of the mechanisms behind data intercorrelations [18,19]. The results of the MI analysis agree with what is known about the physics of the phenomena represented by the data [20].…”

Section: Introductionsupporting

confidence: 66%

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Maciejewska,

Azizah,

Szczurek

2023

Sensors

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High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.

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Section: Introductionsupporting

confidence: 66%

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Maciejewska,

Azizah,

Szczurek

2023

Sensors

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“…In the proposed model, the DCP is extracted and fed parallelly into a CNN model that enhances the features. Meteorological features have a direct correlation with air pollution concentrations and are highly influential in air quality estimation (Ji et al 2020;Ulpiani et al 2022;Aladag 2023). Our model uses a parallel 1D-CNN network trained on meteorological variables, which supplements the image features.…”

Section: Statements and Declarations Introductionmentioning

confidence: 99%

An ensemble deep learning approach for air quality estimation in Delhi, India

Mohan,

Abraham

2024

Earth Sci Inform

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South Asian megacities are significant contributors to the degrading air quality. In highly populated norther n India, Delhi is a major hotspot for air pollutants that influence health and climate. Effective mitigation of air pollution is impeded by inadequate estimation which emphasizes the need for cost-effective alternatives. This paper proposes an ensemble model based on transformer and Convolutional Neural Network (CNN) models to estimate air quality from images and weather parameters in Delhi. A Data Efficient Image transformer (DeiT) is fine-tuned with outdoor images, and parallelly dark-channel prior extracted from images are fed to a CNN model. Additionally, a 1-dimensional CNN is trained with meteorological features to improve accuracy. The predictions from these three parallel branches are then fused with ensemble learning to classify images into six Air Quality Index (AQI) classes and estimate the AQI value. To train and validate the proposed model, an image dataset is collected from Delhi, India termed 'AirSetDelhi' and properly labeled with ground-truth AQI values. Experiments conducted on the dataset demonstrate that the proposed model outperforms other deep learning networks in the literature. The model achieved an overall accuracy of 89.28% and a Cohen Kappa score of 0.856 for AQI classification, while it obtained an RMSE of 47.36 and an R 2 value of 0.861 for AQI estimation, demonstrating efficacy in both tasks. As a regional estimation model based on images and weather features, the proposed model offers an alternative feasible approach for air quality estimation.

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“…A lot of scholars also have conducted extensive research on the correlation between the concentrations of air pollutants and meteorological factors [5][6][7][15][16][17][18]. Zhang et al [15] analyzed the mass concentration characteristics of air pollutants and change characteristics of air quality in Shenyang from 2014 to 2019 and the effect of meteorological factors on the mass concentration of atmospheric pollutants PM 10 , PM 2.5 , and O 3 .…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [15] analyzed the mass concentration characteristics of air pollutants and change characteristics of air quality in Shenyang from 2014 to 2019 and the effect of meteorological factors on the mass concentration of atmospheric pollutants PM 10 , PM 2.5 , and O 3 . Ulpiani et al [17] studied the intercorrelation between the meteorological factors and pollutant concentrations in Sydney, Australia, and showed that strong correlation exists between temperature and NO 2 , and relative humidity and PM 2.5 . Barzeghar et al [18] explored the relationships between air pollutants and meteorological factors using Spearman's rank correlation test and demonstrated that NO 2 , NO, SO 2 , and CO have a obvious correlation with meteorological factors.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Air Pollution and Its Relationship with Meteorological Factors: A Case Study of 31 Provincial Capitals in China

Cai¹,

Yu²,

Qin³

2023

Pol. J. Environ. Stud.

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Analyzing the spatial distribution of meteorological factors and air pollutants and its correlation is of great importance for urban air pollution prevention. This study explored the spatial distribution of meteorological factors and air pollutants, analyzed the correlation between meteorological factors and air pollutants, and revealed the main meteorological factors that influence the air pollutants in 31 provincial capitals of China during 2010-2020. The results show that the spatial distribution characteristics of air pollutants and the days of air quality equal to or above grade II in 31 provincial capitals of China during 2010-2020 vary obviously. The days of air quality equal to or above grade II are negatively correlated with the annual average concentration of NO 2 and annual average concentration of PM 10 during 2010-2020 , and annual average concentration of SO 2 during 2010 to 2018. The temperature, precipitation, and relative humidity are the main meteorological factors affecting the air quality in different seasons in 31 provincial capitals of China during 2010-2020. The findings will provide a scientific basis for atmospheric environment control and urban air quality forecast.

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Urban meteorology and air quality in a rapidly growing city: Inter-parameter associations and intra-urban heterogeneity

Cited by 15 publications

References 47 publications

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis

An ensemble deep learning approach for air quality estimation in Delhi, India

Spatial Distribution of Air Pollution and Its Relationship with Meteorological Factors: A Case Study of 31 Provincial Capitals in China

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