Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning

Patel, Kanan; Bhandari, Sahil; Gani, Shahzad; Kumar, Purushottam; Baig, Nisar; Habib, Gazala; Apte, Joshua S.; Ruiz, Lea Hildebrandt

doi:10.1080/02786826.2023.2193237

Cited by 5 publications

(3 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Nanos [28] and Rai et al [29] believed that with the increase in particulate matter content, the photosynthetic rate, number of stomata, and stomatal conductance of leaves would be affected to a certain extent, thereby reducing the amount of dust retention in plants to a certain extent. Meteorological factors that affect particulate matter concentrations also include humidity, temperature, and wind speed [30]. Within a day, the dust retention of the same plant can change over time.…”

Section: Introductionmentioning

confidence: 99%

Dust Retention Effect of Greenery in Typical Urban Traffic Landscapes of Nanjing—In the Case of Xuanwu Avenue in Nanjing City

Sheng,

Zhang,

Meng

et al. 2024

Sustainability

View full text Add to dashboard Cite

With the accelerated process of urbanization, air pollution has become increasingly severe. Garden plants can trap atmospheric particulate matter, which is of great significance for improving the urban ecological environment and promoting sustainable development. To investigate the dust retention effect of typical transportation green spaces in Nanjing, this study focuses on thirteen garden plants on Xuanwu Avenue in Nanjing. The dust retention capacity of these plants was determined using the wash-off method, while the microstructure of their leaf surfaces was observed using scanning electron microscopy. The results are as follows: Firstly, per unit leaf area, Liriope spicata, Ophiopogon japonicus, and Viburnum odoratissimum demonstrate solid dust retention abilities. Additionally, Viburnum odoratissimum, Prunus serrulata var. Lannesiana, and Liriope spicata show strong dust retention abilities per single leaf. Moreover, Platanus acerifolia, Viburnum odoratissimum, and Cinnamomum camphora have strong dust retention abilities per plant. Viburnum odoratissimum, Platanus acerifolia, and Prunus serrulata var. Lannesiana exhibit the most substantial dust retention capacities. Secondly, there is a significant negative correlation between dust retention per plant and the potassium content, while a significant positive correlation is observed with plant height, canopy height, and leaf width. Furthermore, there is a highly significant positive correlation between dust retention per unit leaf area and stomatal length and a highly significant negative correlation with leaf length. The surface microstructure of the blade mainly increases the dust retention capacity of the blade by increasing the friction of the leaf surface. Lastly, specific leaf surface microstructures, such as grooved epidermis and trichomes, enhance plants’ dust retention capacity. Consequently, for the future configuration of road green spaces in Nanjing, a mixed planting mode of trees, shrubs, and grass is recommended. Priority should be given to selecting plants with strong overall dust retention capabilities, such as Platanus acerifolia, Viburnum odoratissimum, and Prunus serrulata var. Lannesiana, to alleviate air pollution, improve the urban ecological environment, and achieve sustainable development.

show abstract

Section: Introductionmentioning

confidence: 99%

Dust Retention Effect of Greenery in Typical Urban Traffic Landscapes of Nanjing—In the Case of Xuanwu Avenue in Nanjing City

Sheng,

Zhang,

Meng

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Liu et al employed intelligent algorithms to seek optimal parameters, proposing a genetic algorithm-based extreme learning machine model [ 22 ]. Patel et al conducted sensitivity analysis to comprehend the individual factor impacts, subsequently employing a random forest model for predicting air quality in Delhi [ 23 ]. Ma et al employed a variety of machine learning models such as ANN, XGBoost, and SVM to construct an ensemble method for predicting air quality in Macau [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Xia,

Chen,

Wang

et al. 2024

Entropy

View full text Add to dashboard Cite

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction’s mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

show abstract

“…The strict Covid-19 lockdown measures provided a unique opportunity to study the changes in event-driven vehicular emissions (González-Pardo et al, 2022;Borlaza et al, 2023;Hay et al, 2023;Patel et al, 2023), formulating a scientific basis for designing future air quality mitigation strategies.…”

Section: Introductionmentioning

confidence: 99%

Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning

Wang,

Duan,

Zhang

et al. 2023

Atmos. Chem. Phys.

View full text Add to dashboard Cite

Abstract. Exposure to elemental carbon (EC) and NOx is a public health issue that has been gaining increasing interest, with high exposure levels generally observed in traffic environments, e.g., roadsides. Shanghai, home to approximately 25 million in the Yangtze River Delta (YRD) region in eastern China, has one of the most intensive traffic activity levels in the world. However, our understanding of the trend in vehicular emissions and, in particular, in response to the strict Covid-19 lockdown is limited partly due to the lack of a long-term observation dataset and application of advanced mathematical models. In this study, NOx and EC were continuously monitored at a sampling site near a highway in western Shanghai for 5 years (2016–2020). The long-term dataset was used to train the machine learning model, rebuilding NOx and EC in a business-as-usual (BAU) scenario for 2020. The reduction in NOx and EC attributable to the lockdown was found to be smaller than it appeared because the first week of the lockdown overlapped with the Lunar New Year holiday, whereas, at a later stage of the lockdown, the reduction (50 %–70 %) attributable to the lockdown was more significant, consistent with the satellite monitoring of NO2 showing reduced traffic on a regional scale. In contrast, the impact of the lockdown on vehicular emissions cannot be represented well by simply comparing the concentration before and during the lockdown for conventional campaigns. This study demonstrates the value of continuous air pollutant monitoring at a roadside on a long-term basis. Combined with the advanced mathematical model, air quality changes due to future emission control and/or event-driven scenarios are expected to be better predicted.

show abstract

Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning

Cited by 5 publications

References 73 publications

Dust Retention Effect of Greenery in Typical Urban Traffic Landscapes of Nanjing—In the Case of Xuanwu Avenue in Nanjing City

Dust Retention Effect of Greenery in Typical Urban Traffic Landscapes of Nanjing—In the Case of Xuanwu Avenue in Nanjing City

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning

Contact Info

Product

Resources

About