Air pollution in an urban world: A global view on density, cities and emissions

Castells‐Quintana, David; Dienesch, Elisa; Krause, Melanie

doi:10.1016/j.ecolecon.2021.107153

Cited by 75 publications

(21 citation statements)

References 74 publications

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“…What makes it possible for cities to maximally optimize resource allocation and improve resource utilization efficiency, thus reducing regional environmental pollution, is the positive agglomeration effect brought by the concentration of resources in central cities. Population agglomeration can increase the utilization efficiency of regional infrastructures, which can further improve the quality of the regional environment (Andreoni and Levinson, 1998;Castells-Quintana et al, 2021). At the same time, a higher population agglomeration can bring down the cost of public supervision but increase the earnings of supervision; this can stimulate public demand for supervision and positively promote regional pollution emission reduction and pollution treatment (Leeuwen, 2004).…”

Section: Theoretic Analysismentioning

confidence: 99%

Urban spatial structure and regional smog management for environmental sustainability

Qie

Chao

Yang

2022

Front. Environ. Sci.

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The study examines the influence of the urban spatial structure on smog pollution and its mechanism of action using the panel data of Chinese cities from 2012 to 2017. Research suggests that the multi-center development of Chinese cities has aggravated regional smog pollution. Robustness test results support this finding. Results of the interaction term shed light on the fact that multi-center spatial development in backward regions can exert a greater influence on smog pollution. Meanwhile, suppose the research samples are divided by different zones. In that case, it can be observed that an inverted-U shape characterizes the multi-center development of regions of East China and smog pollution. This indicates that worsening smog pollution appears only as a periodical phenomenon of the pre-stage multi-center development of Chinese cities, which can be ushered into a turning point through certain paths. Analysis of the influence mechanism shows that the transport mode and the energy consumption intensity constitute two major channels for multi-center development of Chinese cities to affect regional smog pollution.

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Section: Theoretic Analysismentioning

confidence: 99%

Urban spatial structure and regional smog management for environmental sustainability

Qie

Chao

Yang

2022

Front. Environ. Sci.

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“…With the continuous advancement of industrialization and urbanization, air pollution has become an increasingly serious problem and has caused widespread concerns around the world (Castells-Quintana et al 2021). Establishing air quality prediction model and taking corresponding control measures in industrial production and daily life can effectively reduce the harm of air pollution to human health and environment.…”

Section: Introductionmentioning

confidence: 99%

Regional Collaborative Prediction of Air Pollutants Based on CNN-BiLSTM Model

2023

Preprint

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The development of industry has brought serious air pollution problems. It is very important to establish a high-precision and high-performance air quality prediction model and take corresponding control measures. In this paper, based on four years of air quality and meteorological data in Tianjin, China, the relationship between various meteorological factors and air pollutant concentrations are analyzed, the abnormal data are detected and preprocessed. A hybrid deep learning model consisting of convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to predict pollutant concentrations, and the effects of three different database input modes are compared. In addition, the Bayesian optimization algorithm is applied to obtain the optimal combination of hyper-parameters for the proposed deep learning model, which makes the model have higher generalization ability. Furthermore, based on the air quality data of multi stations in the region, a regional collaborative prediction method is designed, the concept of strongly-correlated station (SCS) is defined, and the results of collaborative prediction are modified using the idea of SCS, effectively improving the accuracy of prediction.

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“…Increasing air pollution has become a major concern for the environmental quality of life in the urban areas. Recent studies visualize the grim reality of air pollution and its health risks across the cities of the world (Leffel et al 2022 ; Castells-Quintana et al 2021 ; WHO 2020 ; Pant et al 2019 ). For strategizing any control or eradication program, first step would be to monitor air quality at possibly high temporal and spatial resolutions to identify the source, location, quality and quantity of pollutants (Gholami et al 2021 ; Shanavas et al 2020 ; Xu et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model

Kumbalaparambi¹,

Menon²,

Radhakrishnan³

et al. 2022

Environ Sci Pollut Res

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Social media platforms are one of the prominent new-age methods used by public for spreading awareness or drawing attention on an issue or concern. This study demonstrates how the twitter responses of public can be used for qualitative monitoring of air pollution in an urban area. Tweets discussing about air quality in Delhi, India, were extracted during 2019–2020 using a machine learning technique based on self-attention network. These tweets were cleaned, sorted, and classified into 3-class quality viz. poor air quality, good air quality, and noise or neutral tweets. The present study used a multilayer classification model with first layer as an embedding layer and second layer as bi-directional long-short term memory (BiLSTM) layer. A method was then devised for estimating PM 2.5 concentration from the tweets using ‘spaCy’ similarity analysis of classified tweets and data extracted from Continuous Ambient Air Quality Monitoring Stations (CAAQMS) in Delhi for the study period. The accuracy of this estimation was found to be high (80–99%) for extreme air quality conditions (extremely good or severe) and lower during moderate variations in air quality. Application of this methodology depended on perceivable changes in air quality, twitter engagement, and environmental consciousness among public. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-22836-w.

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Air pollution in an urban world: A global view on density, cities and emissions

Cited by 75 publications

References 74 publications

Urban spatial structure and regional smog management for environmental sustainability

Urban spatial structure and regional smog management for environmental sustainability

Regional Collaborative Prediction of Air Pollutants Based on CNN-BiLSTM Model

Assessment of urban air quality from Twitter communication using self-attention network and a multilayer classification model

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