Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Luo, Hao; Han, Yong; Cheng, Xiao; Lu, Chen; Wu, Yonghua

doi:10.3390/atmos12010043

Cited by 11 publications

(6 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, to enhance the industrial linkage between various regions and develop economic clusters, urban agglomerations have gradually become an essential form of regional economic development in China. However, the spatial clustering effect of air pollution is also becoming more pronounced, and this is more apparent in urban agglomerations [41]. Simultaneously, the causes of air pollution exhibit heterogeneity across different cities in China [1], creating a complex environment for regional air quality prediction.…”

Section: Research Areamentioning

confidence: 99%

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Zhang,

Duan,

Sun

et al. 2023

Atmosphere

View full text Add to dashboard Cite

Air pollution is a hotspot of wide concern in Chinese cities. With the worsening of air pollution, urban agglomerations face an increasingly complex environment for air quality monitoring, hindering sustainable and high-quality development in China. More effective methods for predicting air quality are urgently needed. In this study, we employed seven single models and ensemble learning algorithms and constructed a hybrid learning algorithm, the LSTM-SVR model, totaling eight machine learning algorithms, to predict the Air Quality Index in six major urban agglomerations in China. We comprehensively compared the predictive performance of the eight algorithmic models in different urban agglomerations. The results reveal that, in areas with higher levels of air pollution, the situation for model prediction is more complicated, leading to a decline in predictive accuracy. The constructed hybrid model LSTM-SVR demonstrated the best predictive performance, followed by the ensemble model RF, both of which effectively enhanced the predictive accuracy in heavily polluted areas. Overall, the predictive performance of the hybrid and ensemble models is superior to that of the single-model prediction methods. This study provides AI technological support for air quality prediction in various regions and offers a more comprehensive discussion of the performance differences between different types of algorithms, contributing to the practical application of air pollution control.

show abstract

Section: Research Areamentioning

confidence: 99%

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Zhang,

Duan,

Sun

et al. 2023

Atmosphere

View full text Add to dashboard Cite

show abstract

“…Theoretical studies have viewed that TCs are closed steady-state thermodynamic cycles receiving heat and moisture from the ocean (Emanuel, 1986). Oceanic evaporation within the TC area is considered to increase with the wind speed (Makarieva et al, 2017).…”

Section: Meteorological Fields and Profilesmentioning

confidence: 99%

Impacts of the Saharan air layer on the physical properties of the Atlantic tropical cyclone cloud systems: 2003–2019

Luo

Han

2021

Atmos. Chem. Phys.

Self Cite

View full text Add to dashboard Cite

Abstract. It is generally known that the tropical cyclone (TC) cloud systems (TCCSs) in the North Atlantic region frequently occur during boreal summer, while the Saharan dust outbreaks occur concurrently. The Sahara air layer (SAL), an elevated layer containing Saharan dry air and mineral dust, has crucial impacts on the generation and evolution of TCs. However, the effects of SAL on the physical (macro and micro) characteristics of the Atlantic TCCSs have not been well constrained, and the interaction mechanisms between them still need further investigation. In this study, our primary interest is to distinguish the various effects of SAL on different intensities of TCs and further find out the probable causes of the varied feedback mechanisms. Therefore, we attempt to identify whether and how the effects of the SAL play a positive or negative role on the TCCSs and to draw a qualitative conclusion on how SAL affects the various intensities of the TCs. This paper focuses on the 70 TC samples from July to September in the years of 2003–2019 to investigate the physical effects of SAL on three intensities of TCs, i.e., the tropical depression (TD), tropical storm (TS), and hurricane (HU). The results show that SAL has a positive impact on the macro properties of HU but significantly suppresses the TD. It appears that the SAL attributes little to the variation of the ice cloud effective radius (CERi) for TS, whereas CERi changes significantly and differentially for TD and HU. When affected by SAL, the probability density function (PDF) curve of CERi generally shifts to the smaller value for TD, whereas the PDF curve becomes flatter for HU. Our analysis indicates that the various responses of TCCSs to SAL are determined by the combined effects of dry air masses, the dust aerosols as ice nuclei, and the thermodynamic and moisture conditions. Based on the observation data analysis, a concept scheme description has been concluded to deepen our recognition of the effects of SAL on the TCCSs.

show abstract

“…The accelerating process of urbanization and the increasing demand for transportation have led to the deterioration of urban air quality, emerging as a critical urban environmental issue [1]. Apart from adversely affecting pedestrians outdoors, pollutants infiltrate indoor spaces through various pathways such as doors, windows, ventilation ducts, and building cracks, posing risks to indoor residents [2].…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of the Effects of Different Window-Opening Strategies on the Indoor Pollutant Dispersion in Street-Facing Buildings

Wu,

Ouyang,

Shi

et al. 2024

Atmosphere

View full text Add to dashboard Cite

The idling of automobiles at street intersections can lead to pollutant accumulation which impacts the health of residents in street-facing buildings. Previous research focused on pollutant dispersion within street canyons and did not consider the coupling of indoor and outdoor pollutants. This paper employs the computational fluid dynamics (CFD) method to simulate the dispersion characteristics of vehicle emission pollutants in street canyons, primarily investigating the indoor and outdoor pollutant dispersion patterns under various window opening configurations (single-sided ventilation, corner ventilation, and different positions of the glass under corner ventilation). Additionally, the study considers the impacts of the aspect ratio and ambient wind speed. Studies have shown that corner ventilation is effective in reducing indoor pollutant levels. When the two window glass positions are far away from the center of the intersection, the average CO mass fraction in the single-sided ventilation room is reduced by 87.1%. The average indoor CO mass fraction on the leeward side decreases with the increasing wind speed and aspect ratio. At a wind speed of 8 m/s, the average indoor CO mass fraction on the leeward side decreases to 2.45 × 10−8. At an aspect ratio of 2, the indoor CO mass fraction on the leeward side decreases with increasing floors before stabilizing at approximately 4.77 × 10−9. This study suggests optimal window opening strategies to reduce indoor pollutant levels in street-facing buildings at street intersections, offering guidance to indoor residents on window ventilation practices.

show abstract

Spatiotemporal Variations in Particulate Matter and Air Quality over China: National, Regional and Urban Scales

Cited by 11 publications

References 63 publications

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Air Quality Index Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Single Machine Learning Model, Ensemble Model, and Hybrid Model

Impacts of the Saharan air layer on the physical properties of the Atlantic tropical cyclone cloud systems: 2003–2019

Numerical Analysis of the Effects of Different Window-Opening Strategies on the Indoor Pollutant Dispersion in Street-Facing Buildings

Contact Info

Product

Resources

About