Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.9
Journals
Atmosphere
Special Issues
Air Pollution and Climate Issues in the Coastal Atmosphere of...
share announcement

Air Pollution and Climate Issues in the Coastal Atmosphere of China

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 10971

Special Issue Editors

Prof. Dr. Xiaohong Yao

E-Mail Website
Guest Editor
Key Lab of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao 266100, China
Interests: PM2.5; O3; organics; trend; extreme events; dust; marine emissions; sea-salt aerosol; DMS; NH3; amines
Dr. Jialiang Feng

E-Mail Website
Guest Editor
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
Interests: PM2.5; O3; organics; amines; sea-salt aerosols
Dr. Yujiao Zhu

E-Mail Website
Guest Editor
Environment Research Institute, Shandong University, Qingdao, Shandong 266237, China
Interests: PM2.5; O3; phytoplankton; DMS; cloud condensation nuclei

Special Issue Information

Dear Colleagues,

Benefiting from better nature and economic environments, an increasing population has migrated from inland areas to coastal cities in China during the last several decades and, thus, accelerated the expansion of the latter. An increasing energy consumption, urbanization, ocean exploitation, etc., generate short-term stresses mitigating air pollution and long-term challenges to confront the climate change. Although the air quality in most coastal cities in China is better than that in inland cities, the exceedance of air pollutants in former cities has been continuously reported, to some extent, including some extreme air pollution events. The air pollution reflects a variety of mixed contributions and interactions from natural and anthropogenic emissions, inland and marine emissions, transported and localized sources. The complexity raises the difficulties of improving the air quality in coastal cities from a relatively low level to an even lower level. Moreover, to address climate changes such as carbon peak and neutrality in coastal environments of China, the complexity is as high as that regarding air pollution.   

In recognition of the complexity, the open access journal Atmosphere is hosting a Special Issue to showcase the most recent findings and new directions related to air pollution, climate issues and the links in between of coastal atmospheres in China, encouraging overview papers regarding inter-disciplinary campaigns in coastal atmospheres. Original results related to emissions, field and laboratory experiments, managements and policies, models and review papers are all welcome as contributions

Prof. Dr. Xiaohong Yao
Dr. Jialiang Feng
Dr. Yujiao Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • PM2.5
  • O3
  • VOC
  • SOA
  • sulfate aerosol
  • ammonium nitrate
  • aerosol pH
  • particle number concentration
  • cloud condensation nuclei
  • carbon sink

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 4054 KiB  
Article
Regional Differences in PM2.5 Environmental Efficiency and Its Driving Mechanism in Zhejiang Province, China
by Xuejuan Fang, Bing Gao, Shenghui Cui, Lei Ding, Lihong Wang and Yang Shen
Atmosphere 2023, 14(4), 672; https://doi.org/10.3390/atmos14040672 - 01 Apr 2023
Cited by 1 | Viewed by 1038
Abstract
Improving the digital economy and environmental governance efficiency are important methods for current high-quality economic development. Based on the panel data of 11 cities in Zhejiang, on the eastern coast of China, fine particulate matter smaller than a 2.5 μm (PM2.5) [...] Read more.
Improving the digital economy and environmental governance efficiency are important methods for current high-quality economic development. Based on the panel data of 11 cities in Zhejiang, on the eastern coast of China, fine particulate matter smaller than a 2.5 μm (PM2.5) environmental efficiency (PMEE) was measured by the undesirable output Slack-Based Measure-Data Envelopment Analysis (SBM-DEA) model. The fixed effect regression model, the divergences in the difference model and other empirical methods were obtained to test the driving mechanism of social-economic factors on the PMEE. The results showed that: (1) the concentration of PM2.5 was continually decreasing, and environmental quality experienced a continuous improvement in Zhejiang province in the observation period, although cities such as Hangzhou, Jiaxing and Shaoxing have relatively severe PM2.5 pollution. (2) The total average value of PMEE in Zhejiang was 0.6430 over the observation period, while there was still a lot of room for improvement when compared to the production frontier. Additionally, PMEE in each city showed a fluctuating growth trend. Cities with a higher PMEE were mainly Zhoushan, Hangzhou and Ningbo. (3) The level of the digital economy had a positive role in promoting the PMEE, which was statistically significant. The level of pollution control and technological innovation also had a significantly positive effect. However, the ratio of the industrial output value to the gross domestic product (GDP) presented a negative effect on the PMEE. In the future, it is suggested that the development of the urban digital economy should be accelerated in an all-around way to improve the efficiency of government pollution control and to improve the technical efficiency of PM2.5 via innovative technological progress. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Figure 1

15 pages, 6319 KiB  
Article
Deep Sequence Learning for Prediction of Daily NO2 Concentration in Coastal Cities of Northern China
by Xingbin Jia, Xiang Gong, Xiaohuan Liu, Xianzhi Zhao, He Meng, Quanyue Dong, Guangliang Liu and Huiwang Gao
Atmosphere 2023, 14(3), 467; https://doi.org/10.3390/atmos14030467 - 27 Feb 2023
Cited by 1 | Viewed by 1215
Abstract
Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next [...] Read more.
Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson’s correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Figure 1

15 pages, 6585 KiB  
Article
Impact Analysis of Super Typhoon 2114 ‘Chanthu’ on the Air Quality of Coastal Cities in Southeast China Based on Multi-Source Measurements
by Fei Li, Qiuping Zheng, Yongcheng Jiang, Aiping Xun, Jieru Zhang, Hui Zheng and Hong Wang
Atmosphere 2023, 14(2), 380; https://doi.org/10.3390/atmos14020380 - 15 Feb 2023
Viewed by 1312
Abstract
The northward typhoon configuration along the southeast coast of China (TCN-SEC) is one of the key circulation patterns influencing the coastal cities in southeast China (CCSE). Here, we analyzed the air quality in CCSE during the high-incidence typhoon period from 2019 [...] Read more.
The northward typhoon configuration along the southeast coast of China (TCN-SEC) is one of the key circulation patterns influencing the coastal cities in southeast China (CCSE). Here, we analyzed the air quality in CCSE during the high-incidence typhoon period from 2019 to 2021. Multi-source measurements were carried out to explore the impact of super typhoon 2114 ‘Chanthu’ on the air quality in CCSE. The results showed that the TCN-SEC and its surrounding weather situation had a favorable impact on the increase in pollutant concentration in CCSE, especially on the increase in O3 concentration. From 13 September to 17 September 2021, affected by the cyclonic shear in the south of super typhoon 2114 ‘Chanthu,’ the strong wind near the ground, stable relative humidity, strong precipitation, and the significantly reduced wind speed had a substantial effect on PM10, PM2.5, SO2, and NO2 concentrations. Calm and light air near the ground, weak precipitation, high daily maximum temperatures, and minimum relative humidity may provide favorable meteorological conditions for the accumulation of O3 precursors and photochemical reactions during the day, resulting in the daily peak values of O3 exceeding 160 μg/m3. The evolution of wind, relative humidity, and boundary layer height could play an important role in the variations in PM10 and PM2.5 concentrations by influencing pollutant accumulation or diffusion. It was suggested that the atmospheric structure of horizontal stability and vertical mixing below 1500 m could play a significant role in the accumulation and vertical distribution of ozone. The results highlight the important role of typhoons in the regional environment and provide a scientific basis for further application of multi-source observation data, as well as air pollution control. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Figure 1

15 pages, 2831 KiB  
Article
Investigation of Policy Relevant Background (PRB) Ozone in East Asia
by Yun Fat Lam and Hung Ming Cheung
Atmosphere 2022, 13(5), 723; https://doi.org/10.3390/atmos13050723 - 01 May 2022
Cited by 1 | Viewed by 1929
Abstract
The concept of Policy Relevant Background (PRB) ozone has emerged in recent years to address the air quality baseline on the theoretical limits of air pollution controls. In this study, the influence of Long-range Transport (LRT) of air pollutants from North America and [...] Read more.
The concept of Policy Relevant Background (PRB) ozone has emerged in recent years to address the air quality baseline on the theoretical limits of air pollution controls. In this study, the influence of Long-range Transport (LRT) of air pollutants from North America and the effect of Stratosphere-Troposphere Transport (STT) on PRB ozone was investigated using GEOS-Chem coupled WRF-CMAQ modelling system. Four distinct seasons in 2006 were simulated to understand better the seasonal and geographical impacts of these externalities on PRB ozone over East Asia (EA). Overall, the LRT impact from North America has been found to be ~0.54 ppbv, while the maximum impacts were found at the mountain stations with values of 2.3 ppbv, 3.3 ppbv, 2.3 ppbv, and 3.0 ppbv for January, April, July, and October, respectively. In terms of PRB ozone, the effect of STT has enhanced the surface background ozone by ~3.0 ppbv, with a maximum impact of 7.8 ppbv found in the northeastern part of East Asia (near Korea and Japan). Springtime (i.e., April) has the most vital STT signals caused by relatively cold weather and unstable atmospheric condition resulting from the transition of the monsoon season. The simulated PRB ozone based on the mean values of the maximum daily 8-h average (MDA8) is 53 ppbv for spring (April) and 22 ppbv for summer (July). Up to ~1.0 ppbv and ~2.2 ppbv of MDA8 ozone were attributed to LRT and STT, respectively. Among the selected cities, Beijing and Guangzhou have received the most substantial anthropogenic enhancement in MDA8 ozone in summer, ranging from 40.0 ppbv to 56.0 ppbv. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Figure 1

18 pages, 3834 KiB  
Article
Does Ambient Secondary Conversion or the Prolonged Fast Conversion in Combustion Plumes Cause Severe PM2.5 Air Pollution in China?
by Yanjie Shen, He Meng, Xiaohong Yao, Zhongren Peng, Yele Sun, Jie Zhang, Yang Gao, Limin Feng, Xiaohuan Liu and Huiwang Gao
Atmosphere 2022, 13(5), 673; https://doi.org/10.3390/atmos13050673 - 22 Apr 2022
Cited by 4 | Viewed by 1930
Abstract
The ambient formation of secondary particulate matter (ambient FSPM) is commonly recognized as the major cause of severe PM2.5 air pollution in China. We present observational evidence showing that the ambient FSPM was too weak to yield a detectable contribution to extreme [...] Read more.
The ambient formation of secondary particulate matter (ambient FSPM) is commonly recognized as the major cause of severe PM2.5 air pollution in China. We present observational evidence showing that the ambient FSPM was too weak to yield a detectable contribution to extreme PM2.5 pollution events that swept northern China between 11 and 14 January 2019. Although the Community Multiscale Air Quality (CMAQ) model (v5.2) reasonably reproduced the observations in January 2019, it largely underestimated the concentrations of the PM2.5 during the episode. We propose a novel mechanism, called the “in-fresh-stack-plume non-precipitation-cloud processing of aerosols” followed by the evaporation of semi-volatile components from the aerosols, to generate PM2.5 at extremely high concentrations because of highly concentrated gaseous precursors and large amounts of water droplets in fresh cooling combustion plumes under poor dispersion conditions, low ambient temperature, and high relative humidity. The recorded non-precipitation-cloud processing of the aerosols in fresh stack combustion plumes normally lasts 20–30 s, but it prolongs as long as 2–5 min under cold, humid, and stagnant meteorological conditions and expectedly causes severe PM2.5 pollution events. Regardless of the presence of the natural cloud in the planetary boundary layer during the extreme events, the fast conversion of air pollutants in water droplets and the generation of the PM2.5 through the non-precipitation-cloud processing of aerosols always occur in fresh combustion plumes. The processing of aerosols is detectable using a nano-scan particle sizer assembled on an unmanned aerial vehicle to monitor the particle formation in stack plumes. In-fresh-stack-plume processed aerosols under varying meteorological conditions need to be studied urgently. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Figure 1

16 pages, 4505 KiB  
Article
Exploring the Sensitivity of Visibility to PM2.5 Mass Concentration and Relative Humidity for Different Aerosol Types
by Jiao Wang, Jianhui Wu, Baoshuang Liu, Xiaohuan Liu, Huiwang Gao, Yufen Zhang, Yinchang Feng, Suqin Han and Xiang Gong
Atmosphere 2022, 13(3), 471; https://doi.org/10.3390/atmos13030471 - 14 Mar 2022
Cited by 4 | Viewed by 2394
Abstract
Fine particle (PM2.5) mass concentration and relative humidity (RH) are the primary factors influencing atmospheric visibility. There are some studies focused on the complex, nonlinear relationships among visibility, PM2.5 concentration, and RH. However, the relative contribution of the two factors [...] Read more.
Fine particle (PM2.5) mass concentration and relative humidity (RH) are the primary factors influencing atmospheric visibility. There are some studies focused on the complex, nonlinear relationships among visibility, PM2.5 concentration, and RH. However, the relative contribution of the two factors to visibility degradation, especially for different aerosol types, is difficult to quantify. In this study, the normalized forward sensitivity index method for identifying the dominant factors of visibility was used on the basis of the sensitivity of visibility to PM2.5 and RH changes. The visibility variation per unit of PM2.5 or RH was parameterized by derivation of the visibility multivariate function. The method was verified and evaluated based on 4453 valid hour data records in Tianjin, and visibility was identified as being in the RH-sensitive regime when RH was above 75%. In addition, the influence of aerosol chemical compositions on sensitivity of visibility to PM2.5 and RH changes was discussed by analyzing the characteristics of extinction components ((NH4)2SO4, NH4NO3, organic matter, and elemental carbon) measured in Tianjin, 2015. The result showed that the fitting equation of visibility, PM2.5, and RH, separately for different aerosol types, further improved the accuracy of the parameterization scheme for visibility in most cases. Full article
(This article belongs to the Special Issue Air Pollution and Climate Issues in the Coastal Atmosphere of China)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop