Air Pollution in China (3rd Edition)

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1020

Special Issue Editor


E-Mail Website
Guest Editor
International Center for Ecology, Meteorology, and Environment, School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: environmental geochemistry and health; air pollution; atmospheric particulate matters; bioaerosols; emerging contaminants; nano-plastics; heavy metals; toxicology; risk assessments; climate change and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the Special Issue, titled ‘Air Pollution in China (2nd Edition)’ (https://www.mdpi.com/journal/atmosphere/special_issues/Air_Pollution_in_China_2nd) published in Atmosphere in 2023 and will cover all aspects of Chinese atmospheric-pollution issues.

In China, serious air pollution, caused by human activities and partly natural factors, has been apparent since around the 1990s. It is worth mentioning that local air pollution has greatly improved in the past 5 years, mainly due to progress in institutional and technical measures since the 2010s. However, the trajectory of air pollution in China is changing at present due to the compound event of photochemical and aerosol pollution, and air pollution control has thus entered a new phase.

 This Special Issue, ‘Air Pollution in China (3rd Edition)’ invites submissions of innovative papers that will help with the development of the Chinese atmospheric environment and the implementation of effective air pollution control strategies in the future.

Prof. Dr. Xiao-San Luo
Guest Editor

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

  • air pollution and health in China
  • atmospheric fine particulate matters
  • aerosols
  • bioaerosols
  • micro/nano-plastics
  • ozone
  • emerging contaminants
  • toxicology and risk assessments
  • air pollution and climate change
  • air pollution observation in China
  • remote sensing of air pollution in China
  • numerical simulation of air pollution in China
  • air pollution prediction method in China
  • air quality management and pollution control in China

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

12 pages, 8950 KiB  
Article
Improvement of PM2.5 Forecast in China by Ground-Based Multi-Pollutant Emission Source Inversion in 2022
by Lili Zhu, Xiao Tang, Wenyi Yang, Yao Zhao, Lei Kong, Huangjian Wu, Meng Fan, Chao Yu and Liangfu Chen
Atmosphere 2024, 15(2), 181; https://doi.org/10.3390/atmos15020181 - 31 Jan 2024
Viewed by 748
Abstract
This study employs an ensemble Kalman filter assimilation method to validate and update the pollutant emission inventory to mitigate the impact of uncertainties on the forecasting performance of air quality numerical models. Based on nationwide ground-level pollutant monitoring data in China, the emission [...] Read more.
This study employs an ensemble Kalman filter assimilation method to validate and update the pollutant emission inventory to mitigate the impact of uncertainties on the forecasting performance of air quality numerical models. Based on nationwide ground-level pollutant monitoring data in China, the emission inventory for the entire country was inverted hourly in 2022. The emission rates for PM2.5, CO, NOx, SO2, NMVOCs, BC, and OC updated by the inversion were determined to be 6.6, 702.4, 37.2, 13.4, 40.3, 3, and 18.2 ng/s/m2, respectively. When utilizing the inverted inventory instead of the priori inventory, the average accuracy of all cities’ PM2.5 forecasts was improved by 1.5–4.2%, especially for a 7% increase on polluted days. The improvement was particularly remarkable in the periods of January–March and November–December, with notable increases in the forecast accuracy of 12.5%, 12%, and 6.8% for the Northwest, Northeast, and North China regions, respectively. The concentration values and spatial distribution of PM2.5 both became more reasonable after the update. Significant improvements were particularly observed in the Northwest region, where the forecast accuracy for all preceding days was improved by approximately 15%. Additionally, the underestimated concentration of PM2.5 in the priori inventory compared to the observation value was notably alleviated by the application of the inversion. Full article
(This article belongs to the Special Issue Air Pollution in China (3rd Edition))
Show Figures

Figure 1

Back to TopTop