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Atmosphere
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New Insights in Air Quality Assessment: Forecasting and Monitoring
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New Insights in Air Quality Assessment: Forecasting and Monitoring

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

Deadline for manuscript submissions: 27 June 2025 | Viewed by 10479

Special Issue Editors

Dr. Qingyan Fu

E-Mail Website
Guest Editor
Shanghai Academy of Environmental Sciences, Shanghai 200233, China
Interests: atmospheric environment monitoring; forecast of ambient air quality; information construction of environmental monitoring, emission inventory and simulation of air pollutants, emission verification of industrial areas; emission monitoring of pollution sources and research on composite air pollution
Dr. Jianjun Li

E-Mail Website
Guest Editor
China National Environmental Monitoring Center, Beijing 100012, China
Interests: air quality monitoring; air quality forecast; environmental monitoring

Special Issue Information

Dear Colleagues,

Air quality monitoring is an important means with which to evaluate ambient air status and human health exposure, while air quality forecasting is used to predict the change trend in air quality in the future. Air quality forecasting is usually based on historical data and monitoring data, using statistical methods, numerical models, artificial intelligence algorithms, expert experience comprehensive judgment, etc., which can be divided into short- and medium-/long-term prediction. Short-term forecasts are usually based on weather forecasts and air quality models, while medium-/long-term forecasts take into account more factors, such as seasonal changes, economic development, changes in pollutant emissions, and even climate change uncertainties. In addition, according to the results of air quality forecasting, different countries and regions set up different air pollution warnings. Early warnings can help the public or government departments to take appropriate measures to protect themselves or reduce the impact of pollution, such as reducing outdoor activities and limiting traffic emissions, etc. In recent years, with the rapid development of air quality monitoring and forecasting technology, the guarantee of major activities and the continuous improvement of air quality have gradually embarked on the road of fine management and regulation.

However, different from air quality monitoring and evaluation, different countries and regions in the world have different forecasting methods, time cycles, and evaluation methods. Being complex and important work, information exchange and standard rules of air quality forecasting are particularly important. The purpose of this Special Issue is to promote the continuous improvement of ambient air quality and the protection of human health in countries around the world by sharing and exchanging the latest ambient air quality monitoring technology, analyses of pollution causes, and practices of forecasting and warning.

Dr. Qingyan Fu
Dr. Jianjun Li
Guest Editors

Manuscript Submission Information

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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 monitoring
  • forecasting
  • emission inventory
  • numerical model
  • pollution warnings
  • O3
  • PM2.5

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Published Papers (7 papers)

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Research

24 pages, 22425 KiB  
Article
Atmospheric Black Carbon Evaluation in Two Sites of San Luis Potosí City During the Years 2018–2020
by Valter Barrera, Cristian Guerrero, Guadalupe Galindo, Dara Salcedo, Andrés Ruiz and Carlos Contreras
Atmosphere 2025, 16(1), 65; https://doi.org/10.3390/atmos16010065 - 9 Jan 2025
Viewed by 406
Abstract
Nevertheless, there is a lot to know about air pollutants in Mexico’s largest cities, like San Luis Potosi City, which is one of the 12 most crowded cities and is expected to grow in the next years; however, there is little information about [...] Read more.
Nevertheless, there is a lot to know about air pollutants in Mexico’s largest cities, like San Luis Potosi City, which is one of the 12 most crowded cities and is expected to grow in the next years; however, there is little information about air pollutant levels mainly particulate matter in their regulated size fractions (PM10 or PM2.5), and its main component of the Organic fraction: Black Carbon (BC), which is especially important because of its chemical properties and their effects on human health, air pollution, and climate change. This work presents a one-year BC monitoring in the northern part of the city (2018–2019) and another one-year BC monitoring in the southern area (2019–2020) during the health contingency situation due to the SARX-CoV-2 virus to obtain direct equivalent black carbon (eBC) concentrations and their main fractions related to fossil fuel and biomass burning using aethalometer AE-33, as well as other air pollutants concentrations measured at the same periods by the governmental local monitoring network (SEGAM). At the North, BC mass annual average concentration was (1.11 µg m−3), divided into seasonal stations, the cold season was the highest with (1.44 µg m−3), followed by the dry season (1.23 µg m−3), rainy season (0.94 µg m−3) and finally warm dry season (0.83 µg m−3). In the south, BC annual average concentration was (1.96 µg m−3); divided into seasons, the highest was the dry season with (2.73 µg m−3), followed by the cold season (2.37 µg m−3), dry warm season (1.61 µg m−3) and the rainy season (1.28 µg m−3). One of the main findings was the dominance of annual mean concentrations of BC originating from fossil fuels (BCff) on the north site in the city was 0.97 and on the south site (BCff) was 0.91 due to some forest fires during the monitoring period. This study presented information from two zones of a growing city in Mexico to generate new air pollutant indicators to have a better understanding of pollutant interactions in the city, to decrease the emission precursor sources, and reduce the health risks in the population. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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13 pages, 9219 KiB  
Article
Exploring How Aerosol Optical Depth Varies in the Yellow River Basin and Its Urban Agglomerations by Decade
by Yinan Zhao, Qingxin Tang, Zhenting Hu, Quanzhou Yu and Tianquan Liang
Atmosphere 2024, 15(12), 1466; https://doi.org/10.3390/atmos15121466 - 8 Dec 2024
Viewed by 500
Abstract
In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) [...] Read more.
In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) From 2011 to 2020, the AOD value of the YRB showed a declining trend, with 96.011% of the zones experiencing a decrease in AOD. The spatial distribution of AOD displayed a pattern of high in the east, low in the west, high in the south, and low in the north. The rate of decline showed a distribution pattern of fast in the southeast and slow in the northwest. (2) The AOD in the YRB showed similar characteristics in different seasons: the south and east were consistently higher than the north and west. The seasonal AOD values in the YRB showed the following pattern: summer > spring > autumn > winter. The AOD values of urban agglomeration were basically larger in spring and summer. (3) The SDE and mean center of the yearly AOD were located in the southeast and Shanxi Province, with the movement from southeast to northwest. It can be divided into three stages based on the movement trajectory: northeast–southwest round-trip movement (2011–2014), one-way movement to the northwest (2014–2018), and southeast–northwest round-trip movement (2018–2020). Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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25 pages, 8614 KiB  
Article
Comparative Analysis of Multiple Deep Learning Models for Forecasting Monthly Ambient PM2.5 Concentrations: A Case Study in Dezhou City, China
by Zhenfang He and Qingchun Guo
Atmosphere 2024, 15(12), 1432; https://doi.org/10.3390/atmos15121432 - 28 Nov 2024
Viewed by 753
Abstract
Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. [...] Read more.
Ambient air pollution affects human health, vegetative growth and sustainable socio-economic development. Therefore, air pollution data in Dezhou City in China are collected from January 2014 to December 2023, and multiple deep learning models are used to forecast air pollution PM2.5 concentrations. The ability of the multiple models is evaluated and compared with observed data using various statistical parameters. Although all eight deep learning models can accomplish PM2.5 forecasting assignments, the precision accuracy of the CNN-GRU-LSTM forecasting method is 34.28% higher than that of the ANN forecasting method. The result shows that CNN-GRU-LSTM has the best forecasting performance compared to the other seven models, achieving an R (correlation coefficient) of 0.9686 and an RMSE (root mean square error) of 4.6491 μg/m3. The RMSE values of CNN, GRU and LSTM models are 57.00%, 35.98% and 32.78% higher than that of the CNN-GRU-LSTM method, respectively. The forecasting results reveal that the CNN-GRU-LSTM predictor remarkably improves the performances of benchmark CNN, GRU and LSTM models in overall forecasting. This research method provides a new perspective for predictive forecasting of ambient air pollution PM2.5 concentrations. The research results of the predictive model provide a scientific basis for air pollution prevention and control. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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13 pages, 10498 KiB  
Article
Nocturnal Ozone Enhancement Induced by Sea-Land Breezes During Summertime in Northern Coastal City Qingdao, China
by He Meng, Jiahong Liu, Lu Wang, Laiyuan Shi and Jianjun Li
Atmosphere 2024, 15(11), 1350; https://doi.org/10.3390/atmos15111350 - 10 Nov 2024
Viewed by 747
Abstract
This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O3) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air [...] Read more.
This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O3) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air pollutants and meteorological factors during a typical sea–land breezes event from 22 to 23 July 2022. A coherent Doppler lidar (CDL) system was employed to continuously detect three-dimensional wind fields. The results revealed that nocturnal ozone levels were enhanced by a conversion of sea–land breezes. Initially, the prevailing northerly land breeze transported high concentrations of O3 and other air pollutants from downtown to the Yellow Sea. As the sea breeze developed in the afternoon, the sea breeze front advanced northward, resulting in a flow of high O3 concentrations back into inland areas. This penetration of the sea breeze front led to a notable spike in O3 concentrations between 16:00 on 22 July and 02:00 on 23 July across downtown areas, with an average increase of over 70 μg/m3 within 10 min. Notably, a time lag in peak O3 concentration was observed with southern downtown areas peaking before northern rural areas. During this period, combined pollution of O3 and PM2.5 was also observed. These findings indicated that the nighttime increase in O3 concentrations, coupled with enhanced atmospheric oxidation, would likely promote the secondary conversion of gaseous precursors into PM2.5. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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17 pages, 5572 KiB  
Article
An Ozone Episode in the Urban Agglomerations along the Yangtze River in Jiangsu Province: Pollution Characteristics and Source Apportionment
by Zhe Cai, Derong Zhou, Jianqiao Yu, Sheng Zhong, Longfei Zheng, Zijun Luo, Zhiwei Tang and Fei Jiang
Atmosphere 2024, 15(8), 942; https://doi.org/10.3390/atmos15080942 - 6 Aug 2024
Viewed by 845
Abstract
A severe ozone episode occurred in cities along the Yangtze River of Jiangsu Province (UAYRJS) from 6 to 8 September 2022, with daily maximum 8-h average ozone concentrations in the range of 65.8–119 ppb, peaking in Nanjing on 7 September. We used the [...] Read more.
A severe ozone episode occurred in cities along the Yangtze River of Jiangsu Province (UAYRJS) from 6 to 8 September 2022, with daily maximum 8-h average ozone concentrations in the range of 65.8–119 ppb, peaking in Nanjing on 7 September. We used the air quality model WRF-CMAQ-ISAM and the Lagrange trajectory model HYSPLIT to quantify the ozone contribution of each region and analyze the causes and regional transmission pathways of ozone pollution in the UAYRJS. Based on simulated emissions, we also estimated the contribution of biogenic volatile organic compounds. We found that weather has a negative impact on pollution, and ozone pollution tracks the movement of the Western Pacific Subtropical High. UAYRJS was affected by oceanic pollution, and there was a mutual influence among the area’s cities. On 6 September, the ozone in UAYRJS was mostly locally generated (50–98%); on 7 September, it was dominated by extra-regional transport (50–80%). Isoprene concentrations in UAYRJS increased by 0.03–0.1 ppb on 6 and 7 September compared with 5 September. Sensitivity testing showed that the hourly ozone concentration increased by 0.1–27.8 ppb (7.6–19.1%) under the influence of biogenic emissions. The results provide a scientific basis for future ozone control measures. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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23 pages, 6024 KiB  
Article
Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling
by Qian Liu, Bingyan Cui and Zhen Liu
Atmosphere 2024, 15(5), 553; https://doi.org/10.3390/atmos15050553 - 30 Apr 2024
Cited by 17 | Viewed by 3945
Abstract
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset [...] Read more.
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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27 pages, 13221 KiB  
Article
Machine Learning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features
by Reem K. Alshammari, Omer Alrwais and Mehmet Sabih Aksoy
Atmosphere 2024, 15(5), 520; https://doi.org/10.3390/atmos15050520 - 24 Apr 2024
Cited by 3 | Viewed by 2159
Abstract
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple [...] Read more.
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional network (TCN). This study highlights the effectiveness of LSTM and TCN models in capturing the complex temporal dynamics of dust storms and demonstrates that they outperform traditional methods, as evidenced by their lower mean absolute error (MAE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the LSTM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64, and R2 score of 0.64. In Jeddah, the LSTM model also exhibited an MAE of 2.48 and an RMSE of 2.96. This research shows the potential of using deep learning models to improve the accuracy and reliability of dust storm frequency forecasts. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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