Advances in Air Quality Monitoring

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 10814

Special Issue Editors

Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
Interests: air quality monitoring; machine learning; air quality and mobility; Gaussian process

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Guest Editor
Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S10 2TN, UK
Interests: machine learning; computational intelligence; statistical signal processing; robot SLAM; navigation and autonomous systems
Special Issues, Collections and Topics in MDPI journals
Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
Interests: air quality assessment; atmospheric composition; DOAS; satellite and ground based remote sensing; aerosol monitoring; low-cost sensors
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Guest Editor
Institute of Environmental Sciences and Engineering, National University of Science and Technology, Islamabad 24090, Pakistan
Interests: air quality assessment; atmospheric composition; DOAS; satellite and ground based remote sensing; aerosol monitoring; low-cost sensors
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China
Interests: urban micro-climate modelling; computational fluid dynamics (CFD); wind comfort; thermal comfort; pollutant dispersion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The World Health Organization (WHO) revised the air quality guidelines on September 22, 2021, and strengthened the standards for pollutants such as PM2.5, PM10, NO2 and CO in comparison with the 2005 guidelines. While ‘nearly 80% of PM2.5-related preventable deaths could be avoided if the world met the new guidelines’, 50% of European cities and 38% of US cities failed to meet the air quality guidelines for PM2.5. To meet these guidelines, China faces the challenge of reducing PM2.5 exposure to its citizens to one-third of the current level. Similar issues exist in other parts of the world.

To improve human wellbeing, the past decade has seen efforts from both academia and industry in large-scale spatial–temporal air quality data accumulation and the development of advanced machine learning models to process data for air quality monitoring and forecasting. However, works that comprehensively assess model performance are still lacking, especially those operating in the presence of data and model parameter uncertainties, etc.

The aim of this Special Issue, hosted by the open access journal Atmosphere, is to promote recent advances in air quality monitoring and forecasting techniques. The topics cover a range of research topics, including but not limited to:

  • air quality models – for indoor and outdoor environments;
  • high-resolution sensors for monitoring and modelling air quality data;
  • methods for prediction and assessment of air quality;
  • scalable and distributed machine learning models in large-scale spatial–temporal air quality forecasting;
  • machine learning models for air quality data and mobility data association;
  • machine learning models for air quality cleaning and outlier detection;
  • machine learning solutions for low-cost air quality sensors, etc.;
  • machine learning solutions for urban and rural area air quality monitoring;
  • other related sub-areas.

Dr. Peng Wang
Prof. Dr. Lyudmila Mihaylova
Dr. Khan Alam
Prof. Dr. Muhammad Fahim Khokhar
Prof. Dr. Liangxiu Han
Dr. Yaxing Du
Guest Editors

Manuscript Submission Information

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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 quality forecasting
  • machine learning for air quality
  • air quality modeling
  • air quality assessment
  • urban air quality
  • indoor and outdoor air quality
  • aerosol monitoring
  • distributed system for air quality
  • sensors for air quality
  • air quality model assessment

Published Papers (5 papers)

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Research

21 pages, 4182 KiB  
Article
Impact of the COVID-19 Pandemic on the 2020 Diurnal Temperature Range (DTR) in the Contiguous USA
by Walid Ahmed, Lydia Marini Hoffmann, Talib Al-Hasani and Rafael M. Santos
Atmosphere 2022, 13(12), 2031; https://doi.org/10.3390/atmos13122031 - 3 Dec 2022
Cited by 1 | Viewed by 1299
Abstract
Following the emergence of COVID-19, nations around the world implemented effective restrictions that limited people’s movements and economic activity, which reportedly led to environmental improvements. The lowering of air emissions is one environmental indicator that has been connected to the pandemic. The diurnal [...] Read more.
Following the emergence of COVID-19, nations around the world implemented effective restrictions that limited people’s movements and economic activity, which reportedly led to environmental improvements. The lowering of air emissions is one environmental indicator that has been connected to the pandemic. The diurnal temperature range (DTR) is one environmental indicator that has been linked to air pollution. In this study, it was hypothesized that because of the pandemic restrictions and slowdowns, the DTR in 2020 for a country that implemented major restrictive measures in reaction to the pandemic would be higher than in previous years, despite or in addition to background climatic forcings. Based on information from weather stations in the contiguous United States of America (USA), the DTR for the year 2020 was compared to the five years before it as a test of this hypothesis. It was verified that the annual mean DTR of 2020 was higher than the three years prior (2017–2019), but lower than the DTR of 2015 and 2016. Compared to historical trends (since 1911), the DTR change in 2020 is within past mean DTR variations that occurred over approx. 12-year cycles, linked to sunspot activity (Schwabe solar cycle). Moreover, climatic effects such as El Niño, La Niña and the prolonged trend of global warming reduce the confidence in the perceived effect of the pandemic. To determine if or how anthropogenic and environmental factors can magnify the impact of the COVID-19 restrictions on the regional mean DTR, five other parameters (annual snowfall quantities, gross domestic product per capita, population density, latitude (northern/southern), and longitude (coastal/inner)) were also examined against changes in DTR from 2015 to 2020. This analysis pointed to the environmental and industrial factors being more strongly correlated with short-term climate changes than societal factors and geographical location. Full article
(This article belongs to the Special Issue Advances in Air Quality Monitoring)
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16 pages, 5361 KiB  
Article
Atmospheric Aerosol Outbreak over Nicosia, Cyprus, in April 2019: Case Study
by Yuliia Yukhymchuk, Gennadi Milinevsky, Ivan Syniavskyi, Ioana Popovici, Florin Unga, Jean Sciare, Franco Marenco, Michael Pikridas and Philippe Goloub
Atmosphere 2022, 13(12), 1997; https://doi.org/10.3390/atmos13121997 - 29 Nov 2022
Viewed by 2277
Abstract
This paper aims to analyze the significant changes in atmospheric aerosol characteristics during the extreme aerosol outbreak event in April 2019 in the atmosphere over Cyprus in the Eastern Mediterranean. We study the aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo, refractive [...] Read more.
This paper aims to analyze the significant changes in atmospheric aerosol characteristics during the extreme aerosol outbreak event in April 2019 in the atmosphere over Cyprus in the Eastern Mediterranean. We study the aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo, refractive index, size, and vertical distribution of aerosol particles during the event of intense aerosol advection in detail. For this purpose, we used the ground-based observations of the sun-photometer AERONET Nicosia station, lidar measurements, and back trajectories of air movements calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT). To compare with background aerosol load conditions during the year, the available data of AOD and AE were used from the observations at the Nicosia AERONET site in the 2015–2022 period. On 23–25 April 2019, strong aerosol advection over Nicosia was detected according to lidar and sun-photometer observations. On 25 April 2019, the day with the largest aerosol contamination, the AOD value exceeded 0.9 at λ = 500 nm. Analysis of the optical and microphysical characteristics during the extreme event supported that the aerosol advection consists of mainly Saharan dust particles. This assumption was confirmed by the AOD versus AE variations, single-scattering albedo, refractive index, and size distribution retrievals, as well as lidar data and HYSPLIT backward trajectories, where air masses containing dust particles came mostly from North Africa. The analysis shows that the April 2019 event was one of the strongest aerosol surges that regularly take place in springtime in the atmosphere over Cyprus. The noticeable reduction in the effective radiative forcing caused by increasing aerosol amount during the aerosol dust outbreak was revealed. Full article
(This article belongs to the Special Issue Advances in Air Quality Monitoring)
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19 pages, 3457 KiB  
Article
Air Quality Assessment along China-Pakistan Economic Corridor at the Confluence of Himalaya-Karakoram-Hindukush
by Maqbool Ahmad, Khadim Hussain, Jawad Nasir, Zhongwei Huang, Khan Alam, Samreen Liaquat, Peng Wang, Waqar Hussain, Lyudmila Mihaylova, Ajaz Ali and Suhaib Bin Farhan
Atmosphere 2022, 13(12), 1994; https://doi.org/10.3390/atmos13121994 - 28 Nov 2022
Cited by 1 | Viewed by 1980
Abstract
Recently, analyses of the air quality in Pakistan have received significant interest, especially regarding the impact of air pollutant concentrations on human health. The Atlas of Baseline Environmental Profiling along the China-Pakistan Economic Corridor (CPEC) at five locations in Gilgit-Baltistan (GB) is a [...] Read more.
Recently, analyses of the air quality in Pakistan have received significant interest, especially regarding the impact of air pollutant concentrations on human health. The Atlas of Baseline Environmental Profiling along the China-Pakistan Economic Corridor (CPEC) at five locations in Gilgit-Baltistan (GB) is a major landmark in this regard due to the presence of massive glaciers in the region, which are considered as water reserves for the country. Using various statistical measurements, the air quality was analyzed at the studied geographic locations. Further, air quality was evaluated based on air pollutant data acquired from ambient air monitoring laboratories. For example, 24 h concentrations of particulate matter (PM2.5) were found to range from 25.4 to 60.1 µg/m3, with peaks in the winter season at Gilgit. It was found that PM2.5 values were 1.7 and 1.3 times greater than National Environmental Quality Standards (NEQS) standards only at Gilgit and Chilas, respectively, and 1.5 to 4 times greater than the World Health Organization (WHO) standards at all locations. Similarly, PM2.5 concentrations were found to range from 31.4 to 63.9 µg/m3, peaking at Chilas in summer 2020. The observed values were 1.1 to 1.8 times and 2 to 4.2 times greater than the NEQS and WHO standards, respectively, at all locations. In addition, the average peaks of black carbon (BC) were measured at Gilgit, both in winter (16.21 µg/m3) and summer (7.83 µg/m3). These elevated levels could be attributed to the use of heavy diesel vehicles, various road activities and different meteorological conditions. Pollutants such as carbon monoxide (CO), sulfur dioxide (SO2), nitrogen oxides (NOX) and ozone (O3) were found to be within NEQS and WHO limits. Based on air quality metrics, the effect of PM2.5 on air quality was found to be moderate in Sost, Hunza and Jaglot, while it was at unhealthy levels at Gilgit and Chilas in the winter of 2019; moderate levels were observed at Sost while unhealthy levels were detected at the remaining locations in the summer of 2020. There are no specific guidelines for BC. However, it is associated with PM2.5, which was found to be a major pollutant at all locations. The concentrations of CO, SO2 and O3 were found to be at safe levels at all locations. The major fraction of air masses is received either locally or from transboundary emissions. This study demonstrates that PM2.5 and BC are the major and prevailing air pollutants within the study region, while other air pollutants were found to be within the permissible limits of the WHO and NEQS. Full article
(This article belongs to the Special Issue Advances in Air Quality Monitoring)
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16 pages, 6079 KiB  
Article
Spatio-Temporal Changes in Air Quality of the Urban Area of Chongqing from 2015 to 2021 Based on a Missing-Data-Filled Dataset
by Huayu Zhang, Yong Nie, Qian Deng, Yaqin Liu, Qiyuan Lyu and Bo Zhang
Atmosphere 2022, 13(9), 1473; https://doi.org/10.3390/atmos13091473 - 10 Sep 2022
Cited by 4 | Viewed by 1839
Abstract
Air pollution is one of the severe environmental issues in Chongqing. Many measures made by the government for improving air quality have been put into use these past few years, while the influence of these measures remains unknown. This study analyzed the changes [...] Read more.
Air pollution is one of the severe environmental issues in Chongqing. Many measures made by the government for improving air quality have been put into use these past few years, while the influence of these measures remains unknown. This study analyzed the changes in the air quality of the urban area of Chongqing between 2015 and 2021 using a complete in situ observation dataset that all missing data were filled by the interpolation of a low-rank tensor completion model with truncate nuclear norm minimization (LRTC-TNN). The results include: (1) the LRTC-TNN model robustly performs to reconstruct missing data of pollutant concentrations with an R2 of 0.93 and an RMSE of 7.78; (2) the air quality index (AQI) decreases by 15.96%, and the total polluted days decrease by 21.05% from 2015 to 2021, showing an obvious promotion in air quality; and (3) the changing air quality is attributed to decreasing concentrations of PM2.5 (34.10%), PM10 (25.03%), and NO2 (5.53%) from 2015 to 2021, whereas an increasing concentration of O3 (10.49%) is observed. The processing method for missing data, intact AQI datasets, and analysis of changes are beneficial to policy-making for environmental improvement and fill the gap in the field of data interpolation for air quality datasets in mountainous areas. Full article
(This article belongs to the Special Issue Advances in Air Quality Monitoring)
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16 pages, 12064 KiB  
Article
Spatiotemporal Variation in Air Pollution Characteristics and Influencing Factors in Ulaanbaatar from 2016 to 2019
by Suriya, Narantsogt Natsagdorj, Aorigele, Haijun Zhou and Sachurila
Atmosphere 2022, 13(6), 990; https://doi.org/10.3390/atmos13060990 - 20 Jun 2022
Cited by 4 | Viewed by 2143
Abstract
Ambient air pollution is a global environmental issue that affects human health. Ulaanbaatar (UB), the capital of Mongolia, is one of the most polluted cities in the world, and it is of great importance to study the temporal and spatial changes in air [...] Read more.
Ambient air pollution is a global environmental issue that affects human health. Ulaanbaatar (UB), the capital of Mongolia, is one of the most polluted cities in the world, and it is of great importance to study the temporal and spatial changes in air pollution in this city, along with their influencing factors. To understand the characteristics of atmospheric pollutants in UB, the contents of PM10, PM2.5, SO2, NO2, CO, and O3, as well as their influencing factors, were analyzed from data obtained from automatic air quality monitoring stations. These analyses yielded six major findings: (1) From 2016 to 2019, there was a total of 883 pollution days, and PM2.5 and PM10 were the primary pollutants on 553 and 351 of these days, respectively. The air pollution was dominated by PM10 in spring and summer, affected by both PM2.5 and PM10 in autumn, and dominated by PM2.5 in winter. (2) Compared with 2016, the number of days with good air quality in UB in 2019 increased by 45%, and the number of days with unhealthy or worse levels of pollution decreased by 56%, indicating that the air quality improved year by year. (3) From 2016 to 2019, the annual average PM2.5/PM10 ratio dropped from 0.55 to 0.45, and the proportion of PM2.5 in particulate matter decreased year by year. The PM concentration and PM2.5/PM10 ratio were highest in winter and lowest in summer. When comparing the four-season averages, the average PM2.5 concentration decreased by 89% from its highest level, and the PM10 concentration decreased by 67%, indicating stronger seasonal differences in PM2.5 than in PM10. (4) The hourly changes in PM concentration showed a bimodal pattern, exhibiting a decrease during the day and a slight increase in the afternoon due to temperature inversion, so the PM2.5/PM10 ratio increased at night in all four seasons. The PM concentration during the heating season was significantly higher than that in the non-heating season, indicating that coal-fired heating was the main cause of air pollution in UB. (5) Sand dust and soot were the two main types of pollution in UB. (6) Correlation analysis and linear fitting analysis showed that PM2.5 and PM10 caused by coal-firing had an important impact on air quality in UB. Coal combustion and vehicle emissions with SO2, NO2, and CO as factors made large contributions to PM2.5. Full article
(This article belongs to the Special Issue Advances in Air Quality Monitoring)
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