Topic Editors

Dipartimento per lo Sviluppo Sostenibile e la Transizione Ecologica, Università del Piemonte Orientale, Vercelli, Italy
Department of Chemistry, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia

Accessing and Analyzing Air Quality and Atmospheric Environment

Abstract submission deadline
closed (31 December 2023)
Manuscript submission deadline
closed (31 March 2024)
Viewed by
50421

Topic Information

Dear Colleagues,

Global air pollution continues to threaten public health to varying degrees and in varying forms, and air quality has become one of the largest environmental issues in modern times. There are many reports of threats to the safety of people's lives and property caused by harsh atmospheric environment and poor air quality, phenomena which have attracted global attention.

Aerosol and gaseous pollutants have fundamental impacts on the Earth's environment, as well as on human health and climate change. Predicting their high concentrations using numerical models is a challenge. Advances in atmospheric numerical models and increased computer power have enabled numerical weather prediction (NWP) and climate research to more effectively protect humans from adverse weather and air quality, as well as the impacts of climate change. Despite these advances, numerical models suffer from various errors related to numerical methods, resolution, physical parameterization, and input data. There is room to further improve predictability through the development of enhanced modeling and data assimilation techniques, operating with a variety of input data and higher resolution. Two-way coupling of the atmosphere with hydrological, oceanic, wave, dust and fire models also has great potential to achieve this goal.

Air quality and atmospheric environment affect all aspects of society. We need to pay attention to the atmospheric environment and monitor air quality. Air pollution monitoring, forecasting and mitigation should be a joint work, carried out with global partners. In light of the global, transcontinental and rapidly changing chemical and emission characteristics of the world's air quality, we invite the submission of research on air-quality monitoring, forecasting, observation and modeling of the atmospheric environment, air pollutants and their impact on human health, as well as the impact of observational and/or predictive models on atmospheric chemistry research.

Prof. Dr. Enrico Ferrero
Dr. Elvira Kovač-Andrić
Topic Editors

Keywords

  • air-quality and atmospheric composition modeling
  • atmospheric chemical observation and monitoring
  • air-quality forecasting
  • air-pollutant-related epidemiology and exposure studies
  • climate impact on air-quality forecasting
  • aerosol data assimilation and forecasting
  • data assimilation with multi-observations
  • estimation and optimization of emission sources
  • development of atmospheric chemical models or air-quality models
  • two-way coupling of atmospheric numerical models with hydrological, ocean, wave, dust and fire models aiming to improve the representation of the atmospheric processes. thermal internal boundary layer air pollution

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Air
air
- - 2023 15.0 days * CHF 1000
Atmosphere
atmosphere
2.9 4.1 2010 17.7 Days CHF 2400
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400
Pollutants
pollutants
- - 2021 21.7 Days CHF 1000

* Median value for all MDPI journals in the second half of 2023.


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Published Papers (44 papers)

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16 pages, 2222 KiB  
Technical Note
Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data
by Alessandro Fania, Alfonso Monaco, Ester Pantaleo, Tommaso Maggipinto, Loredana Bellantuono, Roberto Cilli, Antonio Lacalamita, Marianna La Rocca, Sabina Tangaro, Nicola Amoroso and Roberto Bellotti
Remote Sens. 2024, 16(7), 1206; https://doi.org/10.3390/rs16071206 - 29 Mar 2024
Viewed by 436
Abstract
Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse [...] Read more.
Recent years have witnessed an increasing interest in air pollutants and their effects on human health. More generally, it has become evident how human, animal and environmental health are deeply interconnected within a One Health framework. Ground level air monitoring stations are sparse and thus have limited coverage due to high costs. Satellite and reanalysis data represent an alternative with high spatio-temporal resolution. The idea of this work is to build an Artificial Intelligence model for the estimation of surface-level daily concentrations of air pollutants over the entire Italian territory using satellite, climate reanalysis, geographical and social data. As ground truth we use data from the monitoring stations of the Regional Environmental Protection Agency (ARPA) covering the period 2019–2022 at municipal level. The analysis compares different models and applies an Explainable Artificial Intelligence approach to evaluate the role of individual features in the model. The best model reaches an average R2 of 0.84 ± 0.01 and MAE of 5.00 ± 0.01 μg/m3 across all pollutants which compare well with the body of literature. The XAI analysis highlights the pivotal role of satellite and climate reanalysis data. Our work can facilitate One Health surveys and help researchers and policy makers. Full article
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15 pages, 3172 KiB  
Article
The Application of Aluminium Powder as an Accumulation Medium of Mercury from Air
by Innocentia M. Modise, Nikolai Panichev and Khakhathi L. Mandiwana
Atmosphere 2024, 15(3), 368; https://doi.org/10.3390/atmos15030368 - 18 Mar 2024
Viewed by 857
Abstract
A gaseous elemental mercury (Hg0) sampler was developed for the assessment of mercury (Hg) pollution from the air and utilised aluminium (Al) powder as the accumulation medium. The Hg sampler is presented as an alternative cost-effective sorbent that can be used [...] Read more.
A gaseous elemental mercury (Hg0) sampler was developed for the assessment of mercury (Hg) pollution from the air and utilised aluminium (Al) powder as the accumulation medium. The Hg sampler is presented as an alternative cost-effective sorbent that can be used for the assessment of Hg pollution in atmospheric air in areas where natural bio-indicators such as lichens and moss do not grow, including the urban environments. The chemical treatment of Al materials was necessary to weaken the aluminium oxide (Al2O3) layer to increase the adsorption capability of Al material. Treated Al samples were exposed to Hg vapours for one hour to two weeks in a Hg atmosphere chamber. Other Al powder samples were exposed to the ambient air at areas of the Tshwane Metropolitan Municipality for six to ten months. The analysis of samples by an RA-915+ Zeeman mercury analyser showed that the limit of detection (LOD) and limit of quantification (LOQ) for the determination of Hg in Al powder with a mass of 100 mg were found to be 0.31 ng g−1 and 1.0 ng g−1, respectively. The content of Hg that accumulated on Al powder was linear from 0.1 to 25 ng g−1, thus enabling the measurement of Hg accumulation from air at the global average concentration level. Mercury from air that accumulated on Al powder in the Tshwane Metropolitan Municipality ranged between 70 ng g−1 and 155 ng g−1. Full article
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28 pages, 10203 KiB  
Article
Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning
by Mengnan Liu, Mingliang Ma, Mengjiao Liu, Fei Meng, Pingjie Fu, Huaqiao Xing, Jingxue Bi, Zhe Zheng and Yongqiang Lv
Sustainability 2024, 16(6), 2475; https://doi.org/10.3390/su16062475 - 16 Mar 2024
Viewed by 422
Abstract
The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement of air quality in China. In this work, precursors and related influencing factors were utilized to establish PM2.5 and O3 estimation models in [...] Read more.
The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement of air quality in China. In this work, precursors and related influencing factors were utilized to establish PM2.5 and O3 estimation models in the North China Plain (NCP), the Yangzi River Delta (YRD), and the Pearl River Delta (PRD) using a multi-task-learning (MTL) model. The prediction accuracy of these three MTL models was high, with R2 values ranging from 0.69 to 0.83. Subsequently, these MTL models were used to quantitatively reveal the relative importance of each factor to PM2.5 and O3 collaborative pollution simultaneously. Precursors and meteorological factors were the two most critical influencing factors for PM2.5 and O3 pollution in three regions, with their relative importance values larger than 29.99% and 15.89%, respectively. Furthermore, these models were used to reveal the response of PM2.5 and O3 to each precursor in each region. In the NCP and the YRD, the two most important precursors of PM2.5 pollution are SO2 and HCHO, while the two most critical factors for O3 pollution are HCHO and NO2. Therefore, SO2 and VOC emissions reduction is the most important measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in these two regions. In terms of the PRD, SO2 and NO2 are the most important precursors of PM2.5 pollution, while the most important precursors for O3 pollution are HCHO and SOX, respectively. Thus, NO2, SO2, and VOC emission reduction is the most critical measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in the PRD. Overall, this study provides clues and references for the control of PM2.5 and O3 collaborative pollution in the NCP, the YRD, and the PRD. Full article
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22 pages, 22153 KiB  
Article
High-Resolution Nitrogen Dioxide Measurements from an Airborne Fiber Imaging Spectrometer over Tangshan, China
by Xiaoli Zhang, Liang Xi, Haijin Zhou, Wei Wang, Zhen Chang, Fuqi Si and Yu Wang
Remote Sens. 2024, 16(6), 1042; https://doi.org/10.3390/rs16061042 - 15 Mar 2024
Viewed by 424
Abstract
The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom [...] Read more.
The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on 30 December 2022 and 5 January 2023, covering heavily polluted areas east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points. Full article
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13 pages, 1041 KiB  
Article
Emission Characteristics and Potential Exposure Assessment of Aerosols and Ultrafine Particles at Two French Airports
by Sébastien Artous, Eric Zimmermann, Cécile Philippot, Sébastien Jacquinot, Dominique Locatelli, Adeline Tarantini, Carey Suehs, Léa Touri and Simon Clavaguera
Air 2024, 2(1), 73-85; https://doi.org/10.3390/air2010005 - 13 Mar 2024
Viewed by 660
Abstract
Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (dmn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and [...] Read more.
Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (dmn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and Marseille). This study followed the standard operating procedures for characterizing aerosol emissions from 5 nm to 8 μm (OECD, 2015; EN 17058:2018). It allows determining which are the specific parameters directly related to the emission sources and their contribution to the overall aerosols measured at workplace in airports. The particulate emissions observed during aircraft engine start-up were ~19× higher than the average airborne concentration. The particle size distributions remained mostly <250 nm with dmn50 < 100 nm (showing a specificity for the medium-haul area with an average dmn50 of ~12 nm). The dmn50 can be used to distinguish emission peaks due to aircrafts (dmn50~15 nm) from those due to apron vehicle activities (dmn50 > 20 nm). Chemical elements (titanium and zinc) were identified as potential tracers of aircraft emissions and occurred mainly at the micrometric scale. For aircraft engine emissions, UFPs are mainly due to fuel combustion with the presence of carbon/oxygen. The study concludes with suggestions for future research to extend on the findings presented. Full article
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12 pages, 1800 KiB  
Article
Emission of Particulate Inorganic Substances from Prescribed Open Grassland Burning in Hirado, Akiyoshidai, and Aso, Japan
by Satoshi Irei, Seiichiro Yonemura, Satoshi Kameyama, Asahi Sakuma and Hiroto Shimazaki
Air 2024, 2(1), 61-72; https://doi.org/10.3390/air2010004 - 13 Mar 2024
Viewed by 510
Abstract
Biomass burning is one of the largest sources of particulate matter emissions globally. However, the emission of particulate inorganic species from prescribed grassland burning in Japan has not yet been characterized. In this study, we collected total suspended particulate matter from prescribed grassland [...] Read more.
Biomass burning is one of the largest sources of particulate matter emissions globally. However, the emission of particulate inorganic species from prescribed grassland burning in Japan has not yet been characterized. In this study, we collected total suspended particulate matter from prescribed grassland burning in Hirado, Akiyoshidai, and Aso, Japan. The collected filter samples were brought to the laboratory, and water-soluble inorganic components were analyzed via ion chromatography. The measurement results showed high excess concentrations of potassium, calcium, and magnesium, and these substances were highly correlated, which agreed with previously reported findings. In contrast, the concentrations of sodium, chloride, nitrate, and sulfate were insignificant, even though their high concentrations were reported in other biomass burning studies. Among these low concentration substances, a high correlation was still observed between sulfate and nitrate. It is possible that the low concentrations of those species could have been biased in the measurements, particularly as a result of subtracting blank and background values from the observed concentrations. Building up more data in this area may allow us to characterize the significance of domestic biomass burning’s contribution to inorganic particulate components in Japanese air, which may consequently contributes to better understanding of adverse health effect of airborne particulate matter. Full article
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17 pages, 4467 KiB  
Article
Black Carbon along a Highway and in a Residential Neighborhood during Rush-Hour Traffic in a Cold Climate
by Hrund Ólöf Andradóttir, Bergljót Hjartardóttir and Throstur Thorsteinsson
Atmosphere 2024, 15(3), 312; https://doi.org/10.3390/atmos15030312 - 01 Mar 2024
Viewed by 1417
Abstract
Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold [...] Read more.
Short-term exposure to ultra-fine Black Carbon (BC) particles produced during incomplete fuel combustion of wood and fossil fuel has been linked to respiratory and cardiovascular diseases, hospitalizations and premature deaths. The goal of this research was to assess traffic-related BC in a cold climate along an urban highway and 300 m into an adjacent residential neighborhood. BC was measured with an aethalometer (MA350, Aethlabs) along the main traffic artery in geothermally heated Reykjavík, the capital of Iceland (64.135° N–21.895° W, 230,000 inhabitants). Stationary monitoring confirmed that traffic was the dominant source of roadside BC in winter, averaging 1.0 ± 1.1 µg/m3 (0.6 and 1.1 µg/m3 median and interquartile range; 28,000 vehicles/day). Inter-day variations in BC were primarily correlated to the atmospheric lapse rate and wind speed, both during stationary and mobile campaigns. During winter stills, BC levels surpassed 10 µg/m3 at intersections and built up to 5 µg/m3 during the afternoon in the residential neighborhood (adjacent to the highway with 43,000 vehicles/day). The BC penetrated deeply into the neighborhood, where the lowest concentration was 1.8 µg/m3 within 300 m. BC concentration was highly correlated to nitrogen dioxide (r > 0.8) monitored at the local Urban Traffic Monitoring site. Full article
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23 pages, 8949 KiB  
Article
Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations
by Alessandro Marongiu, Anna Gilia Collalto, Gabriele Giuseppe Distefano and Elisabetta Angelino
Air 2024, 2(1), 38-60; https://doi.org/10.3390/air2010003 - 23 Feb 2024
Viewed by 752
Abstract
This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and [...] Read more.
This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH3 concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation. Full article
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26 pages, 1117 KiB  
Review
Maximising CO2 Sequestration in the City: The Role of Green Walls in Sustainable Urban Development
by Mansoure Jozay, Hossein Zarei, Sarah Khorasaninejad and Taghi Miri
Pollutants 2024, 4(1), 91-116; https://doi.org/10.3390/pollutants4010007 - 22 Feb 2024
Viewed by 657
Abstract
Environmental issues are a pressing concern for modern societies, and the increasing levels of atmospheric CO2 have led to global warming. To mitigate climate change, reducing carbon emissions is crucial, and carbon sequestration plays a critical role in this effort. Technologies for [...] Read more.
Environmental issues are a pressing concern for modern societies, and the increasing levels of atmospheric CO2 have led to global warming. To mitigate climate change, reducing carbon emissions is crucial, and carbon sequestration plays a critical role in this effort. Technologies for utilising CO2 can be divided into two major categories: direct use and conversion into chemicals and energy, and indirect use as a carbon source for plants. While plants’ ability to absorb and store CO2 makes them the best CO2 sink, finding suitable urban areas for significant green spaces is a challenge. Green walls are a promising solution, as they require less land, provide more ecosystem services than horizontal systems do, and can contribute to reducing environmental problems. This study evaluates the conceptual potentials and limitations of urban biomass circulation in terms of energy production, food production, and CO2 consumption, focusing on growth-promoting bacteria, urban agriculture, and vertical systems. The aim of this research is discovering new methods of carbon sequestration using multi-purpose green walls to achieve sustainable urban development and CO2 reduction strategies to contribute to a more sustainable future. Full article
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22 pages, 3503 KiB  
Article
Identification of Causes of Air Pollution in a Specific Industrial Part of the Czech City of Ostrava in Central Europe
by Vladimíra Volná, Radim Seibert, Daniel Hladký and Blanka Krejčí
Atmosphere 2024, 15(2), 177; https://doi.org/10.3390/atmos15020177 - 30 Jan 2024
Viewed by 574
Abstract
This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to [...] Read more.
This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to identify the sources of air pollution in the area, particularly in locations where the contribution of different sources to concentrations of pollutants of concern has not been elucidated yet. In this study, source apportionment in the vicinity of the industrial hot spot was performed by statistically evaluating measured pollutant concentrations as a function of meteorological variables and using the Positive Matrix Factorization (PMF) receptor model. A significant methodological innovation and improvement over previous assessments was the higher monitoring resolution of benzo[a]pyrene, with samples collected at three-hour intervals instead of the standard 24 h collection period. The key findings indicate that in the cold part of the year, secondary particles—specifically sulfates and ammonium nitrates—were responsible for the most significant portion of PM10 air pollution throughout the area of interest. The contribution of these particles ranged from one-third to two-fifths of the total concentration, except at the industrial site of Ostrava–Radvanice (TORE), where they accounted for approximately one-fifth of the measured pollution concentration level. Emissions from individual household heating were identified as the main source of this type of pollution. With regards to benzo[a]pyrene air pollution, this study found that in the whole area of interest, except for the Ostrava–Radvanice site, it mainly originated from individual heating with coal (90%). In contrast, at the Ostrava–Radvanice site, two-thirds of the benzo[a]pyrene pollution came from the premises of Liberty Ostrava a.s., primarily from coke production, and less than one-third came from local domestic heating. This study also determined the spatial extent of the occurrence of extremely high benzo[a]pyrene concentrations (above 5 ng/m3), which are estimated to affect nearly 10,000 inhabitants. The results confirm that the data from the TORE station are only representative of its immediate surroundings and are not applicable to the assessment of air quality and causes of air pollution in the whole city of Ostrava or the urban area of Ostrava–Radvanice as a whole. After years of research, these findings provide the Ministry of the Environment of the Czech Republic and the government with an accurate basis for implementing measures to address the identified pollution sources in the area of interest. The success of this study was made possible by the adoption of a more detailed sampling approach, which involved a resolution of 3 h instead of 24 h. This methodological improvement is a significant finding and will be useful for future source apportionment studies. Full article
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16 pages, 3053 KiB  
Article
Carbon Fixation and Oxygen Release Capacity of Typical Riparian Plants in Wuhan City and Its Influencing Factors
by Zhiqian Lei, Qin Wang and Henglin Xiao
Sustainability 2024, 16(3), 1168; https://doi.org/10.3390/su16031168 - 30 Jan 2024
Viewed by 521
Abstract
In order to explore the carbon fixation and oxygen release capabilities of riparian plants in Wuhan, the photosynthetic rate (Pn) and morphological indicators of 13 typical riparian plants in the middle section of the Xunsi River in Wuhan were measured by portable photosynthesis [...] Read more.
In order to explore the carbon fixation and oxygen release capabilities of riparian plants in Wuhan, the photosynthetic rate (Pn) and morphological indicators of 13 typical riparian plants in the middle section of the Xunsi River in Wuhan were measured by portable photosynthesis apparatus. The daily carbon fixation and oxygen release of each plant at different scales were calculated, and the carbon fixation and oxygen release capacity and its influencing factors were analyzed. The results show that: (1) according to the biological characteristics, the daily carbon fixation and oxygen release capacity per unit leaf area was higher in herbaceous than in trees; the daily carbon fixation and oxygen release capacity per plant, per projected area, and per land area were higher in trees than in herbaceous. (2) The plant with the strongest ability of daily carbon fixation and oxygen release per unit leaf area was Ruellia brittoniana, and the weakest was Triadica sebifera; the plant with the strongest ability of daily carbon fixation and oxygen release of a single plant was Metasequoia glyptostroboides, and the weakest was Lolium perenne; the plant with the strongest ability of daily carbon fixation and oxygen release per land area was Metasequoia glyptostroboides, and the weakest was Alternanthera sessilis. (3) The carbon fixation and oxygen release ability of 13 plant species was analyzed by cluster analysis based on per unit leaf area, per plant, and per land area; ten species of herbaceous plant could be divided into three grades and three species of trees into two grades. This study provides a theoretical reference for the selection and application of riparian zone vegetation in Wuhan, and provides a scientific basis for the evaluation of riparian zone ecological benefits. Full article
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14 pages, 2445 KiB  
Article
Quantifying Contributions of Factors and Their Interactions to Aerosol Acidity with a Multiple-Linear-Regression-Based Framework: A Case Study in the Pearl River Delta, China
by Hong Ling, Mingqi Deng, Qi Zhang, Lei Xu, Shuzhen Su, Xihua Li, Liming Yang, Jingying Mao and Shiguo Jia
Atmosphere 2024, 15(2), 172; https://doi.org/10.3390/atmos15020172 - 29 Jan 2024
Viewed by 679
Abstract
This study presents an approach using multiple linear regression to quantify the impact of meteorological parameters and chemical species on aerosol pH variance in an urban setting in the Pearl River Delta, China. Additionally, it assesses the contributions of interactions among these factors [...] Read more.
This study presents an approach using multiple linear regression to quantify the impact of meteorological parameters and chemical species on aerosol pH variance in an urban setting in the Pearl River Delta, China. Additionally, it assesses the contributions of interactions among these factors to the variance in pH. The analysis successfully explains over 96% of the pH variance, attributing 85.8% to the original variables and 6.7% to bivariate interactions, with further contributions of 2.3% and 1.0% from trivariate and quadrivariate interactions, respectively. Our results highlight that meteorological factors, particularly temperature and humidity, are more influential than chemical components in affecting aerosol pH variance. Temperature alone accounts for 37.3% of the variance, while humidity contributes approximately 20%. On the chemical front, sulfate and ammonium are the most significant contributors, adding 14.3% and 9.1% to the pH variance, respectively. In the realm of bivariate interactions, the interplay between meteorological parameters and chemical components, especially the TNO3RH pair, is exceptionally impactful, constituting 58.1% of the total contribution from interactions. In summary, this study illuminates the factors affecting aerosol pH variance and their interplay, suggesting the integration of statistical methods with thermodynamic models for enhanced understanding of aerosol acidity dynamics in the future. Full article
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16 pages, 1430 KiB  
Article
Experimental and Modeled Assessment of Interventions to Reduce PM2.5 in a Residence during a Wildfire Event
by Chrissi Antonopoulos, H. E. Dillon and Elliott Gall
Pollutants 2024, 4(1), 26-41; https://doi.org/10.3390/pollutants4010003 - 28 Jan 2024
Viewed by 680
Abstract
Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be [...] Read more.
Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events. Full article
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14 pages, 2236 KiB  
Article
Measuring the Synergistic Effect of Pollution and Carbon Reduction in China’s Industrial Sector
by Minglong Xu, Huimin Li and Xianghui Deng
Sustainability 2024, 16(3), 1048; https://doi.org/10.3390/su16031048 - 25 Jan 2024
Viewed by 958
Abstract
The industrial sector is a major source of CO2 and atmospheric pollutants in China, and it is important to promote industrial pollution reduction and carbon reduction to improve the quality of China’s atmospheric environment and meet CO2 peak targets. In this [...] Read more.
The industrial sector is a major source of CO2 and atmospheric pollutants in China, and it is important to promote industrial pollution reduction and carbon reduction to improve the quality of China’s atmospheric environment and meet CO2 peak targets. In this paper, based on 2005 to 2021’s panel data from the industrial sector, we construct a computational model of the synergistic effect of pollution reduction and carbon reduction, quantitatively evaluate the synergistic effect of industrial CO2 emissions and air pollutants, and explore its evolutionary mechanism. The results showed that between 2005 and 2021, there was a clear synergistic effect between CO2 and air pollutants in China’s industrial sector, and the synergistic effect is increasing. For different pollutants, CO2 and SO2 have the strongest synergies, and CO2 and particulate matter have relatively weak synergies. For different energy types, the synergies between coal-related carbon emissions and air pollutants gradually increase, while gas-related carbon emissions and pollutants tend to decrease. From different industry types, the synergies between CO2 and air pollutants are weaker in high-polluting and high-emission industries than in other industries. These results have strong policy implications. First, the focus of synergistic measures should be on source reduction. The second is to make high-polluting and high-emission industries the focus of pollution reduction and carbon reduction. Third is harmonized management of air quality standards and carbon peaking should be promoted. The formulation of relevant policies from the above three aspects will help synergize pollution reduction and carbon reduction in the industrial sector. Full article
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28 pages, 12041 KiB  
Article
Industrial Heat Source-Related PM2.5 Concentration Estimates and Analysis Using New Three-Stage Model in the Beijing–Tianjin–Hebei Region
by Yi Zeng, Xin Sui, Caihong Ma, Ruilin Liao, Jin Yang, Dacheng Wang and Pengyu Zhang
Atmosphere 2024, 15(1), 131; https://doi.org/10.3390/atmos15010131 - 20 Jan 2024
Viewed by 903
Abstract
The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this [...] Read more.
The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development. Full article
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15 pages, 5191 KiB  
Article
Monitoring the Spatio-Temporal Distribution of Soil Salinity Using Google Earth Engine for Detecting the Saline Areas Susceptible to Salt Storm Occurrence
by Mohammad Kazemi Garajeh
Pollutants 2024, 4(1), 1-15; https://doi.org/10.3390/pollutants4010001 - 08 Jan 2024
Viewed by 612
Abstract
Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has [...] Read more.
Recent droughts worldwide have significantly affected ecosystems in various regions. Among these affected areas, the Lake Urmia Basin (LUB) has experienced substantial effects from both drought and human activity in recent years. Lake Urmia, known as one of the hypersaline lakes globally, has been particularly influenced by these activities. The extraction of water since 1995 has resulted in an increase in the extent of salty land, leading to the frequent occurrence of salt storms. To address this issue, the current study utilized various machine learning algorithms within the Google Earth Engine (GEE) platform to map the probability of saline storm occurrences. Landsat time-series images spanning from 2000 to 2022 were employed. Soil salinity indices, Ground Points (GPs), and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products were utilized to prepare the training data, which served as input for constructing and running the models. The results demonstrated that the Support Vector Machine (SVM) performed effectively in identifying the probability of saline storm occurrence areas, achieving high R2 values of 91.12%, 90.45%, 91.78%, and 91.65% for the years 2000, 2010, 2015, and 2022, respectively. Additionally, the findings reveal an increase in areas exhibiting a very high probability of saline storm occurrences from 2000 to 2022. In summary, the results of this study indicate that the frequency of salt storms is expected to rise in the near future, owing to the increasing levels of soil salinity resources within the Lake Urmia Basin. Full article
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18 pages, 11247 KiB  
Article
Numerical Investigation of the Impact of Tall Buildings on Pollutant Dispersion during Stable Stratification
by Yunpeng Li, Ruojie Li, Dongpeng Guo, Dezhong Wang, Yanhui Pan, Junfang Zhang and Rentai Yao
Atmosphere 2024, 15(1), 16; https://doi.org/10.3390/atmos15010016 - 22 Dec 2023
Viewed by 595
Abstract
The present study employs the k-epsilon turbulence model to investigate the influence of stable stratification with different Richardson numbers (Rib) on flow patterns and pollutant dispersion near tall buildings. The results show that thermal stratification significantly affects the flow [...] Read more.
The present study employs the k-epsilon turbulence model to investigate the influence of stable stratification with different Richardson numbers (Rib) on flow patterns and pollutant dispersion near tall buildings. The results show that thermal stratification significantly affects the flow pattern around buildings. As Rib increases, the leeward stagnation point gradually shifts upward toward the top of the building, while the recirculation region on the top of the building moves downward, and the length of the recirculation region on the windward side initially increases and then decreases. The vortex position gradually moves above the building. The region with high TKE/uH2 is primarily concentrated on the top of the building and within the downwind recirculation area. As Rib increases, the TKE/uH2 decreases in the top and wake regions of the building. With increasing Rib, the ground-level pollutant concentration first increases and then decreases, the height of the downwind plume gradually reduces, while the maximum concentration in the plume rises. Full article
(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)
(This article belongs to the Section Air Pollution Control)
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22 pages, 9517 KiB  
Article
Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China
by Mingliang Ma, Mengjiao Liu, Mengnan Liu, Huaqiao Xing, Yuqiang Wang and Fei Meng
Sustainability 2024, 16(1), 123; https://doi.org/10.3390/su16010123 - 22 Dec 2023
Viewed by 653
Abstract
Surface ozone pollution in China has been persistently becoming worse in recent years; therefore, it is of great importance to accurately estimate ozone pollution and explore the spatiotemporal variations in surface ozone in East China. By using S5P-TROPOMI-observed NO2, HCHO data [...] Read more.
Surface ozone pollution in China has been persistently becoming worse in recent years; therefore, it is of great importance to accurately estimate ozone pollution and explore the spatiotemporal variations in surface ozone in East China. By using S5P-TROPOMI-observed NO2, HCHO data (7 km × 3.5 km), and other surface-ozone-influencing factors, including VOCs, meteorological data, NOX emission inventory, NDVI, DEM, population, land use and land cover, and hourly in situ surface ozone observations, an extreme gradient boosting model was used to estimate the daily 0.05° × 0.05° gridded maximum daily average 8 h ozone (MDA8) in East China during 2019–2021. Four surface ozone estimation models were established by combining NO2 and HCHO data from S5P-TROPOMI observations and CAMS reanalysis data. The sample-based validation R2 values of these four models were all larger than 0.92, while their site-based validation R2 values were larger than 0.82. The results revealed that the coverage ratio of the model using CAMS NO2 and CAMS HCHO was the highest (100%), while the coverage ratio of the model using S5P-TROPOMI NO2 and CAMS HCHO was the second highest (96.26%). Furthermore, the MDA8 estimation results of these two models were averaged to produce the final surface ozone estimation dataset. It indicated that O3 pollution in East China during 2019–2021 was susceptible to anthropogenic precursors such as VOCs (22.55%) and NOX (8.97%), as well as meteorological factors (27.35%) such as wind direction, temperature, and wind speed. Subsequently, the spatiotemporal patterns of ozone pollution were analyzed. Ozone pollution in East China is mainly concentrated in the North China Plain (NCP), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD). Among these three regions, ozone pollution in the NCP mainly occurs in June (summer), ozone pollution in the YRD mainly occurs in May (spring), and ozone pollution in the PRD mainly occurs in April (spring) and September (autumn). In addition, surface O3 concentration in East China decreased by 3.74% in 2020 compared to 2019, which may have been influenced by the COVID-19 epidemic and the implementation of the policy of synergistic management of PM2.5 and O3 pollution. The regions mostly affected by the COVID-19 epidemic and the policy of the synergistic management of PM2.5 and O3 pollution were the NCP (−2~−8%), the Middle and Lower of Yangtze Plain (−6~−10%), and the PRD (−4~−10%). Overall, the estimated 0.05° × 0.05° gridded surface ozone in East China from 2019 to 2021 provides a promising data source and data analysis basis for the related researchers. Meanwhile, it reveals the spatial and temporal patterns of O3 pollution and the main influencing factors, which provides a good basis for the control and management of O3 pollution, and also provides technical support for the sustainable development of the environment in East China. Full article
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18 pages, 668 KiB  
Article
Unveiling the Environmental–Economic Nexus: Cointegration and Causality Analysis of Air Pollution and Growth in Oman
by Mwahib Mohammed and Sufian Abdel-Gadir
Sustainability 2023, 15(24), 16918; https://doi.org/10.3390/su152416918 - 17 Dec 2023
Cited by 1 | Viewed by 849
Abstract
The complex relationship between environmental degradation—more especially, air pollution—and economic growth in the Sultanate of Oman between 1990 and 2022 is examined in this article. To identify short- and long-term dynamics in the relationship between air pollution and economic growth, we use vector [...] Read more.
The complex relationship between environmental degradation—more especially, air pollution—and economic growth in the Sultanate of Oman between 1990 and 2022 is examined in this article. To identify short- and long-term dynamics in the relationship between air pollution and economic growth, we use vector error correction models and cointegration. Additionally, Granger causality analysis is used to look into the causal relationships between these important variables. This dataset includes several control variables as well as environmental quality-related factors. The empirical findings demonstrate that the variables have a consistent long-term cointegration relationship. Furthermore, our results show that energy consumption and economic growth have a statistically significant positive effect on CO2 emissions. Moreover, an annual adjustment of about 14.1% in N2O emission disequilibrium is revealed by the short-term analysis. The Granger causality study shows that there are unidirectional causal linkages between CO2 emissions, economic growth, and N2O emissions. These results have significant policy-related ramifications for Oman. Oman has to implement strong climate change policies in order to effectively cut greenhouse gas emissions. Furthermore, as a potential replacement for conventional oil and gas resources, the government can be a key player in promoting and supporting the use of renewable energy sources like green hydrogen. Full article
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21 pages, 4511 KiB  
Article
Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment
by Andrew Shapero, Stella Keck and Adam H. Love
Air 2023, 1(4), 258-278; https://doi.org/10.3390/air1040019 - 05 Dec 2023
Viewed by 1082
Abstract
Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM [...] Read more.
Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure. Full article
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26 pages, 3548 KiB  
Article
Ecological and Risk Assessment of Heavy Metals in a Diverse Industrial Area of Al-Akrasha, Egypt
by Atef M. F. Mohammed, Inas A. Saleh, Hend R. Zahran and Nasser M. Abdel-Latif
Atmosphere 2023, 14(12), 1745; https://doi.org/10.3390/atmos14121745 - 27 Nov 2023
Viewed by 710
Abstract
This study was conducted in one of a diverse industrial area in Al-Akrasha, Egypt. Concentrations of select metals (Cu, Pb, Cr, Ni, Zn, Mn, Cd, Al, Ag, As, B, and Fe) were evaluated in ambient PM10 and surface soils at nine sites. [...] Read more.
This study was conducted in one of a diverse industrial area in Al-Akrasha, Egypt. Concentrations of select metals (Cu, Pb, Cr, Ni, Zn, Mn, Cd, Al, Ag, As, B, and Fe) were evaluated in ambient PM10 and surface soils at nine sites. Random samples of fresh edible tilapia fish were collected from Ismailia Canal at two sites near the Al-Akrasha region. In addition, blood and hair samples were collected from workers and residents living in Al-Akrasha as biomarkers of contamination with these metals. The ecological and health risks of these metals to the workers and residents living in the Al-Akrasha region were assessed. The results showed that heavy metal levels in the ambient air (PM10) of the Al-Akrasha region were higher than the national and international guidelines. There was a very high degree of contamination (CD > 32) of the surface soil in the Al-Akrasha area, which can be attributed to industrial activities emissions, mostly from smelters and the subsequent deposition on the surface soil. Ingestion was the dominant pathway for metals to enter the human body in the Al-Akrasha region. Adults have a higher daily intake and exposure risk than infants and children. Full article
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19 pages, 15744 KiB  
Article
Assessment of the Temporal and Seasonal Variabilities in Air Pollution and Implications for Physical Activity in Lagos and Yaoundé
by Olalekan A. M. Popoola, Rose Alani, Felix Assah, Taibat Lawanson, Awah K. Tchouaffi, Clarisse Mapa-Tassou, Nfondoh Blanche, Damilola Odekunle, Richard Unuigboje, Victor A. Onifade, Toluwalope Ogunro, Meelan Thondoo, Roderic L. Jones and Tolu Oni
Atmosphere 2023, 14(11), 1693; https://doi.org/10.3390/atmos14111693 - 17 Nov 2023
Viewed by 1288
Abstract
Physical activity (PA) can reduce the risk of non-communicable diseases like heart diseases and diabetes. However, exposure to poor air quality (AQ) when engaging in PA could negate the health benefits. The risk associated with air pollution is relatively severe during physical activities [...] Read more.
Physical activity (PA) can reduce the risk of non-communicable diseases like heart diseases and diabetes. However, exposure to poor air quality (AQ) when engaging in PA could negate the health benefits. The risk associated with air pollution is relatively severe during physical activities because a higher inhaled pollution dose is experienced during PA compared to when sedentary. We conducted a yearlong AQ monitoring using a commercial low-cost AQ device. The devices were deployed near a public space used for PA as part of a study to understand the health risks encountered by people informally appropriating public spaces for PA in Lagos, Nigeria and Yaoundé, Cameroon. The parameters monitored included CO, NO, NO2, O3, PM2.5, PM10, CO2, pressure, temperature and relative humidity. We detected unique pollutant temporal profiles at the two locations, with a distinct weekday-to-weekend effect observed for the gaseous pollutants but not for the PM. Transboundary emissions related to the Harmattan haze dominated the background PM concentration in both cities in the dry season. Our findings underscore the importance of long-term AQ monitoring to inform action and offer insights into simple behavioural changes that can maximise the health benefits of PA while minimising the risk of air pollution exposure. Full article
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18 pages, 4607 KiB  
Article
Assessment of Atmospheric Air Quality in the Region of Central Kazakhstan and Astana
by Raikhan Beisenova, Bektemir Zhumashev Kuanyshevich, Gulzhazira Turlybekova, Bakhytzhan Yelikbayev, Anuarbek A. Kakabayev, Samal Shamshedenova and Askar Nugmanov
Atmosphere 2023, 14(11), 1601; https://doi.org/10.3390/atmos14111601 - 26 Oct 2023
Viewed by 1079
Abstract
One of the main issues of environmental protection is the quality of atmospheric air. These problems are especially acute in industrialized regions, where the level of anthropogenic impact is increasing; in Kazakhstan, Central Kazakhstan belongs to such regions. The purpose of this study [...] Read more.
One of the main issues of environmental protection is the quality of atmospheric air. These problems are especially acute in industrialized regions, where the level of anthropogenic impact is increasing; in Kazakhstan, Central Kazakhstan belongs to such regions. The purpose of this study is to study the relationship between diseases of the population and air pollutants from industrial sources. The research methodology was the use of ArcGIS tools and the construction of a correlation between two parameters: pollution and morbidity in the region. Analysis of mortality rates of the population by main classes of causes of death for 2017–2020 in the regional context in the Republic of Kazakhstan revealed that the mortality rate in 2020 increased by 20.2%. When analyzing the causes of death of the population, diseases associated with the negative impact of the environment were selected. It was noted that, in general, in the Republic of Kazakhstan from 2017 to 2020, there was a downward trend, but in the Karaganda region, in 2020, it increased by 8.7%. In Astana, this indicator also tended to decrease, but as a result, a very strong correlation was found between the incidence of malignant neoplasms in Astana and nitrogen dioxide pollution (Pearson index 0.95). Full article
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16 pages, 9186 KiB  
Article
Remote Sensing of Planetary Boundary Layer Thermodynamic and Material Structures over a Large Steel Plant, China
by Xinbing Ren, Liping Zhao, Yongjing Ma, Junsong Wu, Fentao Zhou, Danjie Jia, Dandan Zhao and Jinyuan Xin
Remote Sens. 2023, 15(21), 5104; https://doi.org/10.3390/rs15215104 - 25 Oct 2023
Cited by 2 | Viewed by 646
Abstract
Air pollutants emitted by industries can significantly affect local air quality and jeopardize human health, and the study of the boundary layer thermodynamic structure and diffusion capacity over industrial plants can be beneficial for the improvement of corporate air pollution control measures. The [...] Read more.
Air pollutants emitted by industries can significantly affect local air quality and jeopardize human health, and the study of the boundary layer thermodynamic structure and diffusion capacity over industrial plants can be beneficial for the improvement of corporate air pollution control measures. The continuous high temporal and spatial resolution monitoring of the boundary layer structure (thermal, dynamic, and material) by advanced remote sensing instruments over a single strong industrial source (steel plant) in Shanxi Province, China, from May to June 2021 revealed the boundary layer characteristics under the influence of a single strong local anthropogenic influence. Strong nocturnal temperature inversions and grounded temperature inversions were prone to occur over industrial sources. The local wind field was characterized by significant daily variations, with the whole-layer airflow during the daytime dominated by southwesterly winds. At night, under the influence of radiation, topography, and surface, the airflow was dominated by easterly winds with low speeds (less than 2 m/s) in the low altitude range of 100 m, while the wind direction was still dominated by southwesterly winds with higher speeds in the altitude of 100 m. In addition, the average atmospheric diffusion capacity increased significantly with height in the 500 m altitude range, with an increase in rate of about 2~3 times/50 m, and continued to show a discontinuous increasing trend above 500 m. Combined with the wind direction and wind speed contours, it can be seen that the pollutants can be effectively dispersed at a height of 100 m. The thermal and turbulent boundary layer heights were highly consistent, and the material boundary layer height was significantly higher than the thermal and turbulent boundary layer heights during the daytime when convection was strong. Full article
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15 pages, 2465 KiB  
Article
Nontrivial Impact of Relative Humidity on Organic New Particle Formation from Ozonolysis of cis-3-Hexenyl Acetate
by Austin C. Flueckiger, Christopher N. Snyder and Giuseppe A. Petrucci
Air 2023, 1(4), 222-236; https://doi.org/10.3390/air1040017 - 17 Oct 2023
Viewed by 826
Abstract
The impact of relative humidity (RH) on organic new particle formation (NPF) from the ozonolysis of biogenic volatile organic compounds (BVOCs) remains an area of active debate. Previous reports provide contradictory results, indicating both the depression and enhancement of NPF under conditions of [...] Read more.
The impact of relative humidity (RH) on organic new particle formation (NPF) from the ozonolysis of biogenic volatile organic compounds (BVOCs) remains an area of active debate. Previous reports provide contradictory results, indicating both the depression and enhancement of NPF under conditions of high RH. Herein, we report on the impact of RH on NPF from the dark ozonolysis of cis-3-hexenyl acetate (CHA), a green-leaf volatile (GLV) emitted by vegetation. We show that RH inhibits NPF by this BVOC, essentially shutting it down at RH levels > 1%. While the mechanism for the inhibition of NPF remains unclear, we demonstrate that it is likely not due to increased losses of CHA to the humid chamber walls. New oxidation products dominant under humid conditions are proposed that, based on estimated vapor pressures (VPs), should enhance NPF; however, it is possible that the vapor phase concentration of these low-volatility products is not sufficient to initiate NPF. Furthermore, the reaction of C3-excited state Criegee intermediates (CIs) with water may lead to the formation of small carboxylic acids that do not contribute to NPF. This hypothesis is supported by experiments with quaternary O3 + CHA + α-pinene + RH systems, which showed decreases in total α-pinene-derived NPF at ~0% RH and subsequent recovery at elevated RH. Full article
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15 pages, 1655 KiB  
Article
Ammonia Cycling and Emerging Inorganic Secondary Aerosols from Arable Agriculture
by Vivien Pohl, Alan Gilmer, Vivienne Byers, John Cassidy, Aoife Donnelly, Stig Hellebust, Eoin J. McGillicuddy, Eugene McGovern and David J. O’Connor
Air 2023, 1(3), 207-221; https://doi.org/10.3390/air1030016 - 19 Sep 2023
Viewed by 1152
Abstract
Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Secondary inorganic aerosols (SIAS) have been acknowledged as a key atmospheric pollutant, with serious public [...] Read more.
Air quality monitoring in Ireland is under the jurisdiction of the Environmental Protection Agency in compliance with the Gothenburg Protocol, EU/national legislation, and the National Clean Air Strategy. Secondary inorganic aerosols (SIAS) have been acknowledged as a key atmospheric pollutant, with serious public health impacts and no safe exposure threshold in place to date. Ammonia (NH3) emissions are linked to the secondary production of aerosols through atmospheric reactions occurring with acidic atmospheric components such as sulfuric, nitric, and hydrochloric acid. These reactions result in the formation of ammonium sulfate, ammonium nitrate and ammonium chloride, among others. Approximately 98% of NH3 emissions occurring in Ireland arise from agriculture, with minor contributions from transport and natural sources. A better understanding of NH3 emissions and SIA formation can be achieved through monitoring emissions at the source level. Additionally, mitigation strategies with a more thorough understanding of NH3 dynamics at the source level and consequential SIA formation allow for more efficient action. This project monitored ambient NH3 and SIA on two selected arable agricultural sites and a control site in a rural site close to Dublin on the east coast of Ireland to establish emission levels. Meteorological factors affecting emissions and SIA formation were also measured and cross-correlated to determine micro-meteorological effects. Monitoring at the agricultural sites observed ambient NH3 concentrations ranging from 0.52 µg m−3 to 1.70 µg m−3, with an average of 1.45 µg m−3. At the control site, ambient NH3 measured concentrations ranged from 0.05 µg m−3 to 1.76 µg m−3 with an average of 0.516 µg m−3. Aerosol NH4+ ranged from 0.03 µg m−3 to 1.05 µg m−3 with an average concentration of 0.27 µg m−3 at the agricultural site. The potential effects of meteorological conditions and the implications for the effects of these emissions are discussed, with recommendations to aid compliance with the National Emissions Ceiling and the National Clean Air Strategy (Directive 2001/81/EC). Full article
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18 pages, 6626 KiB  
Article
Composition Characteristics of VOCs in the Atmosphere of the Beibei Urban District of Chongqing: Insights from Long-Term Monitoring
by Shixu Luo, Qingju Hao, Zhongjun Xu, Guosheng Zhang, Zhenghao Liang, Yongxiang Gou, Xunli Wang, Fanghui Chen, Yangjian He and Changsheng Jiang
Atmosphere 2023, 14(9), 1452; https://doi.org/10.3390/atmos14091452 - 18 Sep 2023
Viewed by 902
Abstract
Reducing anthropogenic volatile organic compounds (VOCs) is the most effective way to mitigate O3 pollution, which has increased over the past decades in China. From 2012 to 2017, special stainless-steel cylinders were used to collect ambient air samples from the urban area [...] Read more.
Reducing anthropogenic volatile organic compounds (VOCs) is the most effective way to mitigate O3 pollution, which has increased over the past decades in China. From 2012 to 2017, special stainless-steel cylinders were used to collect ambient air samples from the urban area of Beibei district, Chongqing. Three-step pre-concentration gas chromatography–mass spectrometry was used to detect the collected air samples. The composition, concentration, photochemical reactivity, and sources of VOCs in Beibei were analyzed. During the observation period, the annual average VOC concentration was 31.3 ppbv, which was at an intermediate range compared to other cities in China. Alkanes (36.8%) and aromatics (35.6%) were the most abundant VOC groups, followed by halo-hydrocarbons (14.4%) and alkenes (12.6%). The overall trend of seasonal distribution of VOC concentration was high in summer and autumn, and low in winter and spring, with a statistically significant difference between summer and winter concentrations. The ozone formation potential (OFP) showed that alkenes were the most active species, followed by aromatics and alkanes, and summer was the season with the highest OFP (131.6 ppbv). Three major emission sources were identified through principal component analysis (PCA), i.e., vehicle exhaust emissions (66.2%), fuel oil evaporation (24.8%), and industrial sources (9.0%). To ameliorate the air quality within the study area, concerted efforts should be directed towards curtailing traffic emissions and mitigating the release of alkenes, particularly emphasizing more stringent interventions during the summer season. Full article
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14 pages, 6523 KiB  
Article
Spatio-Temporal Differentiation Characteristics and Driving Factors of Urban Thermal Environment: A Case Study in Shaanxi Province, China
by Xiaogang Feng, Zaihui Zhou, Sekhar Somenahalli, Meng Li, Fengxia Li and Yuan Wang
Sustainability 2023, 15(17), 13206; https://doi.org/10.3390/su151713206 - 02 Sep 2023
Viewed by 901
Abstract
Rapid urbanization and global warming have led to a series of ecological and health problems caused by the deterioration of urban thermal environment (UTE). Using a comprehensive analysis of meteorological and remote sensing data for Shaanxi Province, a model of urban thermal differentiation [...] Read more.
Rapid urbanization and global warming have led to a series of ecological and health problems caused by the deterioration of urban thermal environment (UTE). Using a comprehensive analysis of meteorological and remote sensing data for Shaanxi Province, a model of urban thermal differentiation (UTD) was developed, and the spatio-temporal characteristics of UTE in different regions were analyzed. Using the Geo-explore model, natural and socio-economic factors were chosen to explain the spatio-temporal distribution changes in UTE. The results showed that the UTD and Geo-explore models can be used to estimate spatio-temporal differentiation characteristics and change patterns of UTE. This method can describe UTE’s spatial distribution and change characteristics well, making it suitable for multiple-perspective evaluations. In Shaanxi Province, the spatio-temporal distribution of UTE shows a decreasing trend from south to north and east to west. After 2000, the UTD showed a relatively stable performance in the Southern, Central, and Northern regions. The atmospheric temperature (AT) varied greatly across regions due to different factors. UTE mitigation and improved urban design can be achieved using this method. Full article
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13 pages, 4373 KiB  
Article
An Outlier Detection Study of Ozone in Kolkata India by the Classical Statistics, Statistical Process Control and Functional Data Analysis
by Mohammad Ahmad, Weihu Cheng and Xu Zhao
Sustainability 2023, 15(17), 12790; https://doi.org/10.3390/su151712790 - 24 Aug 2023
Cited by 1 | Viewed by 890
Abstract
Air pollution is prevalent throughout the entire world due to the release of various gases such as NOx, PM, SO2, tropospheric ozone (O3), etc. Ground-stage ozone is the predominant issue in smog and is the product of [...] Read more.
Air pollution is prevalent throughout the entire world due to the release of various gases such as NOx, PM, SO2, tropospheric ozone (O3), etc. Ground-stage ozone is the predominant issue in smog and is the product of the interplay between sunlight and emissions. The destructive impact on the health of the populace might also still occur in cities with noticeably clean air and where ozone levels hardly ever exceed safe limits. Therefore, the findings of small variations in air quality and the technique of regulating air contamination are thought-provoking. The study employs various techniques to effectively observe and assess strategies for detecting and eliminating outliers in ozone emissions from pollution episodes. This technique helps to describe the sources and exceedance values and enhance the value of monitoring the data. In this study, the data have some missing observations. The method of imputation, the classical statistical technique, the statistical process control (SPC) technique, functional data analysis (FDA), and functional process control help to fill in the data and detect outliers, trend deviations, and changes in ozone concentration at ground level. A comparison study is carried out using these three techniques: classical analysis, SPC, and FDA, and the results show how the statistical process control and functional data methods performed better than the classical technique for the detection of outliers and also in what way this methodology can enable an additional, comprehensive method of defining air pollution control measures and water pollution control measures. Full article
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20 pages, 8671 KiB  
Article
Monitoring of Ambient Air Quality Patterns and Assessment of Air Pollutants’ Correlation and Effects on Ambient Air Quality of Lahore, Pakistan
by Waqas Ahmed Khan, Faiza Sharif, Muhammad Fahim Khokhar, Laila Shahzad, Nusrat Ehsan and Muhammad Jahanzaib
Atmosphere 2023, 14(8), 1257; https://doi.org/10.3390/atmos14081257 - 07 Aug 2023
Cited by 2 | Viewed by 2218
Abstract
Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, [...] Read more.
Industrialization, explosive population growth, anthropogenic activities, and vehicular exhaust deteriorate ambient air quality across the world. The current study aims at assessing the impacts on ambient air quality patterns and their co-relations in one of the world’s most polluted cities, i.e., Lahore, Pakistan, during a strict, moderate, and post-COVID-19 period of 28 months (March 2020–June 2022). The purpose of this study is to monitor and analyze the relationship between criteria air pollutants (SO2, particulate matter (PM 10 and 2.5), CO, O3, and NO2) through a Haz-Scanner 6000 and mobile van (ambient air quality monitoring station) over nine towns in Lahore. The results showed significantly lower concentrations of pollutants during strict lockdown which increased during the moderate and post-COVID-19 lockdown periods. The post-COVID-19 period illustrates a significant increase in the concentrations of SO2, PM10, PM2.5, CO, O3, and NO2, in a range of 100%, 270%, 500%, 300%, 70%, and 115%, respectively. Major peaks (pollution concentration) for PM10, PM2.5, NO2, and SO2 were found during the winter season. Multi-linear regression models show a significant correlation between PM with NO2 and SO2. The ratio of increase in the PM concentration with the increasing NO2 concentration is nearly 2.5 times higher than SO2. A significant positive correlation between a mobile van and Haz-Scanner was observed for CO and NO2 data as well as ground-based observation and satellite data of SO2, NO2, and CO. During the strict COVID-19 lockdowns, the reduction in the vehicular and industrial exhaust significantly improved the air quality of nine towns in Lahore. This research sets the ground for further research on the quantification of total emissions and the impacts of vehicular/industrial emissions on human health. Full article
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17 pages, 8469 KiB  
Article
The Aerosol Optical Characteristics in Different Dust Events Based on a 532 nm and 355 nm Polarization Lidar in Beijing
by Zhenyi Chen, Yifeng Huang, Zhiliang Yao, Tianshu Zhang, Guangqiang Fan, Xinyue Cao and Chengli Ji
Remote Sens. 2023, 15(14), 3494; https://doi.org/10.3390/rs15143494 - 11 Jul 2023
Viewed by 961
Abstract
Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, [...] Read more.
Extreme weather events are happening more frequently as a result of global climate change. Dust storms broke out in the spring of 2017 in China and drastically impacted the local air quality. In this study, a variety of data, including aerosol vertical profiles, surface particle concentration, meteorological parameters, and MODIS–derived aerosol optical depth, as well as backward trajectory analysis, were employed to analyze two dust events from April to May in Beijing. The dust plumes were mainly concentrated below 0.8 km, with peak PM10 values of 1000 μg·m−3 and 300 μg·m−3 in the two cases. The aerosols showed different vertical distribution characteristics. The pure dust in case 1 from 4 to 5 May 2017 had a longer duration (2 days) and presented a larger aerosol extinction coefficient (2.27 km−1 at 355 nm and 1.25 km−1 at 532 nm) than that of the mixed dust in case 2 on 17 April 2017 (2.01 km−1 at 355 nm and 1.33 km−1 at 532 nm). The particle depolarization ratio (PDR) remained constant (0.24 ± 0.03 in case 1) from the surface to 0.8 km in height. In contrast, the PDR profile in the mixed dust (case 2) layer was split into two regions—large values exceeding 0.15 above 0.6 km and small values of 0.11 ± 0.03 below 0.6 km. The influence of meteorological information on aerosol distribution was also investigated, and wind was predominant through the observing period. The pure dust in case 1 was mainly from Mongolia, with strong northwest winds, while the near-surface mixed pollution was caused by the combination of long-transported sand and local emission. Furthermore, lidar-derived profiles of dust mass concentrations in the two cases were presented. This study reveals the vertical characteristics of dust aerosols in the production and dissipation of localized dust events and confirms the efficacy of thorough observations with multiple approaches from the ground to space to monitor dust events in real time. Full article
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16 pages, 5498 KiB  
Article
Long-Term Trends in Inferred Continental Background Ozone in Eastern Australia
by Matthew L. Riley, Ningbo Jiang, Hiep Nguyen Duc and Merched Azzi
Atmosphere 2023, 14(7), 1104; https://doi.org/10.3390/atmos14071104 - 01 Jul 2023
Cited by 1 | Viewed by 971
Abstract
A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone are required to understand the limits of anthropogenic emissions controlling ozone reduction. Long-term estimates of background ozone assist in characterising changes in atmospheric composition and can help [...] Read more.
A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone are required to understand the limits of anthropogenic emissions controlling ozone reduction. Long-term estimates of background ozone assist in characterising changes in atmospheric composition and can help quantify the influence of human activity on the atmosphere. Background tropospheric ozone measurements representative of continental air masses are scarce in Australia. Here, we use k-means clustering to identify a cluster of measurements from the long-term air quality monitoring station at Oakdale, NSW, which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis, we estimate background ozone representative of Eastern Australia. We find recent (2017–2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α = 0.05) trend in the mean of +1.8 (1.0–2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring. Full article
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15 pages, 8054 KiB  
Article
Temporal Variations and Spatial Distribution of Air Pollutants in Shaoxing, a City in Yangtze Delta, China Based on Mobile Monitoring Using a Sensor Package
by Gaohan Zhao, Xiaobing Pang, Jingjing Li, Bo Xing, Songhua Sun, Lang Chen, Youhao Lu, Qianqian Sun, Qianqian Shang, Zhentao Wu, Kaibin Yuan, Hai Wu, Shimin Ding, Haiyan Li and Yi Liu
Atmosphere 2023, 14(7), 1093; https://doi.org/10.3390/atmos14071093 - 29 Jun 2023
Viewed by 1162
Abstract
Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are [...] Read more.
Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel. Full article
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12 pages, 1298 KiB  
Article
Research on the Evaluation of Air Quality in Underground Coal Mines Based on a Generalized Contrastive Weighted Comprehensive Scale Index Method
by Shihang Li, Xingyue Chen, Gangcheng Peng, Muze Han, Qiaosong Guo, Jun Hou and Bohan Gao
Atmosphere 2023, 14(6), 1021; https://doi.org/10.3390/atmos14061021 - 14 Jun 2023
Cited by 1 | Viewed by 1337
Abstract
In this study, an optimization model was established based on the generalized contrastive weighted comprehensive scale index method. This model gives the evaluation indicators of SO2, NOx, CO, and TSP. It also innovatively introduces gas, the most harmful substance [...] Read more.
In this study, an optimization model was established based on the generalized contrastive weighted comprehensive scale index method. This model gives the evaluation indicators of SO2, NOx, CO, and TSP. It also innovatively introduces gas, the most harmful substance in underground coal mines, into the evaluation indicators. Moreover, the obvious hazardous concentration limit is used as the third standard concentration of the model. The scale sub-indices and the weights of SO2, NOx, CO, TSP, and gas are calculated, leading to the comprehensive scale index. Finally, the classification standard of the underground air quality is determined. An underground excavation face in Shaanxi Province is used as an example for air quality assessment. The air quality is generally poor at the points close to the working face, while that at the points far away from the working face is generally better. Furthermore, air quality optimization measures are given for areas with poor air quality. Full article
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20 pages, 3029 KiB  
Article
A Case Study of Air Quality and a Health Index over a Port, an Urban and a High-Traffic Location in Rhodes City
by Ioannis Logothetis, Christina Antonopoulou, Georgios Zisopoulos, Adamantios Mitsotakis and Panagiotis Grammelis
Air 2023, 1(2), 139-158; https://doi.org/10.3390/air1020011 - 12 Jun 2023
Viewed by 3157
Abstract
One of people’s greatest concerns about air quality degradation is its impact on human health. This work is a case study that aims to investigate the air quality and the related impact on people’s health in a coastal city over the eastern Mediterranean. [...] Read more.
One of people’s greatest concerns about air quality degradation is its impact on human health. This work is a case study that aims to investigate the air quality and the related impact on people’s health in a coastal city over the eastern Mediterranean. The analysis proceeded during a low-tourist density period, covering the days from 17 to 27 November 2022. Hourly PM2.5, NO2 and O3 concentration records from three, mobile, Air Quality Monitoring Systems (AQMS), established in an urban location, port and central area of Rhodes city, are analyzed. To investigate the impact of pollution levels on human health, the Air Quality Health Index (AQHI) is calculated. The daily and diurnal variation of pollutants’ concentration and AQHI among the different areas, as well as the relation among the ambient air pollutants and AQHI, are studied. Additionally, to investigate the impact of wind regime on the variation of pollution and AQHI levels, the hourly zonal and meridional wind-speed components, as well as the temperature at 2 m, the dew point temperature at 2 m, and the height of the boundary layer from ERA5 reanalysis, are retrieved for the region of the southeastern Mediterranean. Results show that the highest pollution level occurs in the city center of Rhodes, compared to the rest of the studied locations. In general, the findings do not show exceedances of the pollutants’ concentration according to the European Directive 2008/50/EC. Moreover, findings show that in some cases, the health risk is classified from Low to Moderate in terms of AQHI. The analysis indicates that the climate conditions affect the pollutants’ concentration due to dispersion, and likely, the atmospheric transport of pollutants. Finally, this work aims to improve the knowledge regarding the air quality of southeastern Greece, promoting the framework for the green and sustainable development of the South Aegean Sea. Full article
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15 pages, 15054 KiB  
Technical Note
Multi-Satellite Detection of Long-Range Transport and Transformation of Atmospheric Emissions from the Hunga Tonga-Hunga Ha’apai Volcano
by Qinqin Liu, Lu Gui, Jianqiang Liu, Guido Ventura, Qingzhou Yang, Zhongting Wang, Ziyue Tang, Minghui Tao and Xuhui Shen
Remote Sens. 2023, 15(10), 2661; https://doi.org/10.3390/rs15102661 - 19 May 2023
Viewed by 1481
Abstract
Large volumes of atmospheric pollutants injected into the troposphere and stratosphere from volcanic eruptions can exert significant influence on global climate. Through utilizing multi-satellite observations, we present a large-scale insight into the long-range transport and transformation of sulfur dioxide (SO2) emissions [...] Read more.
Large volumes of atmospheric pollutants injected into the troposphere and stratosphere from volcanic eruptions can exert significant influence on global climate. Through utilizing multi-satellite observations, we present a large-scale insight into the long-range transport and transformation of sulfur dioxide (SO2) emissions from the Hunga Tonga-Hunga Ha’apai eruption on 15 January 2022. We found that the transport of volcanic emissions, along with the transformation from SO2 to sulfate aerosols, lasted for two months after the Tongan eruption. The emitted volume of SO2 from the volcano eruption was approximately 183 kilotons (kt). Both satellite observation and numerical simulation results show that the SO2 and volcanic ash plumes moved westward at a rate of one thousand kilometers per day across the Pacific and Atlantic Ocean regions and that SO2 transformation in the atmosphere lasted for half a month. The transport and enhancement of aerosols is related to the conversion of SO2 to sulfate. CALIPSO lidar observations show that SO2 reached an altitude of 25–30 km and transformed into sulfate in the stratosphere after 29 January. Sulfate aerosols in the stratosphere deceased gradually with transport and fell back to the background level after two months. Our study shows that satellite observations give a good characterization of volcanic emissions, transport, and SO2-sulfate conversion, which can provide an essential constraint for climate modeling. Full article
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14 pages, 5880 KiB  
Article
Changes in Air Quality, Meteorology and Energy Consumption during the COVID-19 Lockdown and Unlock Periods in India
by Jayanarayanan Kuttippurath, Vikas Kumar Patel, Gopalakrishna Pillai Gopikrishnan and Hamza Varikoden
Air 2023, 1(2), 125-138; https://doi.org/10.3390/air1020010 - 04 May 2023
Cited by 1 | Viewed by 2466
Abstract
The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. [...] Read more.
The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources. Full article
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19 pages, 4215 KiB  
Article
Air Pollution Prediction Based on Discrete Wavelets and Deep Learning
by Ying Shu, Chengfu Ding, Lingbing Tao, Chentao Hu and Zhixin Tie
Sustainability 2023, 15(9), 7367; https://doi.org/10.3390/su15097367 - 28 Apr 2023
Cited by 3 | Viewed by 1112
Abstract
Air pollution directly affects people’s life and work and is an important factor affecting public health. An accurate prediction of air pollution can provide a credible foundation for determining the social activities of individuals. Scholars have, thus, proposed a variety of models and [...] Read more.
Air pollution directly affects people’s life and work and is an important factor affecting public health. An accurate prediction of air pollution can provide a credible foundation for determining the social activities of individuals. Scholars have, thus, proposed a variety of models and techniques for predicting air pollution. However, most of these studies are focused on the prediction of individual pollution factors and perform poorly when multiple pollutants need to be predicted. This paper offers a DW-CAE model that may strike a balance between overall accuracy and local univariate prediction accuracy in order to observe the trend of air pollution more comprehensively. The model combines deep learning and signal processing techniques by employing discrete wavelet transform to obtain the high and low-frequency features of the target sequence, designing a feature extraction module to capture the relationship between the variables, and feeding the resulting feature matrix to an LSTM-based autoencoder for prediction. The DW-CAE model was used to make predictions on the Beijing PM2.5 dataset and the Yining air pollution dataset, and its prediction accuracy was compared to that of eight baseline models, such as LSTM, IMV-Full, and DARNN. The evaluation results indicate that the proposed DW-CAE model is more accurate than other baseline models at predicting single and multiple pollution factors, and the R2 of each variable is all higher than 93% for the overall prediction of the six air pollutants. This demonstrates the efficacy of the DW-CAE model, which can give technical and theoretical assistance for the forecast, prevention, and control of overall air pollution. Full article
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18 pages, 16904 KiB  
Article
A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk
by Huiyun Ma, Zengwei Liu, Kun Jiang, Bingbo Jiang, Huihui Feng and Shuaifeng Hu
Remote Sens. 2023, 15(9), 2331; https://doi.org/10.3390/rs15092331 - 28 Apr 2023
Cited by 2 | Viewed by 1458
Abstract
Satellite remote sensing provides a potential technology for detecting fog at dawn and dusk on a large scale. However, the spectral characteristics of fog at dawn and dusk are similar to those of the ground surface, which makes satellite-based fog detection difficult. With [...] Read more.
Satellite remote sensing provides a potential technology for detecting fog at dawn and dusk on a large scale. However, the spectral characteristics of fog at dawn and dusk are similar to those of the ground surface, which makes satellite-based fog detection difficult. With the aid of time-series datasets from the Himawari-8 (H8)/AHI, this study proposed a novel algorithm of the self-adaptive threshold of visual background extractor (ST-ViBe) model for satellite fog detection at dawn and dusk. Methodologically, the background model was first built using the difference between MIR and TIR (BTD) and the local binary similarity patterns (LBSP) operator. Second, BTD and scale invariant local ternary pattern (SILTP) texture features were coupled to form scene factors, and the detection threshold of each pixel was determined adaptively to eliminate the influence of the solar zenith angles. The background model was updated rapidly by accelerating the updating rate and increasing the updating quantity. Finally, the residual clouds were removed with the traditional cloud removal method to achieve accurate detection of fog at dawn and dusk over a large area. The validation results demonstrated that the ST-ViBe algorithm could detect fog at dawn and dusk precisely, and on a large scale. The probability of detection, false alarm ratio, and critical success index were 72.5%, 18.5%, 62.4% at dawn (8:00) and 70.6%, 33.6%, 52.3% at dusk (17:00), respectively. Meanwhile, the algorithm mitigated the limitations of the traditional algorithms, such as illumination mutation, missing detection, and residual shadow. The results of this study could guide satellite fog detection at dawn and dusk and improve the detection of similar targets. Full article
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10 pages, 827 KiB  
Communication
Air Pollution in South Texas: A Short Communication of Health Risks and Implications
by Sai Deepak Pinakana, Esmeralda Mendez, Ismaila Ibrahim, Md. Salahuddin Majumder and Amit U. Raysoni
Air 2023, 1(2), 94-103; https://doi.org/10.3390/air1020008 - 30 Mar 2023
Cited by 1 | Viewed by 3108
Abstract
Air pollution is a major public health concern. The region of South Texas in the United States has experienced high levels of air pollution in recent years due to an increase in population, cross-border trade between the U.S.A. and Mexico, and high vehicular [...] Read more.
Air pollution is a major public health concern. The region of South Texas in the United States has experienced high levels of air pollution in recent years due to an increase in population, cross-border trade between the U.S.A. and Mexico, and high vehicular activity. This review assesses the relationships between human health and air pollution in South Texas. A thorough scientific search was performed using PubMed, Science Direct, and ProQuest, with most of the literature focusing on the source apportionment of particulate matter that is 2.5 microns or less in width (PM2.5), Carbon Dioxide (CO2), carbon monoxide (CO), Black Carbon (BC), and associated health risks for children and pregnant women. Findings from the source apportionment studies suggest the role of industries, automobiles emissions, agricultural burning, construction work, and unpaved roads in the overall deterioration of air quality and deleterious health effects, such as respiratory and cardiovascular diseases. This review demonstrates the pressing need for more air pollution and health effects studies in this region, especially the Brownsville–Harlingen–McAllen metropolitan area. Full article
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19 pages, 3954 KiB  
Article
Structural Characteristics and Evolution Trend of Collaborative Governance of Air Pollution in “2 + 26” Cities from the Perspective of Social Network Analysis
by Jiancheng Li
Sustainability 2023, 15(7), 5943; https://doi.org/10.3390/su15075943 - 29 Mar 2023
Cited by 2 | Viewed by 1170
Abstract
The regional and complex air pollution problem has become a major bottleneck restricting the sustainable development of regional economies and societies. Constructing a regional collaborative governance network has become a key solution to solving the cross-regional air pollution problem. By performing a social [...] Read more.
The regional and complex air pollution problem has become a major bottleneck restricting the sustainable development of regional economies and societies. Constructing a regional collaborative governance network has become a key solution to solving the cross-regional air pollution problem. By performing a social network analysis, this paper analyzes the overall structure, internal characteristics, and evolution trend of the collaborative governance network of regional air pollution by selecting the data samples of the “2 + 26” cities from 2017 to 2021. The study found that the excellent results of air pollution control in Beijing–Tianjin–Hebei and its surrounding areas are due to precise and efficient collaboration among the “2 + 26” cities. The collaborative network formed by “2 + 26” cities based on the joint initiation of severe weather emergency responses is an important measure that can help to effectively control regional air pollution problems. There is a distinct difference in the collaborative pattern in the “2 + 26” cities air pollution collaborative governance model, showing a nested-difference network structure. Full article
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23 pages, 16107 KiB  
Article
Assessment of Air Pollution Levels during Sugarcane Stubble Burning Event in La Feria, South Texas, USA
by Sai Deepak Pinakana, Edward Robles, Esmeralda Mendez and Amit U. Raysoni
Pollutants 2023, 3(2), 197-219; https://doi.org/10.3390/pollutants3020015 - 24 Mar 2023
Cited by 3 | Viewed by 1932
Abstract
Agricultural stubble burning is the third largest source of air pollution after vehicular and industrial emissions. Fine particulate matter (PM2.5), volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen dioxide (NO2), and black carbon (BC) are some of the pollutants [...] Read more.
Agricultural stubble burning is the third largest source of air pollution after vehicular and industrial emissions. Fine particulate matter (PM2.5), volatile organic compounds (VOCs), carbon monoxide (CO), nitrogen dioxide (NO2), and black carbon (BC) are some of the pollutants emitted during such burning events. The Lower Rio Grande Valley (RGV) region of South Texas is a major hub of agricultural activity, and sugarcane farming is one of them. Unfortunately, this activity results in episodic events of high air pollution in this low-resourced, Hispanic/Latino majority region of the U.S.–Mexico border. This study presents results from a sugarcane site in La Feria, South Texas, where the air quality was monitored before, during, and after the sugarcane stubble burning. Various parameters were monitored on an hourly basis from 24 February 2022 to 4 April 2022. Our results demonstrate high levels of all the monitored pollutants during the burning phase in contrast to the pre- and post-burning period. The black carbon levels went up to 6.43 µg m−3 on the day of burning activity. An increase of 10%, 11.6%, 25.29%, 55%, and 67.57% was recorded in the PM1, PM2.5, PM10, Black Carbon, and CO levels, respectively, during the burning period in comparison with the total study period. The absorption Ångström exponent value reached a maximum value of 2.03 during the burning activity. ThePM2.5/PM10 ratio was 0.87 during the burning activity. This study also highlights the importance for continuous monitoring of air quality levels due to stubble burning in the Lower Rio Grande Valley Region of South Texas. Full article
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10 pages, 1764 KiB  
Article
Deposition of Potassium on Chimney Wall from Wood Stove Smoke: Implication for the Influence of Domestic Biomass Burning on Atmospheric Aerosols
by Kimitaka Kawamura, Bhagawati Kunwar, Dhananjay Kumar Deshmukh, Petr Vodička and Md. Mozammel Haque
Atmosphere 2023, 14(3), 484; https://doi.org/10.3390/atmos14030484 - 28 Feb 2023
Viewed by 1378
Abstract
Based on the field studies of biomass burning plumes in Alaska, we hypothesized that potassium (K) may be significantly scavenged, during wood stove burning, as deposits on the inner wall of the chimney where the temperature decreases with the height. To test this [...] Read more.
Based on the field studies of biomass burning plumes in Alaska, we hypothesized that potassium (K) may be significantly scavenged, during wood stove burning, as deposits on the inner wall of the chimney where the temperature decreases with the height. To test this hypothesis, we analyzed chimney deposit samples collected from the inner wall of a chimney (6 m long) for the measurement of major ions and anhydrosugars including levoglucosan (Lev). Concentrations of K were found to be highest in the lower part of the chimney with a decreasing trend with height, whereas Lev showed an opposite trend with the lowest concentrations near the bottom of the chimney and an increase with height. We detected an anti-correlation between the two components in the chimney deposits, confirming that K is largely scavenged as a deposit within the chimney while Lev is significantly emitted to the ambient air. We propose that, using K/Lev mass ratios, the relative contributions of open fires and domestic wood burning to ambient aerosols can be evaluated. Full article
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14 pages, 4985 KiB  
Article
Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models
by Ioannis D. Apostolopoulos, George Fouskas and Spyros N. Pandis
Atmosphere 2023, 14(2), 368; https://doi.org/10.3390/atmos14020368 - 13 Feb 2023
Cited by 1 | Viewed by 1682
Abstract
Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different [...] Read more.
Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R2 improved from 0.22 to 0.86. Similar results were obtained for O3, wherein the mean error was reduced from 13 to 4.3 ppb and R2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants. Full article
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