Accessing and Analyzing Air Quality and Atmospheric Environment

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Air air	-	-	2023	15.0 days *	1000 CHF	Submit
Atmosphere atmosphere	3.110	3.7	2010	14.7 Days	2000 CHF	Submit
Remote Sensing remotesensing	5.349	7.4	2009	19.7 Days	2500 CHF	Submit
Sustainability sustainability	3.889	5.0	2009	17.7 Days	2200 CHF	Submit
Pollutants pollutants	-	-	2021	16.8 Days	1000 CHF	Submit

Open AccessTechnical Note

Multi-Satellite Detection of Long-Range Transport and Transformation of Atmospheric Emissions from the Hunga Tonga-Hunga Ha’apai Volcano

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Remote Sens. 2023, 15(10), 2661; https://doi.org/10.3390/rs15102661 - 19 May 2023

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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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Changes in Air Quality, Meteorology and Energy Consumption during the COVID-19 Lockdown and Unlock Periods in India

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Jayanarayanan Kuttippurath

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Vikas Kumar Patel

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Gopalakrishna Pillai Gopikrishnan

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Hamza Varikoden

Air 2023, 1(2), 125-138; https://doi.org/10.3390/air1020010 - 04 May 2023

Viewed by 864

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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Air Pollution Prediction Based on Discrete Wavelets and Deep Learning

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Sustainability 2023, 15(9), 7367; https://doi.org/10.3390/su15097367 - 28 Apr 2023

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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

{PM}_{2.5}

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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

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Remote Sens. 2023, 15(9), 2331; https://doi.org/10.3390/rs15092331 - 28 Apr 2023

Viewed by 573

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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessCommunication

Air Pollution in South Texas: A Short Communication of Health Risks and Implications

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Sai Deepak Pinakana

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Esmeralda Mendez

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Ismaila Ibrahim

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Md. Salahuddin Majumder

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Amit U. Raysoni

Air 2023, 1(2), 94-103; https://doi.org/10.3390/air1020008 - 30 Mar 2023

Viewed by 862

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 (PM_2.5), 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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Structural Characteristics and Evolution Trend of Collaborative Governance of Air Pollution in “2 + 26” Cities from the Perspective of Social Network Analysis

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Jiancheng Li

Sustainability 2023, 15(7), 5943; https://doi.org/10.3390/su15075943 - 29 Mar 2023

Viewed by 564

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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Assessment of Air Pollution Levels during Sugarcane Stubble Burning Event in La Feria, South Texas, USA

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Pollutants 2023, 3(2), 197-219; https://doi.org/10.3390/pollutants3020015 - 24 Mar 2023

Viewed by 682

Abstract

Agricultural stubble burning is the third largest source of air pollution after vehicular and industrial emissions. Fine particulate matter (PM_2.5), 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 (PM_2.5), 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, PM_2.5, 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. ThePM_2.5/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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Deposition of Potassium on Chimney Wall from Wood Stove Smoke: Implication for the Influence of Domestic Biomass Burning on Atmospheric Aerosols

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Kimitaka Kawamura

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Bhagawati Kunwar

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Dhananjay Kumar Deshmukh

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Petr Vodička

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Md. Mozammel Haque

Atmosphere 2023, 14(3), 484; https://doi.org/10.3390/atmos14030484 - 28 Feb 2023

Viewed by 710

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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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Open AccessArticle

Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models

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Ioannis D. Apostolopoulos

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George Fouskas

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Spyros N. Pandis

Atmosphere 2023, 14(2), 368; https://doi.org/10.3390/atmos14020368 - 13 Feb 2023

Viewed by 999

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, PM_2.5, 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

(This article belongs to the Topic Accessing and Analyzing Air Quality and Atmospheric Environment)

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