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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 20894

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Dr. Wei Tang

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Guest Editor

Atmospheric Environment Institute, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
Interests: air quality modeling; inverse model; satellite data; source apportionment
Special Issues, Collections and Topics in MDPI journals

Dr. Cheol-Hee Kim

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Guest Editor

Department of Atmospheric Sciences, Pusan National University, Busan 46421, Republic of Korea
Interests: air pollution

Dr. Fan Meng

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Guest Editor

Asia Center for Air Pollution Research, Niigata 950-2144, Japan
Interests: atmospheric modeling and forecasting; emission inventory; acid rain; source apportionment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide recent research activities in the field of air pollution in Asia, including particulate matter, ozone and other air pollutants. In Asia, anthropogenic emissions have sharply increased in recent years as a result of high economic growth with rapid industrialization, urbanization, and motorization, as well as agricultural activities. Enhanced emissions are always associated with the degradation of air quality, and have negative impacts on human health and climate. A great deal of research has recently been conducted in the field of air pollution mitigation and climate change in Asia. The contents of this Special Issue favor, but are not limited to, air quality measurements; air quality modeling involving mainstream models such as CALPUFF, CMAQ, CAMx, etc.; source apportionment analysis and control policy; satellite data application; and climate change. The latest applications of big data and deep learning technology in the atmospheric field are also favored and considered.

Topics of interest for the Special Issue include, but are not limited to:

Air quality measurement and modeling.
Source apportionment analysis and control policy.
The applications of satellite data in air quality.
The applications of big data and deep learning technology in the atmospheric field.
Interaction between climate change and air quality.

Dr. Wei Tang
Dr. Cheol-Hee Kim
Dr. Fan Meng
Guest Editors

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Keywords

air pollution
source apportionment
satellite data
climate change
deep learning technology

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Published Papers (10 papers)

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Research

18 pages, 4491 KiB

Open AccessArticle

Spatiotemporal Analysis of Urban Growth and PM_2.5 Concentrations in Sylhet, Bangladesh

by Mizanur Rahman, Lei Meng, Adam J. Mathews and Steven Bertman

Atmosphere 2024, 15(11), 1305; https://doi.org/10.3390/atmos15111305 - 30 Oct 2024

Cited by 3 | Viewed by 2171

Abstract

Environmental impacts of urbanization, such as increased air pollution, pose significant challenges for developing countries. This study examines land cover changes and their relationship with particulate matter _2.5 (PM_2.5) concentrations in two upazilas of Bangladesh from 2001 to 2019 using GIS and remote sensing techniques. Results show significant urban expansion (i.e., increase in built-up area) in both upazilas, corresponding with increasing PM_2.5 levels. Linear regression reveals correlations between land cover types and PM_2.5 levels. Mixed forests and waterbodies tend to be negatively associated with PM_2.5 concentrations; on the other hand, built-up and barren land show a positive correlation with PM_2.5. The most significant increase in built-up land and PM_2.5 was in Madhabpur, indicating urgent environmental and health issues. This study emphasizes the critical role of sustainable urban planning and environmental conservation in mitigating urbanization’s adverse effects on air quality, advocating for preserving natural landscapes to maintain ecological balance, protecting urban health, and providing policymakers with insights to develop strategies addressing urban expansion and air pollution. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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20 pages, 3297 KiB

Open AccessArticle

Assessing the Air Pollution Tolerance Index of Urban Plantation: A Case Study Conducted along High-Traffic Roadways

by Zunaira Asif and Wen Ma

Atmosphere 2024, 15(6), 659; https://doi.org/10.3390/atmos15060659 - 30 May 2024

Cited by 1 | Viewed by 1599

Abstract

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm², peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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22 pages, 5168 KiB

Open AccessArticle

Characteristics of Trace Metal Elements in Ambient Sub-Micron Particulate Matter in a Coastal Megacity of Northern China Influenced by Shipping Emissions from 2018 to 2022

by Jinhua Du, Ziyang Liu, Wenxin Tao, Ting Wang, Jiaojiao Zhao, Weiwei Gong, Yue Li, Lian Xue, Jianli Yang, Chaolong Wang, Houyong Zhang, Fei Wang, Yingjie Sun and Yisheng Zhang

Atmosphere 2024, 15(3), 264; https://doi.org/10.3390/atmos15030264 - 22 Feb 2024

Cited by 2 | Viewed by 1299

Abstract

Various shipping emission restrictions have recently been implemented locally and nationally, which might mitigate their impacts on regional air quality, climate change, and human health. In this study, the daily trace metal elements in PM₁ were measured in a coastal megacity in Northern China, from autumn to winter from 2018 to 2022, spanning DECA 1.0 (domestic emission control area), DECA 2.0, IMO 2020, and Pre-OWG Beijing 2022 stages. The trace element changes of V, Ni, Pb, and Zn in PM₁ were analyzed. The concentrations of V declined with shipping emission regulations implemented in 2018–2022 at 3.61 ± 3.01, 1.07 ± 1.04, 0.84 ± 0.62, and 0.68 ± 0.61 ng/m³, respectively, with the V/Ni ratio decreasing at 1.14 ± 0.79, 0.93 ± 1.24, 0.35 ± 0.24, and 0.22 ± 0.18. The V/Ni ratio was dominated by the shipping emissions in the DECA 1.0 stage but has been more affected by the inland sources since DECA 2.0. The V/Ni ratio of local transport air mass was higher than that of long-distance transportation, indicating that some ships were still using high-sulfur fuel oil, especially for the ships 12 nautical miles from the coastline. The multiple linear regression model showed a better fit using V as a tracer for ship emission sources of ambient SO₂ in the DECA 1.0 stage, while the indication effect reduced since DECA 2.0. The V and V/Ni ratios should be carefully used as indicators of ship sources as more vessels will use clean fuels for energy, and the contribution of inland sources to V and Ni will gradually increase. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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18 pages, 4683 KiB

Open AccessArticle

Ambient Ozone and Fine Particular Matter Pollution in a Megacity in South China: Trends, Concurrent Pollution, and Health Risk Assessment

by Pei Zeng, Xiaobo Huang, Min Yan, Zhuoyun Zheng, Zhicheng Qiu, Long Yun, Chuxiong Lin and Li Zhang

Atmosphere 2023, 14(12), 1806; https://doi.org/10.3390/atmos14121806 - 9 Dec 2023

Cited by 5 | Viewed by 2415

Abstract

Over the past several years, Shenzhen’s air quality has significantly improved despite increased ground-level ozone (O₃) and the challenges in reducing fine particulate matter (PM_2.5). We investigated concentration trends, concurrent pollution features, and long-term exposure health risks to enhance our understanding of the characteristics of O₃ and PM_2.5 pollution. From 2016 to 2022, there was a decrease in PM_2.5 levels, but an increase in O₃. Additionally, the premature mortality attributed to long-term air pollution exposure decreased by 20.1%. High-O₃-and-PM_2.5 days were defined as those when the MDA8 O₃ ≥ 160 μg m^–3 and PM_2.5 ≥ 35 μg m^–3. Significantly higher levels of O₃, PM_2.5, nitrogen dioxide (NO₂), O_X (O_X = O₃ + NO₂), and sulfur dioxide (SO₂) were observed on high-O₃-and-PM_2.5 days. Vehicle emissions were identified as the primary anthropogenic sources of volatile organic compounds (VOCs), contributing the most to VOCs (58.4 ± 1.3%), O₃ formation (45.3 ± 0.6%), and PM_2.5 formation (46.6 ± 0.4%). Cities in Guangdong Province around Shenzhen were identified as major potential source regions of O₃ and PM_2.5 during high-O₃-and-PM_2.5 days. These findings will be valuable in developing simultaneous pollution control strategies for PM_2.5 and O₃ in Shenzhen. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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18 pages, 4474 KiB

Open AccessArticle

Personal Exposure to Fine Particulate Air Pollution among Brick Workers in Nepal

by James D. Johnston, Scott C. Collingwood, James D. LeCheminant, Neil E. Peterson, Paul R. Reynolds, Juan A. Arroyo, Andrew J. South, Clifton B. Farnsworth, Ryan T. Chartier, Lindsey N. Layton, James H. Lu, Marli S. Penrod, Seshananda Sanjel and John D. Beard

Atmosphere 2023, 14(12), 1783; https://doi.org/10.3390/atmos14121783 - 2 Dec 2023

Cited by 2 | Viewed by 2461

Abstract

Prior studies suggest brick workers in Nepal may be chronically exposed to hazardous levels of fine particulate matter (PM_2.5) from ambient, occupational, and household sources. However, findings from these studies were based on stationary monitoring data, and thus may not reflect a worker’s individual exposures. In this study, we used RTI International’s MicroPEMs to collect 24 h PM_2.5 personal breathing zone (PBZ) samples among brick workers (n = 48) to estimate daily exposures from ambient, occupational, and household air pollution sources. Participants were sampled from five job categories at one kiln. The geometric mean (GM) PM_2.5 exposure across all participants was 116 µg/m³ (95% confidence interval [CI]: 94.03, 143.42). Job category was significantly (p < 0.001) associated with PBZ PM_2.5 concentrations. There were significant pairwise differences in geometric mean (GM) PBZ PM_2.5 concentrations among workers in administration (GM: 47.92, 95% CI: 29.81, 77.03 µg/m³) vs. firemen (GM: 163.46, 95 CI: 108.36, 246.58 µg/m³, p = 0.003), administration vs. green brick hand molder (GM: 163.35, 95% CI: 122.15, 218.46 µg/m³, p < 0.001), administration vs. top loader (GM: 158.94, 95% CI: 102.42, 246.66 µg/m³, p = 0.005), firemen vs. green brick machine molder (GM: 73.18, 95% CI: 51.54, 103.90 µg/m³, p = 0.03), and green brick hand molder vs. green brick machine molder (p = 0.008). Temporal exposure trends suggested workers had chronic exposure to hazardous levels of PM_2.5 with little to no recovery period during non-working hours. Multi-faceted interventions should focus on the control of ambient and household air pollution and tailored job-specific exposure controls. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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17 pages, 3372 KiB

Open AccessArticle

by Hui Yi, Dan Li, Jianrong Li, Lingling Xu, Zhongwen Huang, Hang Xiao and Lei Tong

Atmosphere 2023, 14(11), 1594; https://doi.org/10.3390/atmos14111594 - 24 Oct 2023

Viewed by 1664

Abstract

Atmospheric mercury and water-soluble inorganic ions (WSIIs) are commonly observable airborne pollutants in the atmosphere that may have similar emission sources. In this study, the interrelated pollution characteristics of atmospheric speciated mercury and WSIIs were studied using a Piper diagram, correlation analysis, pollution episode analysis and potential source contribution function (PSCF) techniques. Also, an empirical regression equation for predicting the temporal variation in gaseous elemental mercury (GEM) was constructed. The results showed that the concentrations of GEM and particle-bound mercury (PBM) roughly increased with the increasing percentage values of NH₄⁺ in cationic normality, and exponentially increased with the decreasing percentage values of Na⁺ + Mg²⁺ in cationic normality. Correlation analysis revealed that the atmospheric speciated mercury was positively (p < 0.01) correlated with most water-soluble inorganic ions, especially for GEM, which was closely correlated with NO₂, NO_x, CO, PM_2.5, NO₃⁻ SO₄²⁻, NH₄⁺ and K⁺ (r > 0.5, p < 0.01), indicating that the emission sources of GEM were related to fossil fuel and biomass combustion, industrial activities, and traffic exhausts. Pollution episode analysis showed that PM_2.5, WSIIs (including SO₄²⁻, NO₃⁻, NH₄⁺, K⁺ and Cl⁻), SO₂ and NO₂ generally exhibited synchronous variations with GEM and PBM, and positive correlations were observed between GEM and PM_2.5, SO₄²⁻, NO₃⁻, NH₄⁺, K⁺, Cl⁻, SO₂ and NO₂ (r = 0.35–0.74, p-value < 0.01). In addition, the potential source region of GEM was similar to that of PM_2.5, SO₄²⁻, NO₃⁻, NH₄⁺, K⁺ and Ca²⁺. Based on the above findings, a satisfactory empirical regression equation, with PM_2.5, NO_x, CO and the percentage value of Na⁺ + Mg²⁺ in cationic normality as independent variables for GEM simulation, was constructed. The result showed that the variation in GEM concentrations could be predicted well by these variables. This model could serve as a potential substitute tool for GEM measurement in the future. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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13 pages, 3362 KiB

Open AccessArticle

Effect of Surface Methane Controls on Ozone Concentration and Rice Yield in Asia

by Kenichi Tatsumi

Atmosphere 2023, 14(10), 1558; https://doi.org/10.3390/atmos14101558 - 13 Oct 2023

Viewed by 1623

Abstract

Surface methane (CH₄) is a significant precursor of tropospheric ozone (O₃), a greenhouse gas that detrimentally impacts crops by suppressing their physiological processes, such as photosynthesis. This relationship implies that CH₄ emissions can indirectly harm crops by increasing troposphere O₃ concentrations. While this topic is important, few studies have specifically examined the combined effects of CH₄ and CH₄-induced O₃ on rice yield and production. Utilizing the GEOS-Chem model, we assessed the potential reduction in rice yield and production in Asia against a 50% reduction in anthropogenic CH₄ emissions relative to the 2010 base year. Based on O₃ exposure metrics, the results revealed an average relative yield loss of 9.5% and a rice production loss of 45,121 kilotons (Kt) based on AOT40. Regions such as the India-Gangetic Plain and the Yellow River basin were particularly affected. This study determined that substantial reductions in CH₄ concentrations can prevent significant rice production losses. Specifically, curbing CH₄ emissions in the Beijing-Tianjin-Hebei region could significantly diminish the detrimental effects of O₃ on rice yields in China, Korea, and Japan. In summary, decreasing CH₄ emissions is a viable strategy to mitigate O₃-induced reductions in rice yield and production in Asia. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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14 pages, 6115 KiB

Open AccessArticle

Analysis and Research on the Differences in Observed Data of Sand–Dust Weather between China and Mongolia

by Yuan You, Linchang An, Siteng Li, Bihui Zhang and Jianzhong Zhang

Atmosphere 2023, 14(9), 1401; https://doi.org/10.3390/atmos14091401 - 5 Sep 2023

Cited by 2 | Viewed by 1491

Abstract

The difference in meteorological factors (such as weather phenomena, wind speed, and visibility) of sand–dust weather between China and Mongolia from 2011 to 2021 was analyzed using meteorological observational data and international exchange of meteorological observation data. Additionally, consistency analysis was performed by integrating satellite retrieval products with meteorological observation data. The results showed that the average annual frequency of sand–dust weather in Mongolia was significantly higher than that in China. In China, the sand–dust weather was mainly characterized by floating dust or blowing dust, while in Mongolia, it was primarily characterized by blowing dust or a sand and dust storm. The average annual wind speed and visibility during sand–dust weather in Mongolia were relatively higher than those in China. Based on the dust grade standard of China, when the floating dust occurred in Mongolia, there were cases with wind speed > level 3 and visibility > 10 km; when the blowing dust or sand and dust storm occurred in Mongolia, there were cases with wind speed ≤ level 3 and visibility > 10 km. In China, the sand–dust weather mainly occurred in the spring, while the sand-dust weather occurred frequently throughout the year in Mongolia. The number of days with dust lasting for 2 days or more in Mongolia exceeded that of China, and Mongolia had a significant impact on the sand–dust weather in China. According to the ground observation data and satellite retrieve products during the dust events, all dust events that significantly affected China and Mongolia during the same period from 2021 to 2022 were classified into three categories; among them, the proportion of types of large-scale sand–dust weather phenomena observed by both satellite and ground observation stations was significantly higher (6 times). By integrating ground observation data and satellite retrieval products and following the dust grade standard of China, the consistent correction of sand–dust weather phenomena was carried out. This laid the foundation for the future development of international dust grade standards and provided technological support for improved dust forecasting services in the Asian region. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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13 pages, 1928 KiB

Open AccessArticle

Stable Isotopes Unravel the Sources and Transport of PM_2.5 in the Yangtze River Delta, China

by Han Zhang, Zhenyu Hong, Lai Wei, Barry Thornton, Youwei Hong, Jinsheng Chen and Xian Zhang

Atmosphere 2023, 14(7), 1120; https://doi.org/10.3390/atmos14071120 - 6 Jul 2023

Cited by 1 | Viewed by 1784

Abstract

To understand the sources and migration pattern of PM_2.5 in the Yangtze River Delta (YRD), China, the total carbon (TC) and total nitrogen (TN) concentrations and the corresponding stable isotope ratios (δ¹³C_TC and δ¹⁵N_TN) were determined in aerosol samples simultaneously collected from August 2014 to April 2015 at three different locations (Shanghai, Ningbo, Nanjing). Ningbo and Shanghai are geographically closer, the research results precisely divide Nanjing and the other two cities into two categories. Nanjing has a higher proportion of nitrogen in PM_2.5 (13.2–15.3%) than Shanghai and Ningbo (8.6–12.6%), and the correlation analysis shows that nitrogen components (mainly ammonium nitrogen) might be the main driving force for the formation of PM_2.5. The isotopes were proven to be sensitive sensors to reflect the impact of special events on PM_2.5. For example, compared to other seasons, δ¹³C_TC in autumn in the three cities are relatively depleted, indicating an input from biomass combustion to PM_2.5 at this time. On New Year’s Eve, three cities simultaneously observed enriched δ¹³C_TC due to the burning of fireworks. During the Qingming Festival, abnormally depleted nitrogen isotope ratios were observed, reflecting the vehicle exhaust pollution caused by people’s short travel. Isotopes are also used to trace the transport process of PM_2.5. Postponing the sampling date in Nanjing by one day increased the linear fit (r²) of δ¹³C_TC between Nanjing and Ningbo from 0.03 to 0.75, while that of δ¹⁵N_TN improved from 0.16 to 0.63, which means PM_2.5 might transport from Nanjing to Shanghai and Ningbo, and the transfer time takes one day. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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24 pages, 14143 KiB

Open AccessArticle

PM_2.5 Concentration Forecasting Using Weighted Bi-LSTM and Random Forest Feature Importance-Based Feature Selection

by Baekcheon Kim, Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jinyong Kim and Sungshin Kim

Atmosphere 2023, 14(6), 968; https://doi.org/10.3390/atmos14060968 - 1 Jun 2023

Cited by 7 | Viewed by 2893

Abstract

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM

_{2.5}

enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM

_{2.5}

can result in respiratory, [...] Read more.

Particulate matter (PM) in the air can cause various health problems and diseases in humans. In particular, the smaller size of PM

_{2.5}

enable them to penetrate deep into the lungs, causing severe health impacts. Exposure to PM

_{2.5}

can result in respiratory, cardiovascular, and allergic diseases, and prolonged exposure has also been linked to an increased risk of cancer, including lung cancer. Therefore, forecasting the PM

_{2.5}

concentration in the surrounding is crucial for preventing these adverse health effects. This paper proposes a method for forecasting the PM

_{2.5}

concentration after 1 h using bidirectional long short-term memory (Bi-LSTM). The proposed method involves selecting input variables based on the feature importance calculated by random forest, classifying the data to assign weight variables to reduce bias, and forecasting the PM

_{2.5}

concentration using Bi-LSTM. To compare the performance of the proposed method, two case studies were conducted. First, a comparison of forecasting performance according to preprocessing. Second, forecasting performance between deep learning (long short-term memory, gated recurrent unit, and Bi-LSTM) and conventional machine learning models (multi-layer perceptron, support vector machine, decision tree, and random forest). In case study 1, The proposed method shows that the performance indices (RMSE: 3.98%p, MAE: 5.87%p, RRMSE: 3.96%p, and R

^{2}

:0.72%p) are improved because weights are given according to the input variables before the forecasting is performed. In case study 2, we show that Bi-LSTM, which considers both directions (forward and backward), can effectively forecast when compared to conventional models (RMSE: 2.70, MAE: 0.84, RRMSE: 1.97, R

^{2}

: 0.16). Therefore, it is shown that the proposed method can effectively forecast PM

_{2.5}

even if the data in the high-concentration section is insufficient. Full article

(This article belongs to the Special Issue Air Pollution in Asia)

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