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16 pages, 1761 KiB

Open AccessArticle

Characteristics of PM_2.5 Chemical Species in 23 Chinese Cities Identified Using a Vehicular Platform

by Hui Chen, Jingjing Liu, Peizhi Wang, Xiao Lin, Jingjin Ma and Chunying Wang

Sustainability 2024, 16(6), 2340; https://doi.org/10.3390/su16062340 - 12 Mar 2024

Cited by 1 | Viewed by 774

Abstract

PM_2.5 pollution remains a significant concern in China due to its adverse environmental and health implications. This study aims to explore in depth the differences in the causes of PM_2.5 pollution between some regions in China based on high temporal resolution [...] Read more.

PM_2.5 pollution remains a significant concern in China due to its adverse environmental and health implications. This study aims to explore in depth the differences in the causes of PM_2.5 pollution between some regions in China based on high temporal resolution PM_2.5 component information. We used a particulate matter chemical composition vehicle (PMCCV) as a mobile monitoring platform which travelled among 23 cities in China from March 2018 to December 2019 to collect PM_2.5 concentrations and chemical composition data. Observations revealed that PM_2.5 concentrations were notably higher in northern cities compared than their southern counterparts. Seasonal variation was evident, with peak concentrations during winter and troughs during summer. In regions experiencing severe winter pollution, such as Hebei and Shanxi (HB/SX), organic matter (OM) emerged as the dominant contributor (47.3%), escalating with increasing PM_2.5 concentrations. OM significantly impacted PM_2.5 levels during autumn in Jiangxi and Anhui (AH/JX) and across the monitoring period in Liuzhou, Guangxi (GX), with the former related to vehicle emissions and the latter related to bagasse reuse and biomass burning emissions. Conversely, nitrate (NO₃⁻) made the highest contribution to PM_2.5 during winter in the AH/JX region (34.4%), which was attributed to reduced SO₂ levels and favorable low-temperature conditions conducive to nitrate condensation. Notably, nitrate contribution to HB/SX rose notably in heavily polluted winter conditions and during light–moderate pollution episodes in the autumn. Sulfate (SO₄²⁻) was dominant among PM_2.5 components during summer in the study regions (29.9% in HB/SX, 36.1% in HN/SD, and 49.7% in AH/JX). Additionally, pollution incidents in Chuzhou, Anhui Province, and Baoding, Hebei Province, underscored nitrates and organic matter, respectively, as the primary causes of sharp PM_2.5 increases. These incidents highlighted the influence of large emissions of primary aerosols, gaseous precursors, and stagnant meteorological conditions as pivotal factors driving haze pollution in the HB/SX region. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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21 pages, 6831 KiB

Open AccessArticle

Modeling the Effect of Green Roofs for Building Energy Savings and Air Pollution Reduction in Shanghai

by Yuanfan Zheng and Liang Chen

Sustainability 2024, 16(1), 286; https://doi.org/10.3390/su16010286 - 28 Dec 2023

Cited by 2 | Viewed by 1162

Abstract

Building energy consumption is an essential source of greenhouse gas (GHG) and air pollution. Green roofs can directly absorb ambient CO₂ and remove air pollutants through their vegetation layers, but a limited number of studies have examined their effects on GHG and [...] Read more.

Building energy consumption is an essential source of greenhouse gas (GHG) and air pollution. Green roofs can directly absorb ambient CO₂ and remove air pollutants through their vegetation layers, but a limited number of studies have examined their effects on GHG and air pollutant reduction associated with building energy savings, especially in the context of climate change. This research examined the performance of green roofs on CO₂ and air pollutant reduction, including SO₂, PM_2.5, and NO_x, through building energy demand savings in Shanghai, China. Climate change mitigation effects were assessed based on the energy consumption of five types of buildings before and after the installation of green roofs under 2020 and 2050 climate conditions, respectively. EnergyPlus software 9.5.0 was applied to simulate hourly energy consumption for different building prototypes with and without green roofs. Green roofs on all building types exhibited positive energy savings on annual, monthly, and diurnal scales, and they can save more energy for most of the building types under the projected 2050 climate condition. Moreover, most of the building energy saved by green roofs came from the Heating, Ventilation, and Cooling (HVAC) systems. In addition, this study discovered that the energy-saving benefits of green roofs vary based on the type of building they were installed on. Green roofs were found to have the largest energy saving on the shopping mall, especially on extremely hot summer days. Finally, a Geographic Information System (GIS)-based approach was developed with the ability to quantify the amount of GHG and air pollutant reduction associated with building energy savings for existing buildings in the Huangpu District of Shanghai. This approach was also utilized to present the spatial distribution of buildings with different levels of suitability to install green roofs by considering their location attributes and air pollutant reduction potential together, which is the major innovation of this research. The purpose of this study is to provide valuable guidance to policy makers regarding the performance of green roofs in building energy-saving and air quality improvement in the urban environment when facing the challenge of climate change, which is essential for urban sustainability. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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12 pages, 1385 KiB

Open AccessArticle

Emission Characteristics of Particle Number from Conventional Gasoline and Hybrid Vehicles

by Ying Zhang, Xinping Yang and Mingliang Fu

Sustainability 2024, 16(1), 12; https://doi.org/10.3390/su16010012 - 19 Dec 2023

Viewed by 801

Abstract

Vehicular particle number (PN) emissions have garnered increasing attention. In this study, nine light-duty vehicles, involving conventional internal combustion engine gasoline vehicles (ICEVs) and hybrid electric vehicles (HEVs), underwent testing on a chassis dynamometer to elucidate key factors influencing PN emissions. We found [...] Read more.

Vehicular particle number (PN) emissions have garnered increasing attention. In this study, nine light-duty vehicles, involving conventional internal combustion engine gasoline vehicles (ICEVs) and hybrid electric vehicles (HEVs), underwent testing on a chassis dynamometer to elucidate key factors influencing PN emissions. We found that with more stringent emission standards Gasoline Direct Injection (GDI) vehicles exhibited a reduction in PN emission factors. Higher PN emissions for GDI vehicles than vehicles with Multi-Port Fuel Injection (PFI) engines were observed; meanwhile, HEV showed lower PN emissions than ICEVs. PN emissions for cold start consistently exceeded warm start across vehicles with different standards and technologies. Notably, China VI HEV exhibited a substantial 19.2-fold increase in PN emissions for cold start compared to warm start. Analysis on a second-by-second basis revealed that cold-start emissions concentrated in low speed, while warm-start emissions were prominent in extra-high speed. Concerning vehicle specific power (VSP), the lowest mean PN emission rate occurred during idle conditions. PN emissions for China IV-VI ICEVs with GDI engines would increase with the increasing VSP, whereas China VI ICEVs with PFI engines and HEV with GDI engines showed varied patterns of PN emissions, especially under cold start. Our study would further facilitate formulating effective strategies for vehicular PN emissions. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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12 pages, 2457 KiB

Open AccessArticle

The Significant Contribution of Polycyclic Aromatic Nitrogen Heterocycles to Light Absorption in the Winter North China Plain

by Yi Cheng, Junfang Mao, Zhe Bai, Wei Zhang, Linyuan Zhang, Hui Chen, Lina Wang, Ling Li and Jianmin Chen

Sustainability 2023, 15(11), 8568; https://doi.org/10.3390/su15118568 - 25 May 2023

Viewed by 1139

Abstract

By quantifying the absorption of black carbon (BC), brown carbon (BrC) and the lensing effect, we found that BrC dominates the total absorption at 450 nm, and the largest absorption contribution proportion of BrC could reach 78.3% during heavy pollution. The average absorption [...] Read more.

By quantifying the absorption of black carbon (BC), brown carbon (BrC) and the lensing effect, we found that BrC dominates the total absorption at 450 nm, and the largest absorption contribution proportion of BrC could reach 78.3% during heavy pollution. The average absorption enhancement (E_abs) at 530 nm was only 1.38, indicating that BC is not coated well here. The average value of the absorption Ångstrom exponent (AAE) between 450 nm and 530 nm was 5.3, suggesting a high concentration of BrC in Wangdu. CHN+ was the greatest contributor to the light absorption of molecules detected in MSOC with a proportion of 12.2–22.4%, in which the polycyclic aromatic nitrogen heterocycles (PANHs) were the dominant compounds. The C₆H₅NO₃ and its homologous series accounted for 3.0–11.3%, and the C₁₅H₉N and its homologous series, including one C₁₆H₁₁N and three C₁₇H₁₃N compounds, accounted for 5.1–12.3%. The absorption of these PANHs is comparable to that of nitro–aromatics, which should attract more attention to the impact of climate radiative forcing. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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17 pages, 4226 KiB

Open AccessArticle

Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau

by Thomas M. T. Lei, Stanley C. W. Ng and Shirley W. I. Siu

Sustainability 2023, 15(6), 5341; https://doi.org/10.3390/su15065341 - 17 Mar 2023

Cited by 9 | Viewed by 2044

Abstract

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution [...] Read more.

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM_2.5, PM_10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R²) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R² value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R² value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM_2.5 and PM₁₀, and CO in both 24 and 48-h forecasts. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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18 pages, 6290 KiB

Open AccessArticle

Nesting Elterman Model and Spatiotemporal Linear Mixed-Effects Model to Predict the Daily Aerosol Optical Depth over the Southern Central Hebei Plain, China

by Fuxing Li, Mengshi Li, Yingjuan Zheng, Yi Yang, Jifu Duan, Yang Wang, Lihang Fan, Zhen Wang and Wei Wang

Sustainability 2023, 15(3), 2609; https://doi.org/10.3390/su15032609 - 1 Feb 2023

Cited by 1 | Viewed by 1262

Abstract

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), [...] Read more.

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (E_f) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (E_r) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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14 pages, 7741 KiB

Open AccessArticle

Characteristics of Atmospheric Pollution in a Chinese Megacity: Insights from Three Different Functional Areas

by Jie Yang, Xinran Fu, Liping Qiao, Lan Yao, Fei Zhang and Weiyue Li

Sustainability 2023, 15(3), 2429; https://doi.org/10.3390/su15032429 - 29 Jan 2023

Cited by 5 | Viewed by 1801

Abstract

The most important atmospheric pollutants include PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different [...] Read more.

The most important atmospheric pollutants include PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃. Characteristics of atmospheric pollution were investigated by analyzing daily and hourly concentrations of the six key pollutants in three different functional areas (urban, suburban, and rural) of Shanghai during 2019–2021. Results show that O₃, exceeding PM_2.5, has become the primary pollutant determining air quality in Shanghai. The frequency of O₃ as a primary pollutant ranged from 40% in an urban area to 71% in a rural area, which was much higher than that of PM_2.5 (14–21%). NO₂ and SO₂, precursors of PM_2.5, presented a clear weekend effect, whereas PM_2.5 at weekends seems higher than that on weekdays. In the warm season, O₃ at weekends was higher than that on weekdays in the three different functional areas, whereas no significant difference was observed between O₃ on weekdays and at weekends in the cold season. Potential source contribution function analysis indicated that air pollution in Shanghai was impacted by inter-regional and intra-regional transport. The potential source areas of PM_2.5 and O₃ were different, which brought challenges to the coordinated control of PM_2.5 and O₃ in Shanghai. This study emphasizes the prominent O₃ pollution in Shanghai, and argues that the prevention and control of O₃ pollution requires regional joint prevention and control strategy. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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14 pages, 5348 KiB

Open AccessArticle

Application of Mobile Monitoring to Study Characteristics of Air Pollution in Typical Areas of the Yangtze River Delta Eco-Green Integration Demonstration Zone, China

by Xinran Fu, Qixin Cai, Yitao Yang, Yu Xu, Fanghong Zhao, Jie Yang, Liping Qiao, Lan Yao and Weiyue Li

Sustainability 2023, 15(1), 205; https://doi.org/10.3390/su15010205 - 23 Dec 2022

Cited by 2 | Viewed by 2468

Abstract

Mobile observation improves the accuracy and coverage of environmental monitoring, and can locate and track pollution sources. We conducted mobile monitoring to obtain real-time atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) [...] Read more.

Mobile observation improves the accuracy and coverage of environmental monitoring, and can locate and track pollution sources. We conducted mobile monitoring to obtain real-time atmospheric pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃) in typical areas, which included a country park and a tourist attraction featuring an ancient town in the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone (Demonstration Zone), China. Results show that the concentrations of the six key pollutants in the ancient town were usually higher than that in the country park, due to high intensity of anthropogenic emissions. Pollutants including PM_2.5, PM₁₀, SO₂ and CO in the ancient town during weekends were higher than that during weekdays, whereas pollutants in the country park presented no difference during weekdays and weekends. Morphology analysis of individual particles by scanning electron microscopy detected abundant soot from fresh emissions and atmospheric aging in the two areas. Agricultural irrigation, powered by diesel combustion, was identified as an emission source in the country park. Open-air cooking, coal combustion for cooking and the frequent redecoration of stores were emission sources in the ancient town. Environmentally friendly agricultural irrigation ways and cleaner cooking fuels were suggested to further improve air quality in the Demonstration Zone. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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18 pages, 3976 KiB

Open AccessArticle

An Air Quality Modeling and Disability-Adjusted Life Years (DALY) Risk Assessment Case Study: Comparing Statistical and Machine Learning Approaches for PM_2.5 Forecasting

by Akmaral Agibayeva, Rustem Khalikhan, Mert Guney, Ferhat Karaca, Aisulu Torezhan and Egemen Avcu

Sustainability 2022, 14(24), 16641; https://doi.org/10.3390/su142416641 - 12 Dec 2022

Viewed by 1911

Abstract

Despite Central and Northern Asia having several cities sharing a similar harsh climate and grave air quality concerns, studies on air pollution modeling in these regions are limited. For the first time, the present study uses multiple linear regression (MLR) and a random [...] Read more.

Despite Central and Northern Asia having several cities sharing a similar harsh climate and grave air quality concerns, studies on air pollution modeling in these regions are limited. For the first time, the present study uses multiple linear regression (MLR) and a random forest (RF) algorithm to predict PM_2.5 concentrations in Astana, Kazakhstan during heating and non-heating periods (predictive variables: air pollutant concentrations, meteorological parameters). Estimated PM_2.5 was then used for Disability-Adjusted Life Years (DALY) risk assessment. The RF model showed higher accuracy than the MLR model (R² from 0.79 to 0.98 in RF). MLR yielded more conservative predictions, making it more suitable for use with a lower number of predictor variables. PM₁₀ and carbon monoxide concentrations contributed most to the PM_2.5 prediction (both models), whereas meteorological parameters showed lower association. Estimated DALY for Astana’s population (2019) ranged from 2160 to 7531 years. The developed methodology is applicable to locations with comparable air pollution and climate characteristics. Its output would be helpful to policymakers and health professionals in developing effective air pollution mitigation strategies aiming to mitigate human exposure to ambient air pollutants. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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15 pages, 1153 KiB

Open AccessArticle

AQI Prediction Based on CEEMDAN-ARMA-LSTM

by Yong Sun and Jiwei Liu

Sustainability 2022, 14(19), 12182; https://doi.org/10.3390/su141912182 - 26 Sep 2022

Cited by 12 | Viewed by 2195

Abstract

In the context of carbon neutrality and air pollution prevention, it is of great research significance to achieve high-accuracy prediction of the air quality index. In this paper, Beijing is used as the study area; data from January 2014 to December 2019 are [...] Read more.

In the context of carbon neutrality and air pollution prevention, it is of great research significance to achieve high-accuracy prediction of the air quality index. In this paper, Beijing is used as the study area; data from January 2014 to December 2019 are used as the training set, and data from January 2020 to December 2021 are used as the test set. The CEEMDAN-ARMA-LSTM model constructed in this paper is used for prediction and analysis. The CEEMDAN model is used to decompose the data to improve the data information utilization. The smooth non-white noise components are fed into the ARMA model, and the remaining components and residuals are fed into the LSTM model. The results show that the MAE, MAPE, MSE, and RMSE of this model are the smallest. Compared with the CEEMDAN-LSTM, LSTM, and ARMA-GARCH models, MAE improved by 22.5%, 53.4%, and 21.5%, MAPE improved by 21.4%, 55.3%, and 26.1%, MSE improved by 39.9%, 76.9%, and 28.5%, and RMSE improved by 22.5%, 52.0%, and 15.4%. The accuracy improvement is significant and has good application prospects. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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14 pages, 3011 KiB

Open AccessArticle

Chemical Characterization, Source Identification, and Health Risk Assessment of Atmospheric Fine Particulate Matter in Winter in Hangzhou Bay

by Fei Zhang, Mei Wan, Xinglong Pang, Lan Yao, Yao Fu, Wenjing Jiang, Jingna Zhu and Ciwen Zhang

Sustainability 2022, 14(19), 12175; https://doi.org/10.3390/su141912175 - 26 Sep 2022

Viewed by 1416

Abstract

PM_2.5 is an important pollutant which affects air quality and human health. In this study, chemical components (water-soluble inorganic ions, organic carbons (OC), elemental carbons (EC), and elemental metals) and health effects were analyzed in wintertime in a suburban area in Hangzhou [...] Read more.

PM_2.5 is an important pollutant which affects air quality and human health. In this study, chemical components (water-soluble inorganic ions, organic carbons (OC), elemental carbons (EC), and elemental metals) and health effects were analyzed in wintertime in a suburban area in Hangzhou Bay. OC and SNA (sulfate, nitrate, and ammonium) contributed 76.2% to local PM_2.5. NH₄⁺ existed mainly in the form of (NH₄)₂SO₄ and NH₄NO₃. Seven sources were resolved from PMF analysis, namely secondary inorganic aerosol (31.8%), vehicle exhaust (19.5%), industry mixed with coal combustion (16.3%), crustal dust (9.5%), biomass burning (9.4%), sea salt (8.7%), and the leather industry (4.8%). Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analysis were applied to study regional transport in this region. Secondary inorganic formation was enhanced from the air plume from the northwest, especially from north Jiangsu Province. The results of the health risk assessment of associated metals indicated the higher potential of Cr and Mn to cause noncarcinogenic effects in children. A significant carcinogenic risk was observed for all people of Cr emitted from the leather industry. Our results showed the chemical characterization and sources of PM_2.5 in a suburban region, the health effects of which should be addressed in future policies to safeguard public health, especially in the leather industry. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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10 pages, 2204 KiB

Open AccessArticle

Morphological and Chemical Characterization of Particulate Matter from an Indoor Measuring Campaign

by Marius Bodor, Alina Ceoromila and Vasile Bașliu

Sustainability 2022, 14(18), 11621; https://doi.org/10.3390/su141811621 - 16 Sep 2022

Cited by 3 | Viewed by 1342

Abstract

The scientifically backed conclusion that pollution with particulate matter presents an important negative effect on human health is the driver of the present study. Not only are the results presented herein a completion, and to some small extent a confirmation, of a previous [...] Read more.

The scientifically backed conclusion that pollution with particulate matter presents an important negative effect on human health is the driver of the present study. Not only are the results presented herein a completion, and to some small extent a confirmation, of a previous study, but these findings are also a confirmation of the need to further investigate the best way for monitoring particulate matter pollution in agglomerated areas throughout the world. This need is emphasized by the moderately positive results obtained in this measuring campaign that was carried out in an indoor location of an industrial city and near a heavily circulated road. The results presented in this study were obtained by utilizing advanced methods such as optical microscopy, scanning electron microscopy (SEM), energy dispersive X-ray microanalysis (EDX), and X-ray diffraction (XRD). Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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14 pages, 6478 KiB

Open AccessArticle

The Impact of COVID-19 Control Measures on Air Quality in Guangdong Province

by Lili Li, Zhihui Mao, Jianjun Du, Tao Chen, Lu Cheng and Xiaocui Wen

Sustainability 2022, 14(13), 7853; https://doi.org/10.3390/su14137853 - 28 Jun 2022

Cited by 4 | Viewed by 1770

Abstract

COVID-19 control measures had a significant social and economic impact in Guangdong Province, and provided a unique opportunity to assess the impact of human activities on air quality. Based on the monitoring data of PM_2.5, PM₁₀, NO₂, [...] Read more.

COVID-19 control measures had a significant social and economic impact in Guangdong Province, and provided a unique opportunity to assess the impact of human activities on air quality. Based on the monitoring data of PM_2.5, PM₁₀, NO₂, and O₃ concentrations from 101 air quality monitoring stations in Guangdong Province from October 2019 to April 2020, the PSCF (potential source contribution factor) analysis and LSTM (long short-term memory) neural network were applied to explore the impact of epidemic control measures on air quality in Guangdong Province. Results showed that during the lockdown, the average concentration of PM_2.5, PM₁₀, NO₂, and O₃ decreased by 37.84%, 51.56%, 58.82%, and 24.00%, respectively. The ranges of potential sources of pollutants were reduced, indicating that air quality in Guangdong Province improved significantly. The Pearl River Delta, characterized by a high population density, recorded the highest NO₂ concentration values throughout the whole study period. Due to the lockdown, the areas with the highest concentrations of O₃, PM_2.5, and PM₁₀ changed from the Pearl River Delta to the eastern and western Guangdong. Moreover, LSTM simulation results showed that the average concentration of PM_2.5, PM₁₀, NO₂, and O₃ decreased by 46.34%, 54.56%, 70.63%, and 26.76%, respectively, which was caused by human-made impacts. These findings reveal the remarkable impact of human activities on air quality and provide effective theoretical support for the prevention and control of air pollution in Guangdong Province. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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16 pages, 297 KiB

Open AccessArticle

Impact of Major Events on Interprovincial Carbon Emissions—Based on PSM-DID Analysis

by Jiwei Liu and Qun Li

Sustainability 2022, 14(12), 7459; https://doi.org/10.3390/su14127459 - 18 Jun 2022

Cited by 7 | Viewed by 2147

Abstract

The success of major events can enhance national image, boost people’s confidence, and alleviate the current “three-fold pressure”—contraction in demand, supply shocks and weak expectations. In the context of the carbon neutrality target, it is important to analyze the relationship between major events [...] Read more.

The success of major events can enhance national image, boost people’s confidence, and alleviate the current “three-fold pressure”—contraction in demand, supply shocks and weak expectations. In the context of the carbon neutrality target, it is important to analyze the relationship between major events and carbon emissions as the ecological, social and economic systems become more closely related. To study the extent and persistence of the impact of major events on the carbon emissions of the hosting provinces, this paper collects annual carbon emission data from 2015 to 2019 for 30 provinces in China. The propensity score matching Difference in Difference model (PSM-DID) is used to explore the impact of major events, such as political conferences, sports events and cultural exchanges, at the national level on inter-provincial carbon emissions. The empirical study shows that (1) the carbon emissions of the provinces involved in major events significantly increase in the year when the major event is held, (2) the carbon emissions of the province significantly decrease in the year after the conclusion of the major event, and (3) the decrease is higher than the increase in carbon emissions in the year when the event is held. Finally, the model results are analyzed in the context of economic events and macroeconomic policy lags during the preparation period of the event, and policy suggestions are made to incorporate carbon neutrality into the overall layout study of ecological civilization construction, strengthening the construction of legal thinking, enhancing inter-provincial and inter-city pollution synergy control, innovating carbon-related technologies, unifying carbon emission accounting and improving data openness. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

15 pages, 8907 KiB

Open AccessArticle

Impact of Inter-Annual Variation in Meteorology from 2010 to 2019 on the Inter-City Transport of PM_2.5 in the Beijing–Tianjin–Hebei Region

by Dongsheng Chen, Xin Jin, Xinyi Fu, Lin Xia, Xiurui Guo, Jianlei Lang, Ying Zhou and Wei Wei

Sustainability 2022, 14(10), 6210; https://doi.org/10.3390/su14106210 - 20 May 2022

Cited by 6 | Viewed by 1641

Abstract

Air pollution has become a great challenge to achieving sustainable development. Among the pollutants, aerosols significantly affect human health and play an important role in global climate change. The concentration of aerosols in the ambient air is influenced strongly by the regional transport [...] Read more.

Air pollution has become a great challenge to achieving sustainable development. Among the pollutants, aerosols significantly affect human health and play an important role in global climate change. The concentration of aerosols in the ambient air is influenced strongly by the regional transport of pollutants and their precursors and may vary considerably under different meteorological conditions in different years. This inter-annual variation in meteorology may yield conflicting results in the quantification of the contribution from regional transport of air pollutants. It creates uncertainty for local governments to develop pollution control measures to reduce the challenges to sustainable development. Previous studies on this issue are often year-specific or cover short time spans, and the inter-city transport of air pollutants in the long term is still not fully understood. Therefore, in this study, the Weather Research and Forecasting (WRF) model and Community Multiscale Air Quality (CMAQ) model was used to assess inter-annual variations in the contribution of inter-city transport to the PM_2.5 concentration in the Beijing–Tianjin–Hebei region from 2010 to 2019. To highlight the impact of inter-annual variations in meteorology, the authors used the same emission inventory and the same model configurations for the 10-year simulation. The major findings can be summarized as follows: (1) Both PM_2.5 concentration and inter-city transport in the Beijing–Tianjin–Hebei (BTH) region were influenced by the inter-annual variation in meteorological conditions. (2) The simulated annual average concentrations in 13 cities in BTH are highly variable, with fluctuations ranging from 30.8% to 54.1%, and more evident variations were found in seasonal results. (3) Seven out of thirteen cities have a contribution from regional transport exceeding 50%, which are located in the eastern half of the Beijing–Tianjin–Hebei region. (4) The magnitude of the regional transport contribution varies significantly among the cities of BTH, on an annual basis, from a minimum inter-annual fluctuation of 8.9% to a maximum of 37.2%, and seasonal fluctuation is even more strongly evident. These results indicate that, when formulating pollution control strategies, inter-annual changes in meteorological conditions should not be ignored. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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17 pages, 4887 KiB

Open AccessArticle

The Independent Impacts of PM_2.5 Dropping on the Physical and Chemical Properties of Atmosphere over North China Plain in Summer during 2015–2019

by Shengju Ou, Wei Wei, Bin Cai, Saisai Chen, Panbo Guan and Shuiyuan Cheng

Sustainability 2022, 14(7), 3930; https://doi.org/10.3390/su14073930 - 26 Mar 2022

Cited by 1 | Viewed by 1621

Abstract

Great changes occurred in the physical and chemical properties of the atmosphere in the North China Plain (NCP) in summer caused by PM_2.5 dropping from 58 μg/m³ in 2015 to 36.0 μg/m³ in 2019. In this study, we first applied [...] Read more.

Great changes occurred in the physical and chemical properties of the atmosphere in the North China Plain (NCP) in summer caused by PM_2.5 dropping from 58 μg/m³ in 2015 to 36.0 μg/m³ in 2019. In this study, we first applied the WRF-Chem model to quantify the impact of PM_2.5 reduction on shortwave radiation reaching the ground (SWDOWN), planetary boundary layer height (PBLH), and the surface concentration of air pollutants (represented by CO). Simulation results obtained an increase of 15.0% in daytime SWDOWN and 9.9% in daytime PBLH, and a decrease of −5.0% in daytime CO concentration. These changes were induced by the varied PM_2.5 levels. Moreover, the variation in SWDOWN further led to a rise in the NO₂ photolysis rate (JNO₂) over this region, by 1.82 × 10⁻⁴~1.91 × 10⁻⁴ s⁻¹ per year. Afterwards, we employed MCM chemical box model to explore how the JNO₂ increase and the precursor decrease (CO, VOCs, and NO_x) influenced O₃ and HO_x radicals. The results revealed that the photolysis rate (J) increase would individually cause a change on daytime surface O₃, OH, and HO₂ radicals by +9.0%, +18.9%, and +23.7%; the corresponding change induced by the precursor decrease was −2.5%, +1.9%, and −2.3%. At the same time, the integrated impacts of the change in J and precursors cause an increase of +6.3%, +21.1%, and +20.9% for daytime surface O₃, OH, and HO₂. Generally, the atmospheric oxidation capacity significantly enhanced during summer in NCP due to the PM_2.5 dropping in recent years. This research can help understand atmosphere changes caused by PM_2.5 reduction comprehensively. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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Airborne Nanoparticles (PM_0.1) in Southeast Asian Cities: A Review

by Worradorn Phairuang, Muhammad Amin, Mitsuhiko Hata and Masami Furuuchi

Sustainability 2022, 14(16), 10074; https://doi.org/10.3390/su141610074 - 15 Aug 2022

Cited by 17 | Viewed by 2937

Abstract

PM_0.1 (particles with a diameter ≤ 0.1 µm), nanoparticles (NPs), or ultrafine particles (UFPs) were interchangeably used in the scientific communities. PM_0.1 originated from both natural and human sources; however, PM_0.1 and its effects on the environment, visibility, and human [...] Read more.

PM_0.1 (particles with a diameter ≤ 0.1 µm), nanoparticles (NPs), or ultrafine particles (UFPs) were interchangeably used in the scientific communities. PM_0.1 originated from both natural and human sources; however, PM_0.1 and its effects on the environment, visibility, and human health to understanding air pollution levels, sources, and impacts in Southeast Asia (SEA) countries continue to be challenging. The concentrations of PM_0.1 in most SEA countries are much worse than in western countries’ environments. A further motivation of this reviewed article is to provide a critical synthesis of the current knowledge and study of ambient PM_0.1 in SEA cities. The primary influence of characteristics of PM_0.1 appears to be local sources, including biomass burning and motor vehicles. Continuous monitoring of PM_0.1 in mass and number concentration should be further understood. A critical review is of great importance to facilitating air pollution control policies and predicting the behavior of PM_0.1 in SEA. Full article

(This article belongs to the Special Issue Aerosols and Air Pollution)

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