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Zhan Y, Xie M, Zhuang B, Gao D, Zhu K, Lu H, Wang T, Li S, Li M, Luo Y, Zhao R. Particle-ozone complex pollution under diverse synoptic weather patterns in the Yangtze River Delta region: Synergistic relationships and the effects of meteorology and chemical compositions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174365. [PMID: 38960176 DOI: 10.1016/j.scitotenv.2024.174365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/10/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
There is considerable academic interest in the particle-ozone synergistic relationship (PO) between fine particulate matter (PM2.5) and ozone (O3). Using various synoptic weather patterns (SWPs), we quantitatively assessed the variations in the PO, which is relevant to formulating policies aimed at controlling complex pollution in the air. First, based on one-year sampling data from March 2018 to February 2019, the SWPs classification of the Yangtze River Delta (YRD) was conducted using the sum-of-squares technique (SS). Five dominant SWPs can be found in the YRD region, including the Aleutian low under SWP1 (occurring 45 % of the year), a tropical cyclone under SWP2 (21 %), the tropical cyclone and western Pacific Subtropical High (WPSH) under SWP3 (15.4 %), the WPSH under SWP4 (6.9 %), and a continental high pressure under SWP5 (3.1 %). The phenomenon of a "seesaw" between PM2.5 and O3 concentrations exhibited significant spatial heterogeneity, which was influenced by meteorological mechanisms. Second, the multi-linear regression (MLR) model and the partial correlation (PCOR) analysis were employed to quantify the effects of dominant components and meteorological factors on the PO. Meteorological variables could collectively explain only 33.0 % of the PM2.5 variations, but 58.0 % for O3. O3 promoted each other with low concentrations of PM2.5 but was inhibited by high concentrations of PM2.5. High relative humidity (RH) was conducive to the generation of PM2.5 secondary components and enhanced the radiative effects of aerosols and the negative correlation of PO. In addition, attention should be paid to assessing the combined effects of precursor levels, weather, and chemical reactions on the particle-ozone complex pollution. The control of O3 pollutants should be intensified in summer, while the focus should be on reducing PM2.5 pollutants in winter. Prevention and control measures need to reflect the differences in weather conditions and pollution characteristics, with a focus on RH and secondary components of PM2.5.
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Affiliation(s)
- Yangzhihao Zhan
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Min Xie
- School of Environment, Nanjing Normal University, Nanjing 210023, China; Carbon Monitoring and Digital Application Technology Center, Carbon Peak and Carbon Neutralization Strategy Institute of Jiangsu Province, Nanjing 210023, China
| | - Bingliang Zhuang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Da Gao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Kuanguang Zhu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; Hubei Provincial Academy of Eco-Environmental Sciences, Wuhan 430073, China
| | - Hua Lu
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China; Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Shu Li
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Mengmeng Li
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Yi Luo
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Runqi Zhao
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
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Esu CO, Pyo J, Cho K. Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter. JOURNAL OF HAZARDOUS MATERIALS 2024; 475:134864. [PMID: 38876025 DOI: 10.1016/j.jhazmat.2024.134864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/03/2024] [Accepted: 06/07/2024] [Indexed: 06/16/2024]
Abstract
Conventional environmental health research is primarily focused on isolated chemical exposures, neglecting the complex interactions between multiple pollutants that may synergistically or antagonistically influence toxicity, thereby posing unexpected health risks. In this study, we address this knowledge gap by introducing an explainable machine learning (ML) approach with Feature Localized Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) designed to extract the dose-response relationships of specific pollutants in mixtures. In contrast to traditional SHAP, FLIT-SHAP can localize the global intercept to elucidate mixture effects, which is crucial for understanding the oxidative potential (OP) of ambient particulate matter (PM). Assessing multi-pollutant OP using FLIT-SHAP revealed both synergistic (55-63 %) and antagonistic (25-42 %) effects in laboratory-controlled OP data, but an antagonistic (33-66 %; lower OP) effect in ambient PM. Notably, the FLIT-SHAP approach demonstrated higher prediction accuracy (R2 = 0.99) compared to the additive model (R2 = 0.89) when evaluated against real-world PM samples. Quinones, such as phenanthrenequinone, play a more significant role in PM2.5 than previously recognized. Through this study, we highlighted the potential of FLIT-SHAP to enhance toxicity predictions and aid decision-making in the field of environmental health.
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Affiliation(s)
- Charles O Esu
- Department of Environmental Engineering, Pusan National University, Republic of Korea
| | - JongCheol Pyo
- Department of Environmental Engineering, Pusan National University, Republic of Korea
| | - Kuk Cho
- Department of Environmental Engineering, Pusan National University, Republic of Korea; Institute of Environmental Studies, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Republic of Korea.
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Liu Y, Xu X, Ji D, He J, Wang Y. Examining trends and variability of PM 2.5-associated organic and elemental carbon in the megacity of Beijing, China: Insight from decadal continuous in-situ hourly observations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 938:173331. [PMID: 38777070 DOI: 10.1016/j.scitotenv.2024.173331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/24/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024]
Abstract
Organic carbon (OC) and elemental carbon (EC) in fine particulate matter (PM2.5) play pivotal roles in impacting human health, air quality, and climate change dynamics. Long-term monitoring datasets of OC and EC in PM2.5 are indispensable for comprehending their temporal variations, spatial distribution, evolutionary patterns, and trends, as well as for assessing the effectiveness of clean air action plans. This study presents and scrutinizes a comprehensive 10-year hourly dataset of PM2.5-bound OC and EC in the megacity of Beijing, China, spanning from 2013 to 2022. Throughout the entire study period, the average concentrations of OC and EC were recorded at 8.8 ± 8.7 and 2.5 ± 3.0 μg/m3, respectively. Employing the seasonal and trend decomposition methodology, specifically the locally estimated scatter plot smoothing method combined with generalized least squares with the autoregressive moving average method, the study observed a significant decline in OC and EC concentrations, reducing by 5.8 % yr-1 and 9.9 % yr-1 at rates of 0.8 and 0.4 μg/m3 yr-1, respectively. These declining trends were consistently verified using Theil-Sen method. Notably, the winter months exhibited the most substantial declining trends, with rates of 9.3 % yr-1 for OC and 10.9 % yr-1 for EC, aligning with the positive impact of the implemented clean air action plan. Weekend spikes in OC and EC levels were attributed to factors such as traffic regulations and residential emissions. Diurnal variations showcased higher concentrations during nighttime and lower levels during daytime. Although meteorological factors demonstrated an overall positive impact with average reduction in OC and EC concentrations by 8.3 % and 8.7 %, clean air action plans including the Air Pollution Prevention and Control Action Plan (2013-2017) and the Three-Year Action Plan to Win the Blue Sky War (2018-2020) have more contributions in reducing the OC and EC concentrations with mass drop rates of 87.1 % and 89.2 % and 76.7 % and 96.7 %, respectively. Utilizing the non-parametric wind regression method, significant concentration hotspots were identified at wind speeds of ≤2 m/s, with diffuse signals recorded in the southwestern wind sectors at wind speeds of approximately 4-5 m/s. Interannual disparities in potential source regions of OC and EC were evident, with high potential source areas observed in the southern and northwestern provinces of Beijing from 2013 to 2018. In contrast, during 2019-2022, potential source areas with relatively high values of potential source contribution function were predominantly situated in the southern regions of Beijing. This analysis, grounded in observational data, provides insights into the decadal changes in the major atmospheric composition of PM2.5 and facilitates the evaluation of the efficacy of control policies, particularly relevant for developing countries.
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Affiliation(s)
- Yu Liu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China
| | - Xiaojuan Xu
- University of Chinese Academy of Sciences, Beijing 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China
| | - Dongsheng Ji
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China.
| | - Jun He
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, China; Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute, Ningbo 315100, China
| | - Yuesi Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of Chinese Academy of Sciences, Beijing 100049, China; Atmosphere Sub-Center of Chinese Ecosystem Research Network, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100191, China
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Zheng X, Meng H, Tan Q, Zhou Z, Zhou X, Liu X, Grieneisen ML, Wang N, Zhan Y, Yang F. Impacts of the Chengdu 2021 world university games on NO 2 pollution: Implications for urban vehicle electrification promotion. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:175073. [PMID: 39089381 DOI: 10.1016/j.scitotenv.2024.175073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/24/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.
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Affiliation(s)
- Xi Zheng
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Haiyan Meng
- Department of Environmental Science and Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu, Sichuan 610072, China
| | - Zihang Zhou
- Chengdu Academy of Environmental Sciences, Chengdu, Sichuan 610072, China
| | - Xiaoling Zhou
- Chengdu Academy of Environmental Sciences, Chengdu, Sichuan 610072, China
| | - Xuan Liu
- Chengdu Academy of Environmental Sciences, Chengdu, Sichuan 610072, China
| | - Michael L Grieneisen
- Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, United States
| | - Nan Wang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yu Zhan
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Fumo Yang
- College of Carbon Neutrality Future Technology, Sichuan University, Chengdu, Sichuan 610065, China
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Khayyam J, Xie P, Xu J, Tian X, Feng H, Qinjin W. Vertically resolved meteorological adjustments of aerosols and trace gases in Beijing, Taiyuan, and Hefei by using RF model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 948:174795. [PMID: 39029749 DOI: 10.1016/j.scitotenv.2024.174795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/05/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024]
Abstract
Air pollution represents a complex phenomenon defined by the presence of various gases and particulate matter, leading to intricate spatio-temporal fluctuations. This study aims to enhance our understanding of how meteorological factors influence trace gases and aerosols, exacerbating air pollution in various geographical locations, specifically in Beijing's Fengtai (BJFT), Taiyuan City (SXTY), and Hefei's Science Island (HFDP). The study employs 2D-MAX-DOAS observations and utilizes the Random Forest (RF) model to decouple the influence of meteorological conditions from pollutant data. The vertical profile of nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO), and aerosols at each study site was classified into four distinct layers, followed by conducting a meteorological decoupling analysis on each layer. This decoupling analysis demonstrates that meteorology significantly influences aerosols across all sites, with reductions ranging from 75 % to 95 % after de-weathering. SO2 shows minimal susceptibility, with the changes ranging from ±20 % to ±60 % after de-weathering. Among all sites, BJFT's pollutants exhibit less susceptibility overall, while pollutants at HFDP are more susceptible. The findings further reveal significant meteorological interventions in pollutants in surface layers (0.05 km and 0.2-0.4 km) at BJFT, with some exceptions at SXTY. However, pollutants, particularly NO2 and aerosols in higher layers (0.6-0.8 km and 1.0-1.2 km) at HFDP, also experience significant meteorological interferences. The findings at HFDP and SXTY reveal that removing meteorological influence also adjusts the profile shape of pollutants. For instance, the NO2 profile at HFDP during the winter season shifted from a bimodal to an exponential shape after de-weathering. Overall, this study sheds light on the complex interplay between meteorological factors and trace gases at various altitudes across different geographic locations, offering insights crucial for holistic and effective pollution mitigation strategies.
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Affiliation(s)
- Junaid Khayyam
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Pinhua Xie
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China.
| | - Jin Xu
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Xin Tian
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Hu Feng
- University of Science and Technology of China, Hefei 230026, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Wei Qinjin
- University of Science and Technology of China, Hefei 230026, China
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Kou Y, Zhang W, Zhang Y, Ge X, Wu Y. Toxic effects of trace metal(loid) mixtures on aquatic organisms. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 948:174677. [PMID: 39009169 DOI: 10.1016/j.scitotenv.2024.174677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/17/2024] [Accepted: 07/08/2024] [Indexed: 07/17/2024]
Abstract
The co-occurrence of metal (loid)s in realistic aquatic environments necessitates the evaluation of their combined effects. However, the generality of the additive effect hypothesis is contentious, particularly due to metal(loid)-metal(loid) interactions. The absence of systematic evaluation approaches restricts our ability to draw overall conclusions and make reliable predictions. In this study, we reviewed 1473 effect sizes from 38 publications, and classified all responses into seven main categories (from molecular to individual levels) according to their toxicological significance. Our meta-analysis revealed that metal(loid) mixtures had significant effects on aquatic organisms (33 %, 95 % CI 28 %-39 %, P < 0.05), along with significant response heterogeneity (Qt = 690,319.62, P < 0.0001; I2 = 99.95 %). Concurrently, we developed a Random Forest machine learning model to predict adverse effects and identify key variables. These two methods demonstrated that the toxicity of metal(loid) mixtures is primarily linked to the choice of toxicity endpoints, and the characteristics of metal(loid) mixtures. Our findings underscore the potential of combining meta-analysis with machine learning, a more systematic approach, to enhance the understanding and prediction of the adverse effects of metal(loid) mixtures, and they offer guidance for risk assessment and policy-making in complex environmental scenarios.
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Affiliation(s)
- Yajing Kou
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (AEET), School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
| | - Wei Zhang
- School of Environmental Science and Engineering, Guangzhou University, Guangzhou 510006, China
| | - Yunjiang Zhang
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (AEET), School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
| | - Xinlei Ge
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (AEET), School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
| | - Yun Wu
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (AEET), School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China.
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Tian X, Wang Z, Xie P, Xu J, Li A, Pan Y, Hu F, Hu Z, Chen M, Zheng J. A CNN-SVR model for NO 2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO 2 in Nanjing, China. J Environ Sci (China) 2024; 141:151-165. [PMID: 38408816 DOI: 10.1016/j.jes.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 02/28/2024]
Abstract
In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.
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Affiliation(s)
- Xin Tian
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Zijie Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Pinhua Xie
- Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Jin Xu
- Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Ang Li
- Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Yifeng Pan
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Feng Hu
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, China
| | - Zhaokun Hu
- Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Mingsheng Chen
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Jiangyi Zheng
- Key laboratory of Environmental Optical and Technology, Anhui Institute of optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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Li J, Yuan B, Yang S, Peng Y, Chen W, Xie Q, Wu Y, Huang Z, Zheng J, Wang X, Shao M. Quantifying the contributions of meteorology, emissions, and transport to ground-level ozone in the Pearl River Delta, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 932:173011. [PMID: 38719052 DOI: 10.1016/j.scitotenv.2024.173011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/29/2024] [Accepted: 05/01/2024] [Indexed: 05/14/2024]
Abstract
Ozone pollution presents a growing air quality threat in urban agglomerations in China. It remains challenge to distinguish the roles of emissions of precursors, chemical production and transportations in shaping the ground-level ozone trends, largely due to complicated interactions among these 3 major processes. This study elucidates the formation factors of ozone pollution and categorizes them into local emissions (anthropogenic and biogenic emissions), transport (precursor transport and direct transport from various regions), and meteorology. Particularly, we attribute meteorology, which affects biogenic emissions and chemical formation as well as transportation, to a perturbation term with fluctuating ranges. The Community Multiscale Air Quality (CMAQ) model was utilized to implement this framework, using the Pearl River Delta region as a case study, to simulate a severe ozone pollution episode in autumn 2019 that affected the entire country. Our findings demonstrate that the average impact of meteorological conditions changed consistently with the variation of ozone pollution levels, indicating that meteorological conditions can exert significant control over the degree of ozone pollution. As the maximum daily 8-hour average (MDA8) ozone concentrations increased from 20 % below to 30 % above the National Ambient Air Quality Standard II, contributions from emissions and precursor transport were enhanced. Concurrently, direct transport within Guangdong province rose from 13.8 % to 22.7 %, underscoring the importance of regional joint prevention and control measures under adverse weather conditions. Regarding biogenic emissions and precursor transport that cannot be directly controlled, we found that their contributions were generally greater in urban areas with high nitrogen oxides (NOx) levels, primarily due to the stronger atmospheric oxidation capacity facilitating ozone formation. Our results indicate that not only local anthropogenic emissions can be controlled in urban areas, but also the impacts of local biogenic emissions and precursor transport can be potentially regulated through reducing atmospheric oxidation capacity.
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Affiliation(s)
- Jin Li
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Bin Yuan
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China.
| | - Suxia Yang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yuwen Peng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Weihua Chen
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Qianqian Xie
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Yongkang Wu
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Zhijiong Huang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Junyu Zheng
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Xuemei Wang
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
| | - Min Shao
- College of Environment and Climate, Institute for Environmental and Climate Research, Guangdong-Hongkong-Macau Joint Laboratory of Collaborative Innovation for Environmental Quality, Jinan University, Guangzhou 511443, China
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Li Y, Yao L, Yang J, Wu J, Tang X, Liang S, Zhang Y, Feng Y. Characterizing the emission trends and pollution evolution patterns during the transition period following COVID-19 at an industrial megacity of central China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 278:116354. [PMID: 38691882 DOI: 10.1016/j.ecoenv.2024.116354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/03/2024]
Abstract
After the resumption of work and production following the COVID-19 pandemic, many cities entered a "transition phase", characterized by the gradual recovery of emission levels from various sources. Although the overall PM2.5 emission trends have recovered, the specific changes in different sources of PM2.5 remain unclear. Here, we investigated the changes in source contributions and the evolution pattern of pollution episodes (PE) in Wuhan during the "transition period" and compared them with the same period during the COVID-19 lockdown. We found that vehicle emissions, industrial processes, and road dust exhibited significant recoveries during the transition period, increasing by 5.4%, 4.8%, and 3.9%, respectively, during the PE. As primary emissions increased, secondary formation slightly declined, but it still played a predominant role (accounting for 39.1∼ 43.0% of secondary nitrate). The reduction in industrial activities was partially offset by residential burning. The evolution characteristics of PE exhibited significant differences between the two periods, with PM2.5 concentration persisting at a high level during the transition period. The differences in the evolution patterns of the two periods were also reflected in their change rates at each stage, which mostly depend on the pre-PE concentration level. The transition period shows a significantly higher value (8.4 μg m-3 h-1) compared with the lockdown period, almost double the amount. In addition to local emissions, regional transport should be a key consideration in pollution mitigation strategies, especially in areas adjacent to Wuhan. Our study quantifies the variations in sources between the two periods, providing valuable insights for optimizing environmental planning to achieve established goals.
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Affiliation(s)
- Yafei Li
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Lu Yao
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Jingyi Yang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Jianhui Wu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China.
| | - Xiao Tang
- LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Shengwen Liang
- Wuhan Biological Environment Monitoring Center, Wuhan 430022, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300000, China
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10
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Luo Z, Feng C, Yang J, Dai Q, Dai T, Zhang Y, Liang D, Feng Y. Assessing emission-driven changes in health risk of source-specific PM 2.5-bound heavy metals by adjusting meteorological covariates. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172038. [PMID: 38552967 DOI: 10.1016/j.scitotenv.2024.172038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 03/05/2024] [Accepted: 03/26/2024] [Indexed: 04/15/2024]
Abstract
Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China' second Clean Air Action (2018-2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.
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Affiliation(s)
- Zhongwei Luo
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Chengliang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Jingyi Yang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China; Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
| | - Tianjiao Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China; Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Danni Liang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China; Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
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11
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An H, Li X, Huang Y, Wang W, Wu Y, Liu L, Ling W, Li W, Zhao H, Lu D, Liu Q, Jiang G. A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science. ECO-ENVIRONMENT & HEALTH 2024; 3:131-136. [PMID: 38638173 PMCID: PMC11021822 DOI: 10.1016/j.eehl.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/23/2023] [Accepted: 01/02/2024] [Indexed: 04/20/2024]
Abstract
The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".
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Affiliation(s)
- Haoyuan An
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Xiangyu Li
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yuming Huang
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Weichao Wang
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yuehan Wu
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Lin Liu
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Weibo Ling
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Wei Li
- Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Hanzhu Zhao
- Biomedical Engineering Institute, School of Control Science and Engineering, Shandong University, Jinan 250061, China
| | - Dawei Lu
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Toxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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12
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Tan Z, Feng M, Liu H, Luo Y, Li W, Song D, Tan Q, Ma X, Lu K, Zhang Y. Atmospheric Oxidation Capacity Elevated during 2020 Spring Lockdown in Chengdu, China: Lessons for Future Secondary Pollution Control. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:8815-8824. [PMID: 38733566 DOI: 10.1021/acs.est.3c08761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2024]
Abstract
This study presents the measurement of photochemical precursors during the lockdown period from January 23, 2020, to March 14, 2020, in Chengdu in response to the coronavirus (COVID-19) pandemic. To derive the lockdown impact on air quality, the observations are compared to the equivalent periods in the last 2 years. An observation-based model is used to investigate the atmospheric oxidation capacity change during lockdown. OH, HO2, and RO2 concentrations are simulated, which are elevated by 42, 220, and 277%, respectively, during the lockdown period, mainly due to the reduction in nitrogen oxides (NOx). However, the radical turnover rates, i.e., OH oxidation rate L(OH) and local ozone production rate P(O3), which determine the secondary intermediates formation and O3 formation, only increase by 24 and 48%, respectively. Therefore, the oxidation capacity increases slightly during lockdown, which is partly attributed to unchanged alkene concentrations. During the lockdown, alkene ozonolysis seems to be a significant radical primary source due to the elevated O3 concentrations. This unique data set during the lockdown period highlights the importance of controlling alkene emission to mitigate secondary pollution formation in Chengdu and may also be applicable in other regions of China given an expected NOx reduction due to the rapid transformation to electrified fleets in the future.
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Affiliation(s)
- Zhaofeng Tan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Miao Feng
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Hefan Liu
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Yina Luo
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Wei Li
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Danlin Song
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Qinwen Tan
- Chengdu Academy of Environmental Sciences, Chengdu 610072, China
| | - Xuefei Ma
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Keding Lu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Yuanhang Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control (Peking University), College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
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13
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Ma Y, Nobile F, Marb A, Dubrow R, Kinney PL, Peters A, Stafoggia M, Breitner S, Chen K. Air pollution changes due to COVID-19 lockdowns and attributable mortality changes in four countries. ENVIRONMENT INTERNATIONAL 2024; 187:108668. [PMID: 38640613 DOI: 10.1016/j.envint.2024.108668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/20/2024] [Accepted: 04/15/2024] [Indexed: 04/21/2024]
Abstract
COVID-19 lockdowns reduced nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions in many countries. We aim to quantify the changes in these pollutants and to assess the attributable changes in mortality in Jiangsu, China; California, U.S.; Central-southern Italy; and Germany during COVID-19 lockdowns in early 2020. Accounting for meteorological impacts and air pollution time trends, we use a machine learning-based meteorological normalization technique and the difference-in-differences approach to quantify the changes in NO2 and PM2.5 concentrations due to lockdowns. Using region-specific estimates of the association between air pollution and mortality derived from a causal modeling approach using data from 2015 to 2019, we assess the changes in mortality attributable to the air pollution changes caused by the lockdowns in early 2020. During the lockdowns, NO2 reductions avoided 1.41 (95% empirical confidence interval [eCI]: 0.94, 1.88), 0.44 (95% eCI: 0.17, 0.71), and 4.66 (95% eCI: 2.03, 7.44) deaths per 100,000 people in Jiangsu, China; California, U.S.; and Central-southern Italy, respectively. Mortality benefits attributable to PM2.5 reductions were also significant, albeit of a smaller magnitude. For Germany, the mortality benefits attributable to NO2 changes were not significant (0.11; 95% eCI: -0.03, 0.25), and an increase in PM2.5 concentrations was associated with an increase in mortality of 0.35 (95% eCI: 0.22, 0.48) deaths per 100,000 people during the lockdown. COVID-19 lockdowns overall improved air quality and brought attributable health benefits, especially associated with NO2 improvements, with notable heterogeneity across regions. This study underscores the importance of accounting for local characteristics when policymakers adapt successful emission control strategies from other regions.
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Affiliation(s)
- Yiqun Ma
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Federica Nobile
- Department of Epidemiology, Lazio Region Health Service / ASL Roma 1, Rome, Italy
| | - Anne Marb
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Robert Dubrow
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA
| | - Patrick L Kinney
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA
| | - Annette Peters
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service / ASL Roma 1, Rome, Italy
| | - Susanne Breitner
- Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Kai Chen
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA; Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, CT, USA.
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14
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Xu B, Yu H, Shi Z, Liu J, Wei Y, Zhang Z, Huangfu Y, Xu H, Li Y, Zhang L, Feng Y, Shi G. Knowledge-guided machine learning reveals pivotal drivers for gas-to-particle conversion of atmospheric nitrate. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100333. [PMID: 38021366 PMCID: PMC10661687 DOI: 10.1016/j.ese.2023.100333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 10/09/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023]
Abstract
Particulate nitrate, a key component of fine particles, forms through the intricate gas-to-particle conversion process. This process is regulated by the gas-to-particle conversion coefficient of nitrate (ε(NO3-)). The mechanism between ε(NO3-) and its drivers is highly complex and nonlinear, and can be characterized by machine learning methods. However, conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors. It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact of ε(NO3-). Here we introduce a supervised machine learning approach-the multilevel nested random forest guided by theory approaches. Our approach robustly identifies NH4+, SO42-, and temperature as pivotal drivers for ε(NO3-). Notably, substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results. Furthermore, our approach underscores the significance of NH4+ during both daytime (30%) and nighttime (40%) periods, while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis. This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.
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Affiliation(s)
- Bo Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Zongbo Shi
- School of Geography Earth and Environment Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Jinxing Liu
- State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin Key Laboratory of air Pollutants Monitoring Technology, School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin, 300072, China
- Gigantic Technology (Tianjin) Co., Ltd, Tianjin, 300072, China
| | - Yuting Wei
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Zhongcheng Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yanqi Huangfu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Han Xu
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Yue Li
- College of Computer Science, Nankai University, Tianjin, 300350, China
| | - Linlin Zhang
- China National Environmental Monitoring Centre, Beijing, 100012, China
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
- CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research (CLAER), College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China
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15
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Hall J, Zhong J, Jowett S, Mazzeo A, Thomas GN, Bryson JR, Dewar S, Inglis N, Wolstencroft M, Muller C, Bloss W, Harrison R, Bartington S. Regional impact assessment of air quality improvement: The air quality lifecourse assessment tool (AQ-LAT) for the West Midlands combined authority (WMCA). ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024:123871. [PMID: 38729507 DOI: 10.1016/j.envpol.2024.123871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 05/12/2024]
Abstract
Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 μg/m3) and PM2.5 (5 μg/m3) in the West Midlands Combined Authority Area. All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.
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Affiliation(s)
- James Hall
- Health Economics Unit, Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - Jian Zhong
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK; Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - Sue Jowett
- Health Economics Unit, Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - Andrea Mazzeo
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK; Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - G Neil Thomas
- Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - John R Bryson
- Department of Strategy and International Business, Birmingham Business School, University of Birmingham, Edgbaston, Birmingham, B152TT, UK
| | - Steve Dewar
- Coventry City Council, Earl Street, Coventry, CV1 5RR, UK
| | - Nadia Inglis
- Coventry City Council, Earl Street, Coventry, CV1 5RR, UK
| | | | - Catherine Muller
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | - William Bloss
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK; Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK
| | | | - Suzanne Bartington
- Institute of Applied Health Research, University of Birmingham, Edgbaston Park Road, Birmingham, B15 2TT, UK.
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16
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Tan Y, Zhang Y, Wang T, Chen T, Mu J, Xue L. Dissecting Drivers of Ozone Pollution during the 2022 Multicity Lockdowns in China Sheds Light on Future Control Direction. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6988-6997. [PMID: 38592860 DOI: 10.1021/acs.est.4c01197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
In 2022, many Chinese cities experienced lockdowns and heatwaves. We analyzed ground and satellite data using machine learning to elucidate chemical and meteorological drivers of changes in O3 pollution in 27 major Chinese cities during lockdowns. We found that there was an increase in O3 concentrations in 23 out of 27 cities compared with the corresponding period in 2021. Random forest modeling indicates that emission reductions in transportation and other sectors, as well as the changes in meteorology, increased the level of O3 in most cities. In cities with over 80% transportation reductions and temperature fluctuations within -2 to 2 °C, the increases in O3 concentrations were mainly attributable to reductions in nitrogen oxide (NOx) emissions. In cities that experienced heatwaves and droughts, increases in the O3 concentrations were primarily driven by increases in temperature and volatile organic compound (VOC) emissions, and reductions in NOx concentrations from ground transport were offset by increases in emissions from coal-fired power generation. Despite 3-99% reduction in passenger volume, most cities remained VOC-limited during lockdowns. These findings demonstrate that to alleviate urban O3 pollution, it will be necessary to further reduce industrial emissions along with transportation sources and to take into account the climate penalty and the impact of heatwaves on O3 pollution.
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Affiliation(s)
- Yue Tan
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Yingnan Zhang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Tao Wang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Tianshu Chen
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077,China
| | - Jiangshan Mu
- Environment Research Institute, Shandong University, Qingdao, Shandong 266237, China
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao, Shandong 266237, China
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Mejía C D, Faican G, Zalakeviciute R, Matovelle C, Bonilla S, Sobrino JA. Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador. Heliyon 2024; 10:e28152. [PMID: 38560184 PMCID: PMC10979269 DOI: 10.1016/j.heliyon.2024.e28152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 02/27/2024] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.
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Affiliation(s)
- Danilo Mejía C
- Grupo CATOx – CEA de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
- Carrera de Ingeniería Ambiental de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
| | - Gina Faican
- Grupo CATOx – CEA de la Universidad de Cuenca, Campus Balzay, 010207 Cuenca, Ecuador
| | - Rasa Zalakeviciute
- Grupo de Biodiversidad Medio Ambiente y Salud (BIOMAS), Universidad de Las Americas, Quito - EC 170125, Ecuador
| | - Carlos Matovelle
- Carrera de Ingeniería Ambienta de la Universidad Católica de Cuenca, Ecuador
| | - Santiago Bonilla
- Research Center for the Territory and Sustainable Habitat, Universidad Tecnológica Indoamérica, Machala y Sabanilla, 170301 Quito, Ecuador
| | - José A. Sobrino
- Gobal Change Unit (GCU), Image Processing Laboratory (IPL), University of Valencia, Spain
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Li R, Zhao J, Feng K, Tian Y. Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 920:170777. [PMID: 38331278 DOI: 10.1016/j.scitotenv.2024.170777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/10/2024]
Abstract
Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.
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Affiliation(s)
- Rumei Li
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Jinghao Zhao
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China
| | - Kun Feng
- Shanxi Low-carbon Environmental Protection Industry Group Co., Ltd., Taiyuan 030012, China; Shanxi Ecological Environment Monitoring Center, Taiyuan 030027, China
| | - Yajun Tian
- Extended Energy Big Data and Strategy Research Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China; Shandong Energy Institute, Qingdao 266101, China; Qingdao New Energy Shandong Laboratory, Qingdao 266101, China.
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Yue H, He C, Huang Q, Zhang D, Shi P, Moallemi EA, Xu F, Yang Y, Qi X, Ma Q, Bryan BA. Substantially reducing global PM 2.5-related deaths under SDG3.9 requires better air pollution control and healthcare. Nat Commun 2024; 15:2729. [PMID: 38548716 PMCID: PMC10978932 DOI: 10.1038/s41467-024-46969-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/14/2024] [Indexed: 04/01/2024] Open
Abstract
The United Nations' Sustainable Development Goal (SDG) 3.9 calls for a substantial reduction in deaths attributable to PM2.5 pollution (DAPP). However, DAPP projections vary greatly and the likelihood of meeting SDG3.9 depends on complex interactions among environmental, socio-economic, and healthcare parameters. We project potential future trends in global DAPP considering the joint effects of each driver (PM2.5 concentration, death rate of diseases, population size, and age structure) and assess the likelihood of achieving SDG3.9 under the Shared Socioeconomic Pathways (SSPs) as quantified by the Scenario Model Intercomparison Project (ScenarioMIP) framework with simulated PM2.5 concentrations from 11 models. We find that a substantial reduction in DAPP would not be achieved under all but the most optimistic scenario settings. Even the development aligned with the Sustainability scenario (SSP1-2.6), in which DAPP was reduced by 19%, still falls just short of achieving a substantial (≥20%) reduction by 2030. Meeting SDG3.9 calls for additional efforts in air pollution control and healthcare to more aggressively reduce DAPP.
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Affiliation(s)
- Huanbi Yue
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- School of International Affairs and Public Administration, Ocean University of China, Qingdao, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Chunyang He
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China.
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China.
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Beijing Normal University, Beijing, China.
- Academy of Plateau Science and Sustainability, People's Government of Qinghai Province & Beijing Normal University, Xining, China.
| | - Qingxu Huang
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Da Zhang
- College of Geography and Ocean Sciences, Yanbian University, Yanji, China.
| | - Peijun Shi
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
- Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management & Ministry of Education, Beijing Normal University, Beijing, China
- Academy of Plateau Science and Sustainability, People's Government of Qinghai Province & Beijing Normal University, Xining, China
| | - Enayat A Moallemi
- Commonwealth Scientific and Industrial Research Organization (CSIRO), Melbourne, Victoria, Australia
| | - Fangjin Xu
- Key Laboratory of Environmental Change and Natural Disasters of Chinese Ministry of Education, Beijing Normal University, Beijing, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Yang Yang
- School of International Affairs and Public Administration, Ocean University of China, Qingdao, China
- Institute of Marine Development, Ocean University of China, Qingdao, China
| | - Xin Qi
- Frontiers Science Center for Deep Ocean Multispheres and Earth System (FDOMES), Ocean University of China, Qingdao, China
| | - Qun Ma
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai, China
| | - Brett A Bryan
- School of Life and Environmental Sciences, Deakin University, Melbourne, Victoria, Australia
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Khayyam J, Xie P, Xu J, Tian X, Hu Z, Li A. Evaluating the multi-variable influence on O 3, NO 2, and HCHO using BRTs and RF model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 925:171488. [PMID: 38462000 DOI: 10.1016/j.scitotenv.2024.171488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 02/27/2024] [Accepted: 03/03/2024] [Indexed: 03/12/2024]
Abstract
This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques-Boosted Regression Trees (BRTs) and Random Forest (RF) models-we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2-9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.
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Affiliation(s)
- Junaid Khayyam
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Pinhua Xie
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China.
| | - Jin Xu
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
| | - Xin Tian
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
| | - Zhaokun Hu
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Ang Li
- Key laboratory of Environmental Optical and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
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21
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Qu Q, Wang S, Zhao B, Hu R, Liang C, Zhang H, Li S, Feng B, Hou X, Yin D, Du J, Chu Y, Zhang Y, Wu Q, Wen Y, Wu X, Hu J, Zhang S, Hao J. Response of organic aerosol in Beijing to emission reductions during the XXIV Olympic Winter Games. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:170033. [PMID: 38220000 DOI: 10.1016/j.scitotenv.2024.170033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/05/2024] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
Organic aerosol (OA) serves as a crucial component of fine particulate matter. However, the response of OA to changes in anthropogenic emissions remains unclear due to its complexity. The XXIV Olympic Winter Games (OWG) provided real atmospheric experimental conditions on studying the response of OA to substantial emission reductions in winter. Here, we explored the sources and variations of OA based on the observation of aerosol mass spectrometer (AMS) combined with positive matrix factorization (PMF) analysis in urban Beijing during the 2022 Olympic Winter Games. The influences of meteorological conditions on OA concentrations were corrected by CO and verified by deweathered model. The CO-normalized primary OA (POA) concentrations from traffic, cooking, coal and biomass burning during the OWG decreased by 39.8 %, 23.2 % and 65.0 %, respectively. Measures controlling coal and biomass burning were most effective in reducing POA during the OWG. For the CO-normalized concentration of secondary OA (SOA), aqueous-phase related oxygenated OA decreased by 51.8 % due to the lower relative humidity and emission reduction in precursors, while the less oxidized‑oxygenated OA even slightly increased as the enhanced atmospheric oxidation processes may partially offset the efficacy of emission control. Therefore, more targeted reduction of organic precursors shall be enhanced to lower atmospheric oxidation capacity and mitigate SOA pollution.
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Affiliation(s)
- Qipeng Qu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
| | - Bin Zhao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Ruolan Hu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Chengrui Liang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Haowen Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Shengyue Li
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Boyang Feng
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xuan Hou
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Dejia Yin
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jinhong Du
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yangxi Chu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yanning Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Qingru Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yifan Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Xiaomeng Wu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jingnan Hu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Shaojun Zhang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Jiming Hao
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
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Zhang L, Wang L, Wang R, Chen N, Yang Y, Li K, Sun J, Yao D, Wang Y, Tao M, Sun Y. Exploring formation mechanism and source attribution of ozone during the 2019 Wuhan Military World Games: Implications for ozone control strategies. J Environ Sci (China) 2024; 136:400-411. [PMID: 37923450 DOI: 10.1016/j.jes.2022.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 12/05/2022] [Accepted: 12/10/2022] [Indexed: 11/07/2023]
Abstract
A series of emission reduction measures were conducted in Wuhan, Central China, to ensure good air quality during the 7th Military World Games (MWG) in October 2019. To better understand the implications for ozone (O3) pollution control strategies, we applied integrated analysis approaches based on the de-weathered statistical model, parameterization methods, chemical box model, and positive matrix factorization model. During the MWG, concentrations of O3, NOx, and volatile organic compound (VOCs), OFP (O3 formation potential), LOH (OH radical loss rate) were 83 µg/m3, 43 µg/m3, 26 ppbv, 188 µg/m3, and 3.9 s-1, respectively, which were 26%, 18%, 3%, 15%, and 13% lower than pre-MWG values and 6%, 39%, 30%, 33%, and 50% lower than post-MWG values, respectively. After removing meteorological influence, O3 and its precursors during the MWG decreased largely compared with post-MWG values, and only O3, NO2, and oxygenated VOCs (OVOCs) declined compared with pre-MWG values, which revealed the emission reduction measures during the MWG played an important role for O3 decline. For six VOCs sources, the mass contributions of biomass burning and solvents usage during the MWG decreased largely compared with pre-MWG values. O3 production was sensitive to VOCs and the key species were aromatics, OVOCs, and alkenes, which originated mainly from solvents usage, biomass burning, industrial-related combustion, and vehicle exhaust. Decreasing O3 concentration during the strict control was mainly caused by OVOCs reduction due to biomass burning control. Generally, the O3 abatement strategies of Wuhan should be focused on the mitigation of high-reactivity VOCs.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
| | - Runyu Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Nan Chen
- Hubei Ecological Environment Monitoring Center Station, Wuhan 430072, China
| | - Yuan Yang
- Guizhou Research and Designing Institute of Environmental Sciences, Guizhou Academy of Environmental Science and Design, Guiyang 550081, China
| | - Ke Li
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jie Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Dan Yao
- School of Environment, Key Laboratory for Yellow River and Huai River Water Environment and Pollution Control, Ministry of Education, Henan Normal University, Xinxiang 453007, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Minghui Tao
- Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Singh A, Morley GL, Coignet C, Leach F, Pope FD, Neil Thomas G, Stacey B, Bush T, Cole S, Economides G, Anderson R, Abreu P, Bartington SE. Impacts of ambient air quality on acute asthma hospital admissions during the COVID-19 pandemic in Oxford City, UK: a time-series study. BMJ Open 2024; 14:e070704. [PMID: 38262660 PMCID: PMC10806833 DOI: 10.1136/bmjopen-2022-070704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 12/14/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVES The study aims to investigate the short-term associations between exposure to ambient air pollution (nitrogen dioxide (NO2), particulate matter pollution-particles with diameter<2.5 µm (PM2.5) and PM10) and incidence of asthma hospital admissions among adults, in Oxford, UK. DESIGN Retrospective time-series study. SETTING Oxford City (postcode areas OX1-OX4), UK. PARTICIPANTS Adult population living within the postcode areas OX1-OX4 in Oxford, UK from 1 January 2015 to 31 December 2021. PRIMARY AND SECONDARY OUTCOME MEASURES Hourly NO2, PM2.5 and PM10 concentrations and meteorological data for the period 1 January 2015 to 31 December 2020 were analysed and used as exposures. We used Poisson linear regression analysis to identify independent associations between air pollutant concentrations and asthma admissions rate among the adult study population, using both single (NO2, PM2.5, PM10) and multipollutant (NO2 and PM2.5, NO2 and PM10) models, where they adjustment for temperature and relative humidity. RESULTS The overall 5-year average asthma admissions rate was 78 per 100 000 population during the study period. The annual average rate decreased to 46 per 100 000 population during 2020 (incidence rate ratio 0.58, 95% CI 0.42 to 0.81, p<0.001) compared to the prepandemic years (2015-2019). In single-pollutant analysis, we observed a significantly increased risk of asthma admission associated with each 1 μg/m3 increase in monthly concentrations of NO2 4% (95% CI 1.009% to 1.072%), PM2.5 3% (95% CI 1.006% to 1.052%) and PM10 1.8% (95% CI 0.999% to 1.038%). However, in the multipollutant regression model, the effect of each individual pollutant was attenuated. CONCLUSIONS Ambient NO2 and PM2.5 air pollution exposure increased the risk of asthma admissions in this urban setting. Improvements in air quality during COVID-19 lockdown periods may have contributed to a substantially reduced acute asthma disease burden. Large-scale measures to improve air quality have potential to protect vulnerable people living with chronic asthma in urban areas.
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Affiliation(s)
- Ajit Singh
- School of Geography Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Gabriella L Morley
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
| | - Cécile Coignet
- NHS Oxfordshire Clinical Commissioning Group, Oxford, UK
| | - Felix Leach
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Francis D Pope
- School of Geography Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
| | - Graham Neil Thomas
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | | | - Tony Bush
- Department of Engineering Science, University of Oxford, Oxford, UK
- Apertum, Oxfordshire, UK
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24
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Li T, Li J, Xie L, Lin B, Jiang H, Sun R, Wang X, Liu B, Tian C, Li Q, Jia W, Zhang G, Peng P. In situ biomass burning enhanced the contribution of biogenic sources to sulfate aerosol in subtropical cities. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168384. [PMID: 37956844 DOI: 10.1016/j.scitotenv.2023.168384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 11/02/2023] [Accepted: 11/05/2023] [Indexed: 11/15/2023]
Abstract
Sulfurous gases released by biogenic sources play a key role in the global sulfur cycle. However, the contribution of biogenic sources to sulfate aerosol in the urban atmosphere has received little attention. Emission sources and formation process of sulfate in Guangzhou, a subtropical mega-city in China, were clarified using multiple methods, including isotope tracers and chemical markers. The δ18O of sulfate suggested that secondary sulfate was the dominant component (84 %) of sulfate aerosol, which mainly formed by transition metal ion (TMI) catalyzed oxidation (31 %) and OH radical oxidation (30 %). The factors driving secondary sulfate formation were revealed using a tree boosting model, which suggested that NH3, temperature, and oxidants were the most important factors. The δ34S of sulfate indicated that biogenic sources accounted for annual average of 26.0 % of the sulfate, which increased to 30.4 % in winter monsoon period. Rice straw burning enhanced sulfate formation by promoting the release of reduced sulfur from soil, which is rapidly converted into sulfate under a subtropical urban atmosphere with high concentration of NH3 and oxidants. This study revealed the important influence of rice straw burning on biogenic sulfur emission during the rice harvest, thereby providing insight into the sulfur cycle and regional air pollution.
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Affiliation(s)
- Tingting Li
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Jun Li
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China.
| | - Luhua Xie
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China.
| | - Boji Lin
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Hongxing Jiang
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China
| | - Rong Sun
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, PR China
| | - Xiao Wang
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Ben Liu
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Chongguo Tian
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, PR China
| | - Qilu Li
- School of Environment, Henan Normal University, Key Laboratory for Yellow River and Huai River Water Environment and Pollution Control, Ministry of Education, Henan Key Laboratory for Environmental Pollution Control, Xinxiang 453007, PR China
| | - Wanglu Jia
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China
| | - Gan Zhang
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China
| | - Ping'an Peng
- State Key Laboratory of Organic Geochemistry, State Key Laboratory of Isotope Geochemistry, Guangdong Provincial Key Laboratory of Environmental Protection and Resources Utilization, and Guangdong-Hong Kong-Macao Joint Laboratory for Environmental Pollution and Control, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, PR China; CAS Center for Excellence in Deep Earth Science, Guangzhou 510640, PR China
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25
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Rosa AH, Stubbings WA, Akinrinade OE, Jeunon Gontijo ES, Harrad S. Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:122794. [PMID: 37926413 DOI: 10.1016/j.envpol.2023.122794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 11/07/2023]
Abstract
The impact of measures to restrict population mobility during the COVID-19 pandemic on atmospheric concentrations of polycyclic aromatic hydrocarbons (PAH) and brominated flame retardants (BFRs) is poorly understood. This study analyses the effects of meteorological parameters and mobility restrictions during the COVID-19 pandemic on concentrations of PAH and BFRs at the University of Birmingham in the UK utilising a neural network (self-organising maps, SOM). Air sampling was performed using Polyurethane Foam (PUF) disk passive samplers between October 2019 and January 2021. Data on concentrations of PAH and BFRs were analysed using SOM and Spearman's rank correlation. Data on meteorological parameters (air temperature, wind, and relative humidity) and mobility restrictions during the pandemic were included in the analysis. Decabromodiphenyl ether (BDE-209) was the most abundant polybrominated diphenyl ether (PBDE) (23-91% Σ7PBDEs) but was detected at lower absolute concentrations (4.2-35.0 pg m-3) than in previous investigations in Birmingham. Air samples were clustered in five groups based on SOM analysis and the effects of meteorology and pandemic-related restrictions on population mobility could be visualised. Concentrations of most PAH decreased during the early stages of the pandemic when mobility was most restricted. SOM analysis also helped to identify the important influence of wind speed on contaminant concentrations, contributing to reduce the concentration of all analysed pollutants. In contrast, concentrations of most PBDEs remained similar or increased during the first COVID-19 lockdown which was attributed to their primarily indoor sources that were either unaffected or increased during lockdown.
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Affiliation(s)
- André Henrique Rosa
- Institute of Science and Technology, São Paulo State University (UNESP), Av. Três de Março, 511, Alto da Boa Vista, 18087-180, Sorocaba, SP, Brazil; School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - William A Stubbings
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Olumide Emmanuel Akinrinade
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; Department of Chemistry, University of Lagos, Lagos, Nigeria
| | - Erik Sartori Jeunon Gontijo
- Institute of Science and Technology, São Paulo State University (UNESP), Av. Três de Março, 511, Alto da Boa Vista, 18087-180, Sorocaba, SP, Brazil; KISTERS AG, Business Unit HydroMet, Schoemperlenstr.12a, 76185, Karlsruhe, Germany
| | - Stuart Harrad
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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26
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Arter CA, Buonocore JJ, Isakov V, Pandey G, Arunachalam S. Air pollution benefits from reduced on-road activity due to COVID-19 in the United States. PNAS NEXUS 2024; 3:pgae017. [PMID: 38292536 PMCID: PMC10825624 DOI: 10.1093/pnasnexus/pgae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 01/02/2024] [Indexed: 02/01/2024]
Abstract
On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.
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Affiliation(s)
- Calvin A Arter
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jonathan J Buonocore
- Department of Environment Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Vlad Isakov
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Gavendra Pandey
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Saravanan Arunachalam
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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27
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Zheng H, Kong S, Seo J, Yan Y, Cheng Y, Yao L, Wang Y, Zhao T, Harrison RM. Achievements and challenges in improving air quality in China: Analysis of the long-term trends from 2014 to 2022. ENVIRONMENT INTERNATIONAL 2024; 183:108361. [PMID: 38091821 DOI: 10.1016/j.envint.2023.108361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/02/2023] [Accepted: 11/29/2023] [Indexed: 01/25/2024]
Abstract
Due to the implementation of air pollution control measures in China, air quality has significantly improved, although there are still additional issues to be addressed. This study used the long-term trends of air pollutants to discuss the achievements and challenges in further improving air quality in China. The Kolmogorov-Zurbenko (KZ) filter and multiple-linear regression (MLR) were used to quantify the meteorology-related and emission-related trends of air pollutants from 2014 to 2022 in China. The KZ filter analysis showed that PM2.5 decreased by 7.36 ± 2.92% yr-1, while daily maximum 8-h ozone (MDA8 O3) showed an increasing trend with 3.71 ± 2.89% yr-1 in China. The decrease in PM2.5 and increase in MDA8 O3 were primarily attributed to changes in emission, with the relative contribution of 85.8% and 86.0%, respectively. Meteorology variations, including increased ambient temperature, boundary layer height, and reduced relative humidity, also contributed to the reduction of PM2.5 and the enhancement of MDA8 O3. The emission-related trends of PM2.5 and MDA8 O3 exhibited continuous decrease and increase, respectively, from 2014 to 2022, while the variation rates slowed during 2018-2020 compared to that during 2014-2017, highlighting the challenges in further improving air quality, particularly in simultaneously reducing PM2.5 and O3. This study recommends reducing NH3 emissions from the agriculture sector in rural areas and transport emissions in urban areas to further decrease PM2.5 levels. Addressing O3 pollution requires the reduction of O3 precursor gases based on site-specific atmospheric chemistry considerations.
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Affiliation(s)
- Huang Zheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Shaofei Kong
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China.
| | - Jihoon Seo
- Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
| | - Yingying Yan
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Yi Cheng
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Liquan Yao
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
| | - Yanxin Wang
- Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430078, China; Research Centre for Complex Air Pollution of Hubei Province, Wuhan 430078, China
| | - Tianliang Zhao
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of the China Meteorological Administration, PREMIC, Nanjing University of Information Science &Technology, Nanjing, China
| | - Roy M Harrison
- School of Geography, Earth and Environment Sciences, University of Birmingham, Birmingham B15 2TT, UK; Department of Environmental Sciences, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, PO Box 80203, Jeddah, Saudi Arabia.
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28
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Lyu Y, Zhang Q, Sun Q, Gu M, He Y, Walters WW, Sun Y, Pan Y. Revisiting the dynamics of gaseous ammonia and ammonium aerosols during the COVID-19 lockdown in urban Beijing using machine learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166946. [PMID: 37696398 DOI: 10.1016/j.scitotenv.2023.166946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/18/2023] [Accepted: 09/07/2023] [Indexed: 09/13/2023]
Abstract
The concentration of atmospheric ammonia (NH3) in urban Beijing substantially decreased during the COVID-19 lockdown (24 January to 3 March 2020), likely due to the reduced human activities. However, quantifying the impact of anthropogenic interventions on NH3 dynamics is challenging, as both meteorology and chemistry mask the real changes in observed NH3 concentrations. Here, we applied machine learning techniques based on random forest models to decouple the impacts of meteorology and emission changes on the gaseous NH3 and ammonium aerosol (NH4+) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH3 concentration. In addition, significant reductions in NOx and SO2 emissions could also elevate NH3 concentrations by favoring NH3 gas-phase partitioning. However, the observed NH3 concentration significantly decreased by 35.9 % during the lockdown, indicating a significant reduction in emissions or enhanced chemical sinks. Rapid gas-to-particle conversion was indeed found during the lockdown. Thus, the observed reduced NH3 concentrations could be partially explained by the enhanced transformation into NH4+. Therefore, the sum of NH3 and NH4+ (collectively, NHx) is a more reliable tracer than NH3 or NH4+ alone to estimate the changes in NH3 emissions. Compared to that under the scenario without lockdowns, the NHx concentration decreased by 26.4 %. We considered that this decrease represents the real decrease in NH3 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH3 only (35.9 %). This study highlights the importance of considering chemical sinks in the atmosphere when applying machine learning techniques to link the concentrations of reactive species with their emissions.
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Affiliation(s)
- Yixuan Lyu
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianqian Zhang
- National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China.
| | - Qian Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengna Gu
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, China
| | - Yuexin He
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Wendell W Walters
- Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI 02912, USA; Institute at Brown for Environment and Society, Brown University, Providence, RI 02912, USA
| | - Yele Sun
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuepeng Pan
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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29
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Zhang L, Wang L, Liu B, Tang G, Liu B, Li X, Sun Y, Li M, Chen X, Wang Y, Hu B. Contrasting effects of clean air actions on surface ozone concentrations in different regions over Beijing from May to September 2013-2020. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166182. [PMID: 37562614 DOI: 10.1016/j.scitotenv.2023.166182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Due to the nonlinear impacts of meteorology and precursors, the response of ozone (O3) trends to emission changes is very complex over different regions in megacity Beijing. Based on long-term in-situ observations at 35 air quality sites (four categories, i.e., urban, traffic, northern suburban and southern suburban sites) and satellite data, spatiotemporal variability of O3, gaseous precursors, and O3-VOCs-NOx sensitivity were explored through multiple metrics during the warm season from 2013 to 2020. Additionally, the contribution of meteorology and emissions to O3 was separated by a machine-learning-based de-weathered method. The annual averaged MDA8 O3 and O3 increased by 3.7 and 2.9 μg/m3/yr, respectively, with the highest at traffic sites and the lowest in northern suburb, and the rate of Ox (O3 + NO2) was 0.2 μg/m3/yr with the highest in southern suburb, although NO2 declined strongly and HCHO decreased slightly. However, the increment of O3 and Ox in the daytime exhibited decreasing trends to some extent. Additionally, NOx abatements weakened O3 loss through less NO titration, which drove narrowing differences in urban-suburban O3 and Ox. Due to larger decrease of NO2 in urban region and HCHO in northern suburb, the extent of VOCs-limited regime fluctuated over Beijing and northern suburb gradually shifted to transition or NOx-limited regime. Compared with the directly observed trends, the increasing rate of de-weathered O3 was lower, which was attributed to favorable meteorological conditions for O3 generation after 2017, especially in June (the most polluted month); whereas the de-weathered Ox declined except in southern suburb. Overall, clean air actions were effective in reducing the atmospheric oxidation capacity in urban and northern suburban regions, weakening local photochemical production over Beijing and suppressing O3 deterioration in northern suburb. Strengthening VOCs control and keeping NOx abatement, especially in June, will be vital to reverse O3 increase trend in Beijing.
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Affiliation(s)
- Lei Zhang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Lili Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China.
| | - Boya Liu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Guiqian Tang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Baoxian Liu
- Beijing Key Laboratory of Airborne Particulate Matter Monitoring Technology, Beijing Municipal Ecological Environmental Monitoring Center, Beijing 100048, China
| | - Xue Li
- Beijing Municipal Ecology and Environment Bureau, Beijing 100048, China
| | - Yang Sun
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Mingge Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute Chinese Academy of Sciences, Beijing 100101, China
| | - Xianyan Chen
- National Climate Center, China Meteorological Administration, Beijing 100081, China
| | - Yuesi Wang
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Hu
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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30
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Mokarram M, Taripanah F, Pham TM. Using neural networks and remote sensing for spatio-temporal prediction of air pollution during the COVID-19 pandemic. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:122886-122905. [PMID: 37979107 DOI: 10.1007/s11356-023-30859-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 10/31/2023] [Indexed: 11/19/2023]
Abstract
The study aims to monitor air pollution in Iranian metropolises using remote sensing, specifically focusing on pollutants such as O3, CH4, NO2, CO2, SO2, CO, and suspended particles (aerosols) in 2001 and 2019. Sentinel 5 satellite images are utilized to prepare maps of each pollutant. The relationship between these pollutants and land surface temperature (LST) is determined using linear regression analysis. Additionally, the study estimates air pollution levels in 2040 using Markov and Cellular Automata (CA)-Markov chains. Furthermore, three neural network models, namely multilayer perceptron (MLP), radial basis function (RBF), and long short-term memory (LSTM), are employed for predicting contamination levels. The results of the research indicate an increase in pollution levels from 2010 to 2019. It is observed that temperature has a strong correlation with contamination levels (R2 = 0.87). The neural network models, particularly RBF and LSTM, demonstrate higher accuracy in predicting pollution with an R2 value of 0.90. The findings highlight the significance of managing industrial towns to minimize pollution, as these areas exhibit both high pollution levels and temperatures. So, the study emphasizes the importance of monitoring air pollution and its correlation with temperature. Remote sensing techniques and advanced prediction models can provide valuable insights for effective pollution management and decision-making processes.
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Affiliation(s)
- Marzieh Mokarram
- Department of Geography, Faculty of Economics, Management and Social Sciences, Shiraz University, Shiraz, Iran
| | - Farideh Taripanah
- Department of Desert Control and Management, University of Kashan, Kashan, Iran
| | - Tam Minh Pham
- Research Group On "Fuzzy Set Theory and Optimal Decision-Making Model in Economics and Management", Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
- VNU School of Interdisciplinary Studies, Vietnam National University, Hanoi, 144 Xuan Thuy Str., Hanoi, 100000, Vietnam.
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31
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Song C, Liu B, Cheng K, Cole MA, Dai Q, Elliott RJR, Shi Z. Attribution of Air Quality Benefits to Clean Winter Heating Policies in China: Combining Machine Learning with Causal Inference. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17707-17717. [PMID: 36722723 PMCID: PMC10666544 DOI: 10.1021/acs.est.2c06800] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Heating is a major source of air pollution. To improve air quality, a range of clean heating policies were implemented in China over the past decade. Here, we evaluated the impacts of winter heating and clean heating policies on air quality in China using a novel, observation-based causal inference approach. During 2015-2021, winter heating causally increased annual PM2.5, daily maximum 8-h average O3, and SO2 by 4.6, 2.5, and 2.3 μg m-3, respectively. From 2015 to 2021, the impacts of winter heating on PM2.5 in Beijing and surrounding cities (i.e., "2 + 26" cities) decreased by 5.9 μg m-3 (41.3%), whereas that in other northern cities only decreased by 1.2 μg m-3 (12.9%). This demonstrates the effectiveness of stricter clean heating policies on PM2.5 in "2 + 26" cities. Overall, clean heating policies caused the annual PM2.5 in mainland China to reduce by 1.9 μg m-3 from 2015 to 2021, potentially avoiding 23,556 premature deaths in 2021.
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Affiliation(s)
- Congbo Song
- School of
Geography, Earth and Environmental Science, University of Birmingham, Birmingham, B15 2TT, U.K.
| | - Bowen Liu
- Department
of Economics, University of Birmingham, BirminghamB15 2TT, U.K.
- Department
of Strategy and International Business, University of Birmingham, BirminghamB15 2TT, U.K.
| | - Kai Cheng
- Department
of Economics, University of Birmingham, BirminghamB15 2TT, U.K.
| | - Matthew A. Cole
- Department
of Economics, University of Birmingham, BirminghamB15 2TT, U.K.
| | - Qili Dai
- State
Environmental Protection Key Laboratory of Urban Ambient Air Particulate
Matter Pollution Prevention and Control, College of Environmental
Science and Engineering, Nankai University, Tianjin300350, China
| | | | - Zongbo Shi
- School of
Geography, Earth and Environmental Science, University of Birmingham, Birmingham, B15 2TT, U.K.
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32
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Ma L, Graham DJ, Stettler MEJ. Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:18271-18281. [PMID: 37566731 PMCID: PMC10666281 DOI: 10.1021/acs.est.2c09596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.
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Affiliation(s)
- Liang Ma
- Department of Civil and Environmental
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Daniel J. Graham
- Department of Civil and Environmental
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Marc E. J. Stettler
- Department of Civil and Environmental
Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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Zhong J, Hodgson JR, James Bloss W, Shi Z. Impacts of net zero policies on air quality in a metropolitan area of the United Kingdom: Towards world health organization air quality guidelines. ENVIRONMENTAL RESEARCH 2023; 236:116704. [PMID: 37481053 DOI: 10.1016/j.envres.2023.116704] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/11/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023]
Abstract
Climate change and air pollution are closely interlinked since carbon dioxide and air pollutants are co-emitted from fossil fuel combustion. Net Zero (NZ) policies aiming to reduce carbon emissions will likely bring co-benefits in air quality and associated health. However, it is unknown whether regional NZ policies alone will be sufficient to reduce air pollutant levels to meet the latest 2021 World Health Organisation (WHO) guidelines. Here, we carried out high resolution air quality modelling for in the West Midlands region, a typical metropolitan area in the UK, to quantify the effects of different NZ policies on air quality. Results show that NZ policies will significantly improve air quality in the West Midlands, with up to 6 μg m-3 (21%) reduction in annual mean NO2 (mostly through the electrification of vehicle fleet, EV) and up to 1.4 μg m-3 (12%) reduction in annual mean PM2.5 projected for 2030 relative to levels under a "business as usual" (BAU) scenario. Under BAU, 2030 PM2.5 concentrations in most wards would be below 10 μg m-3 whilst under the Net Zero scenario, those in all wards would be below 10 μg m-3. This means that the ward averages in the West Midlands would meet the UK PM2.5 of 10 μg m-3target a decade early under the Net Zero scenario. However, no ward-level-averaged annual mean PM2.concentrations meet the 2021 WHO Air Quality guideline level of 5 μg m-3 under any scenario. Similarly for NO2 only 18 wards (8% of the region's population) are predicted to have NO2 concentrations below the 2021 WHO guideline level (10 μg m-3). Decarbonisation policies linked to Net Zero deliver substantial regional air quality benefits, but are not in isolation sufficient to deliver clean air with air pollutant levels low enough to meet the 2021 WHO guidelines.
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Affiliation(s)
- Jian Zhong
- School of Geography, Earth & Environmental Sciences, the University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - James Robert Hodgson
- School of Geography, Earth & Environmental Sciences, the University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - William James Bloss
- School of Geography, Earth & Environmental Sciences, the University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Zongbo Shi
- School of Geography, Earth & Environmental Sciences, the University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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Wan F, Hao Y, Huang W, Wang X, Tian M, Chen J. Hindered visibility improvement despite marked reduction in anthropogenic emissions in a megacity of southwestern China: An interplay between enhanced secondary inorganics formation and hygroscopic growth at prevailing high RH conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:165114. [PMID: 37379922 DOI: 10.1016/j.scitotenv.2023.165114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/19/2023] [Accepted: 06/23/2023] [Indexed: 06/30/2023]
Abstract
The PM2.5-bound visibility improvement remains challenging in China despite vigorous control on anthropogenic emissions in recent years. One critical issue could exist in the distinct physicochemical properties especially of secondary aerosol components. Taken the COVID-19 lockdown as an extreme case, we focus on the relationship between visibility, emission cuts, and secondary formation of inorganics with changing optical and hygroscopic behaviors in Chongqing, a representative city characterized with humid weather and poor diffusion conditions in Sichuan Basin, southwest of China. It is found that the increased secondary aerosol abundance (e.g., PM2.5/CO and PM2.5/PM10 as a proxy) with enhanced atmospheric oxidative capacity (e.g., O3/Ox, Ox = O3 + NO2), combined with insignificant meteorological dilution effect, might partly offset the benefit on the improved visibility from substantial reduction in anthropogenic emissions during the COVID-19 lockdown. This is in line with the efficient oxidation rates of sulfur and nitrogen (i.e., SOR, NOR), increasing more significantly with PM2.5 and relative humidity (RH) in comparison to O3/Ox. The resulted larger fraction of nitrate and sulfate (i.e., fSNA) would promote the optical enhancement (i.e., f(RH)) and mass extinction efficiency (MEE) of PM2.5, especially under highly humid conditions (e.g., RH > 80 %, with approximately half of the occurrence frequency). This could further facilitate secondary aerosol formation via aqueous-phase reaction and heterogeneous oxidation, likely due to enhanced water uptake and enlarged size/surface area upon hydration. In combination of gradually increased atmospheric oxidative capacity, this positive feedback would in turn inhibit the visibility improvement particularly at high RH environment. Considering the current air pollution complex status over China, further work on the formation mechanisms of major secondary species (e.g., sulfate, nitrate, and secondary organics), size-resolved chemical and hygroscopic properties, together with their interactions are highly recommended. Our results are hoping to assist in the atmospheric pollution complex mitigation and prevention in China.
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Affiliation(s)
- Fenglian Wan
- College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Yuhang Hao
- College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Wei Huang
- National Meteorological Center, China Meteorological Administration, Beijing, China
| | - Xinyu Wang
- College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Mi Tian
- College of Environment and Ecology, Chongqing University, Chongqing, China
| | - Jing Chen
- College of Environment and Ecology, Chongqing University, Chongqing, China; Key Laboratory of Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing, China.
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35
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Pathak M, Patel VK, Kuttippurath J. Spatial heterogeneity in global atmospheric CO during the COVID-19 lockdown: Implications for global and regional air quality policies. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 335:122269. [PMID: 37524239 DOI: 10.1016/j.envpol.2023.122269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/20/2023] [Accepted: 07/25/2023] [Indexed: 08/02/2023]
Abstract
The COVID-19 lockdown (LD) provided a unique opportunity to examine the changes in regional and global air quality. Changes in the atmospheric carbon monoxide (CO) during LD warrant a thorough analysis as CO is a major air pollutant that affects human health, ecosystem and climate. Our analysis reveals a decrease of 5-10% in the CO column during LD (April-May 2020) compared to the pre-lockdown (PreLD, March 2020) periods in regions with high anthropogenic activity, such as East China (EC), Indo-Gangetic Plain (IGP), North America, parts of Europe and Russia. However, this reduction did not occur in the regions of frequent and intense wildfires and agricultural waste burning (AWB). We find high heterogeneity in the CO column distributions, from regional to city scales during the LD period. To determine the sources of CO emissions during LD, we examined the ratios of nitrogen dioxide (NO2), sulfur dioxide (SO2) to CO for major cities in the world. This facilitated the identification of contributions from different sources; including vehicles, industries and biomass burning during LD. The comparison between CO levels during the LD and PreLD periods indicates a notable reduction in the global tropospheric CO, but no significant change in the stratosphere. It is found that CO emissions decreased during LD in the hotspot regions, but rebounded after the LD restrictions were lifted. This study, therefore, highlights the importance of policy decisions and their implementations in the global and regional scales to improve the air quality, and thus to protect public health and environment.
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Affiliation(s)
- M Pathak
- CORAL, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
| | - V K Patel
- CORAL, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
| | - J Kuttippurath
- CORAL, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India.
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36
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Hassan MA, Faheem M, Mehmood T, Yin Y, Liu J. Assessment of meteorological and air quality drivers of elevated ambient ozone in Beijing via machine learning approach. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:104086-104099. [PMID: 37698799 DOI: 10.1007/s11356-023-29665-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/30/2023] [Indexed: 09/13/2023]
Abstract
Over the past few years, surface ozone (O3) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O3 from 2015 to 2020 regularly increased from 57.32 to 62.72 μg/m3, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O3 levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O3. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO2 stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O3 levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R2) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O3 concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O3 guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O3 was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O3 and highlights the importance of future research toward devising an optimum strategy to combat growing O3 pollution in urban areas.
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Affiliation(s)
- Muhammad Azher Hassan
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
| | - Muhammad Faheem
- Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Tariq Mehmood
- Department of Environmental Engineering, Helmholtz Centre for Environmental Research - UFZ, Permoserstr. 15, D-04318, Leipzig, Germany
| | - Yihui Yin
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Junjie Liu
- Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
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37
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Lu B, Zhang Z, Jiang J, Meng X, Liu C, Herrmann H, Chen J, Xue L, Li X. Unraveling the O 3-NO X-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2023; 308:119864. [PMID: 37250918 PMCID: PMC10204281 DOI: 10.1016/j.atmosenv.2023.119864] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 04/24/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023]
Abstract
The COVID-19 pandemic promoted strict restrictions to human activities in China, which led to an unexpected increase in ozone (O3) regarding to nitrogen oxides (NOx) and volatile organic compounds (VOCs) co-abatement in urban China. However, providing a quantitative assessment of the photochemistry that leads to O3 increase is still challenging. Here, we evaluated changes in O3 arising from photochemical production with precursors (NOX and VOCS) in industrial regions in Shanghai during the COVID-19 lockdowns by using machine learning models and box models. The changes of air pollutants (O3, NOX, VOCs) during the COVID-19 lockdowns were analyzed by deweathering and detrending machine learning models with regard to meteorological and emission effects. After accounting for effects of meteorological variability, we find increase in O3 concentration (49.5%). Except for meteorological effects, model results of detrending the business-as-usual changes indicate much smaller reduction (-0.6%), highlighting the O3 increase attributable to complex photochemistry mechanism and the upward trends of O3 due to clear air policy in Shanghai. We then used box models to assess the photochemistry mechanism and identify key factors that control O3 production during lockdowns. It was found that empirical evidence for a link between efficient radical propagation and the optimized O3 production efficiency of NOX under the VOC-limited conditions. Simulations with box models also indicate that priority should be given to controlling industrial emissions and vehicle exhaust while the VOCs and NOX should be managed at a proper ratio in order to control O3 in winter. While lockdown is not a condition that could ever be continued indefinitely, findings of this study offer theoretical support for formulating refined O3 management in industrial regions in Shanghai, especially in winter.
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Affiliation(s)
- Bingqing Lu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Zekun Zhang
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Jiakui Jiang
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Xue Meng
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Chao Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Hartmut Herrmann
- Leibniz-Institut für Troposphärenforschung (IfT), Permoserstr. 15, 04318, Leipzig, Germany
| | - Jianmin Chen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
| | - Likun Xue
- Environment Research Institute, Shandong University, Qingdao, Shandong, 266237, China
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, China
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38
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Meng X, Lu B, Liu C, Zhang Z, Chen J, Herrmann H, Li X. Abrupt exacerbation in air quality over Europe after the outbreak of Russia-Ukraine war. ENVIRONMENT INTERNATIONAL 2023; 178:108120. [PMID: 37527587 DOI: 10.1016/j.envint.2023.108120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/16/2023] [Accepted: 07/26/2023] [Indexed: 08/03/2023]
Abstract
Much attention has been paid to the world economy and social situations in response to the outbreak of war between Russia and Ukraine in the context of COVID-19. However, much less attention has been paid to the detrimental effect of war on the atmospheric environment. Here, we used an extended deweathered-detrended technique to quantitatively evaluate changes in ambient NO2, O3, and PM2.5 AQI levels arising from emission changes (due to pandemic-driven lockdowns and war-related activities) in European cities. Results show pandemic-induced lockdowns mitigated regional air pollution in Europe, but the war activities led to an average increase of approximately 9.78% in PM2.5 and 10.07% in NO2, along with an average decrease of about 7.93% in O3 levels in cities near the war zones. Moreover, the regional air pollution exacerbated by the war activities has offset the improvements in air quality observed during the COVID-19 pandemic. The potential mechanism analysis show that the increase in atmospheric pollutant emissions driven by the war activities led to the complexity of chemical reactions in the mixed atmospheric system, which posed a huge challenge to the alleviation of air pollution in the region. This study highlights the urgent need for a ceasefire from an environmental perspective.
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Affiliation(s)
- Xue Meng
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
| | - Bingqing Lu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
| | - Chao Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
| | - Zekun Zhang
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
| | - Jianmin Chen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
| | - Hartmut Herrmann
- Leibniz-Institut für Troposphärenforschung (IfT), Permoserstr. 15, Leipzig, 04318, Germany
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200032, China
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39
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Zhang Q, Mao X, Wang Z, Tan Y, Zhang Z, Wu Y, Gao Y. Impact of the emergency response to COVID-19 on air quality and its policy implications: Evidence from 290 cities in China. ENVIRONMENTAL SCIENCE & POLICY 2023; 145:50-59. [PMID: 37070073 PMCID: PMC10093300 DOI: 10.1016/j.envsci.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 02/11/2023] [Accepted: 04/05/2023] [Indexed: 05/07/2023]
Abstract
The emergency response to the COVID-19 pandemic had an extreme exogenous impact on society and the economy. This paper aims to explore the impacts of the national emergency response and the subsequent emergency response termination on air quality and its policy implications through regression discontinuity design (RDD) estimation by employing panel data on daily air quality from January 1, 2019, to July 31, 2020, for 290 cities in China. The empirical results showed that the emergency response resulted in a significant decrease in most of the major pollutant concentrations within a short time frame, and the average air quality index (AQI) decreased by approximately 11.0%. The concentrations of PM2.5, PM10, SO2, NO2, and CO decreased by approximately 18.8%, 13.1%, 13.5%, 11.1% and 6.7%, respectively, while the O3 concentration did not change significantly. Further causal analysis found that mandatory traffic restrictions and the shutdown of industries were two important factors that contributed greatly to air quality improvement. Moreover, since the process of returning to normal daily activities and promoting the economy were gradual, the results showed that air pollution did not rebound immediately after the government called for the "resumption of production and work" and announced the "termination of the emergency response". Our findings suggest that to achieve a substantial and sustainable improvement in air quality, it is necessary to continuously implement strict emission control routines and take co-control measures for various VOCs precursors of ozone.
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Affiliation(s)
- Qingyong Zhang
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Xianqiang Mao
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Zhengzao Wang
- School of Economics and Management, Beijing University of Technology, Beijing 100124, China
| | - Yutong Tan
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Ziyin Zhang
- Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China
| | - Yanjie Wu
- School of Environment, Beijing Normal University, Beijing 100875, China
| | - Yubing Gao
- School of Environment, Beijing Normal University, Beijing 100875, China
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40
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Shaygan M, Mokarram M. Investigating Patterns of Air Pollution in Metropolises Using Remote Sensing and Neural Networks During the COVID-19 Pandemic. ADVANCES IN SPACE RESEARCH : THE OFFICIAL JOURNAL OF THE COMMITTEE ON SPACE RESEARCH (COSPAR) 2023:S0273-1177(23)00465-9. [PMID: 37361684 PMCID: PMC10284456 DOI: 10.1016/j.asr.2023.06.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 06/07/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023]
Abstract
The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH4), Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Dioxide (NO2), Ozone (O3), Sulfur Dioxide (SO2), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R2=0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R2=0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.
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Affiliation(s)
- M Shaygan
- Assistant Prof., Dept. of Remote Sensing & GIS, Tarbiat Modares University
| | - M Mokarram
- Associate Prof., Dep. of Geography, Faculty of Economics, Management and Social sciences, Shiraz University
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41
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Li Y, Sha Z, Tang A, Goulding K, Liu X. The application of machine learning to air pollution research: A bibliometric analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 257:114911. [PMID: 37154080 DOI: 10.1016/j.ecoenv.2023.114911] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/27/2023] [Accepted: 04/10/2023] [Indexed: 05/10/2023]
Abstract
Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.
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Affiliation(s)
- Yunzhe Li
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Zhipeng Sha
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
| | - Aohan Tang
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
| | - Keith Goulding
- Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UK
| | - Xuejun Liu
- Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
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42
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Bush T, Bartington S, Pope FD, Singh A, Thomas GN, Stacey B, Economides G, Anderson R, Cole S, Abreu P, Leach FCP. The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK. BUILDING AND ENVIRONMENT 2023; 237:110330. [PMID: 37124118 PMCID: PMC10121078 DOI: 10.1016/j.buildenv.2023.110330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 05/03/2023]
Abstract
Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9-10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and PM2.5 levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021.
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Affiliation(s)
- Tony Bush
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
- Apertum Consulting, Harwell, Oxfordshire, UK
| | - Suzanne Bartington
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Francis D Pope
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Ajit Singh
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - G Neil Thomas
- Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Brian Stacey
- Ricardo Energy and Environment, The Gemini Building, Fermi Avenue, Harwell, Didcot, OX11 0QR, UK
| | - George Economides
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Ruth Anderson
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Stuart Cole
- Oxfordshire County Council, County Hall, New Road, Oxford, OX1 1ND, UK
| | - Pedro Abreu
- Oxford City Council, Town Hall, St Aldate's, Oxford, OX1 1BX, UK
| | - Felix C P Leach
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
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Tuluri F, Remata R, Walters WL, Tchounwou PB. Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6022. [PMID: 37297626 PMCID: PMC10252722 DOI: 10.3390/ijerph20116022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.
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Affiliation(s)
- Francis Tuluri
- Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA
| | - Reddy Remata
- Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA;
| | - Wilbur L. Walters
- College of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USA;
| | - Paul B. Tchounwou
- RCMI Center for Health Disparities Research, Jackson State University, Jackson, MS 39217, USA
- RCMI Center for Urban Health Disparities Research and Innovation, Morgan State University, Baltimore, MD 21251, USA
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44
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Duan X, Yan Y, Xie K, Niu Y, Xu Y, Peng L. Impact of primary emission variations on secondary inorganic aerosol formation: Prospective from COVID-19 lockdown in a typical northern China city. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121355. [PMID: 36842622 DOI: 10.1016/j.envpol.2023.121355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/18/2023] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Hourly observations in northern China city of Taiyuan were performed to compare secondary inorganic aerosol (SIA) reaction mechanisms, and emission effects on SIA during the pre-lock and COVID-19 lock days. Emission control implemented and meteorological conditions during lock days both caused beneficial impact on air quality. NO2 showed the highest decrease ratio of -49.5%, while the relative fraction of NO3- in PM2.5 increased the most (2.7%). Source apportionment revealed the top three contributors to PM2.5 were secondary formation (SF), coal combustion (CC), and vehicle exhaust (VE) during both pre-lock and lock days. EC lock/pre were all lower than 1, suggesting the overall reduction of primary emissions during lock days, while the higher ratio of (SIA/EC) lock/pre (1.01-1.36) indicated the enhanced secondary formation in lock days. The ratio of SIA of pollution to clean days during lock periods considerably higher by 23.7% compared with that in pre-lock periods, which was indicated SIA secondary formation was more pronounced and contributed great to pollution days in lock periods though secondary formation existed in pre-lock and lock periods. Enhanced secondary formation of NO3- and SO42- during lock days might be mainly due to the increased in aqueous and gas-phase reactions, respectively. Except for SF, high contribution of VE and CC were also important for high SIA concentration in pre-lock and lock days, respectively. The decreased contribution of VE weakens its contribution to SIA formation, indicating the effectiveness of VE emission control, as confirmed during the COVID-19 pandemic. This study highlights the aqueous and gas-phase reactions for nitrate and sulfate, respectively, which contributed to heavy pollution, as well as indicated the important role of VE on SIA formation, suggesting the urgent need to further strengthen controls on vehicle emissions.
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Affiliation(s)
- Xiaolin Duan
- Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Yulong Yan
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing, 100044, China; Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing, 100044, China.
| | - Kai Xie
- Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Yueyuan Niu
- Key Laboratory of Resources and Environmental Systems Optimization, Ministry of Education, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China
| | - Yang Xu
- School for Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing, 102206, China
| | - Lin Peng
- Engineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Environment, Beijing Jiaotong University, Beijing, 100044, China; Institute of Transport Energy and Environment, Beijing Jiaotong University, Beijing, 100044, China
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45
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Ren Y, Guan X, Zhang Q, Li L, Tao C, Ren S, Wang Q, Wang W. A machine learning-based study on the impact of COVID-19 on three kinds of pollution in Beijing-Tianjin-Hebei region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 884:163190. [PMID: 37061051 PMCID: PMC10102532 DOI: 10.1016/j.scitotenv.2023.163190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/25/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023]
Abstract
Large-scale restrictions on anthropogenic activities in China in 2020 due to the Corona Virus Disease 2019 (COVID-19) indirectly led to improvements in air quality. Previous studies have paid little attention to the changes in nitrogen dioxide (NO2), fine particulate matter (PM2.5) and ozone (O3) concentrations at different levels of anthropogenic activity limitation and their interactions. In this study, machine learning models were used to simulate the concentrations of three pollutants during periods of different levels of lockdown, and compare them with observations during the same period. The results show that the difference between the simulated and observed values of NO2 concentrations varies at different stages of the lockdown. Variation between simulated and observed O3 and PM2.5 concentrations were less distinct at different stages of lockdowns. During the most severe period of the lockdowns, NO2 concentrations decreased significantly with a maximum decrease of 65.28 %, and O3 concentrations increased with a maximum increase of 75.69 %. During the first two weeks of the lockdown, the titration reaction in the atmosphere was disrupted due to the rapid decrease in NO2 concentrations, leading to the redistribution of Ox (NO2 + O3) in the atmosphere and eventually to the production of O3 and secondary PM2.5. The effect of traffic restrictions on the reduction of NO2 concentrations is significant. However, it is also important to consider the increase in O3 due to the constant volatile organic compounds (VOCs) and the decrease in NOx (NO+NO2). Traffic restrictions had a limited effect on improving PM2.5 pollution, so other beneficial measures were needed to sustainably reduce particulate matter pollution. Research on COVID-19 could provide new insights into future clean air action.
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Affiliation(s)
- Yuchao Ren
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Xu Guan
- Shandong Academy for Environmental Planning, Jinan 250101, PR China.
| | - Qingzhu Zhang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China.
| | - Lei Li
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Chenliang Tao
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Shilong Ren
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Qiao Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
| | - Wenxing Wang
- Big Data Research Center for Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266003, PR China
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Li Y, Meng Y, Zhong H. Unintended consequences of COVID-19 public policy responses on renewable energy power: evidence from OECD countries in the EU. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:46503-46526. [PMID: 36717418 PMCID: PMC9887235 DOI: 10.1007/s11356-023-25427-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Since 2020, governments around the world have implemented many types of public policies in response to the outbreak of COVID-19. These dramatic public policies have substantially changed production and consumption activities, thereby temporarily lowering electricity use and greenhouse gas emissions. This study argues that pandemic-induced public policies have unintentionally slowed the transition to renewable energy use in the EU since the decline in greenhouse gas emissions due to the lockdowns helped countries temporarily reduce their total emissions. We employ a fixed-effect model to investigate the effects of different types of COVID-19 public policy responses on electricity production, consumption, and net imports in 12 OECD countries in the EU, and we mainly focus on the share of electricity production from renewable energy sources. Among several public policy responses, stringent lockdown policies, such as workplace closures, stay-at-home requirements, and restrictions on gathering size, have negative and statistically significant impacts on electricity generation and consumption. Furthermore, countries with stringent lockdown policies are more likely to import electricity from other countries to mitigate the electricity shortages in their domestic markets. Importantly, we find that lockdown events have negative and statistically significant effects on the share of renewables in electricity production while increasing the share of fossil fuels in electricity production. In contrast, economic support policies such as income support, debt relief, and economic stimulus programs help reduce the share of fossil fuels in electricity production and decrease the net import of electricity from other countries. Our results indicate that the public policies implemented in response to the outbreak of COVID-19 have mixed effects on the transition to renewable energy sources in the EU, suggesting that the current decline in greenhouse gas emissions comes from the reduction in electricity use due to lockdown events instead of the adoption of renewable energy use and discouraging the transition to renewable energy sources.
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Affiliation(s)
- Yuan Li
- School of Management, China University of Mining and Technology, Beijing, China
- Sinopec Energy Management Co. Ltd, Beijing, China
| | - Ye Meng
- Lab for Low-Carbon Intelligent Governance (LLIG), School of Economics and Management, Beihang University, Beijing, China
| | - Hua Zhong
- Lab for Low-Carbon Intelligent Governance (LLIG), School of Economics and Management, Beihang University, Beijing, China
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Ding J, Dai Q, Fan W, Lu M, Zhang Y, Han S, Feng Y. Impacts of meteorology and precursor emission change on O 3 variation in Tianjin, China from 2015 to 2021. J Environ Sci (China) 2023; 126:506-516. [PMID: 36503777 DOI: 10.1016/j.jes.2022.03.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/05/2022] [Accepted: 03/03/2022] [Indexed: 06/17/2023]
Abstract
Deterioration of surface ozone (O3) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O3 variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algorithm were utilized to quantify the meteorological impacts on the recorded O3 trend and identify the key meteorological factors affecting O3 pollution in Tianjin, the biggest coastal port city in Northern China. After "removing" the meteorological fluctuations from the observed O3 time series, we found that variation of O3 in Tianjin was largely driven by the changes in precursors emissions. The meteorology was unfavorable for O3 pollution in period of 2015-2016, and turned out to be favorable during 2017-2021. Specifically, meteorology contributed 9.3 µg/m3 O3 (13%) in 2019, together with the increase in precursors emissions, making 2019 to be the worst year of O3 pollution since 2015. Since then, the favorable effects of meteorology on O3 pollution tended to be weaker. Temperature was the most important factor affecting O3 level, followed by air humidity in O3 pollution season. In the midday of summer days, O3 pollution frequently exceeded the standard level (>160 µg/m3) at a combined condition with relative humidity in 40%-50% and temperature > 31°C. Both the temperature and the dryness of the atmosphere need to be subtly considered for summer O3 forecasting.
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Affiliation(s)
- Jing Ding
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Qili Dai
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
| | - Wenyan Fan
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Miaomiao Lu
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Yufen Zhang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
| | - Suqin Han
- Tianjin Environmental Meteorological Center, Qi xiangtai road, Tianjin 300074, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China.
| | - Yinchang Feng
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300074, China
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48
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Kumar P, Omidvarborna H, Yao R. A parent-school initiative to assess and predict air quality around a heavily trafficked school. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160587. [PMID: 36470381 DOI: 10.1016/j.scitotenv.2022.160587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 11/19/2022] [Accepted: 11/26/2022] [Indexed: 06/17/2023]
Abstract
Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM1, PM2.5, PM10), gaseous pollutants (NO2, O3 and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM2.5 was around three-times the World Health Organisation limit. PM2.5 and NO2 exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM2.5 and NO2, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO2 dataset that performed moderately (R2, 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.
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Affiliation(s)
- Prashant Kumar
- Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom; Institute for Sustainability, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom.
| | - Hamid Omidvarborna
- Global Centre for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, Surrey, United Kingdom
| | - Runming Yao
- School of The Built Environment, University of Reading, RG6 6DF, United Kingdom; Joint International Research Laboratory of Green Buildings and Built Environments (Ministry of Education), School of the Civil Engineering, Chongqing University, Chongqing 400045, China
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49
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Zou C, Wu L, Wang Y, Sun S, Wei N, Sun B, Ni J, He J, Zhang Q, Peng J, Mao H. Evaluating traffic emission control policies based on large-scale and real-time data: A case study in central China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160435. [PMID: 36435260 DOI: 10.1016/j.scitotenv.2022.160435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 11/15/2022] [Accepted: 11/19/2022] [Indexed: 06/16/2023]
Abstract
The traffic control policies, including "Odd and Even" (OAE) and "One Day Per Week" (ODPW), were adopted in Zhengzhou, China. In this study, we use the bottom-up policy evaluation framework to capture the temporal-spatial characteristics of traffic conditions and vehicle emissions under various traffic restriction scenarios. Moreover, we use the street-scale simulation model to evaluate the effectiveness of improving air quality. Results showed that the improvements in traffic conditions led to the emission decrease by about 28.3 % for carbon monoxide (CO), 16.2 % for nitrogen oxide (NOx), 21.3 % for particulate matter (PM2.5), and 23.2 % for total hydrocarbon (THC) under OAE. During ODPW, total vehicle emissions decreased by 14.1 % for CO, 10.2 % for NOx, 13.7 % for PM2.5, and 12.4 % for THC. However, the spatial analysis indicates traffic restrictions could not significantly reduce those emissions caused by high traffic volume; besides, buses, middle-duty trucks, and heavy-duty trucks have partly offset the reduction benefit from restrictions. The air quality simulation results reveal no significant concentration decrease of CO and nitrogen dioxide (NO2) in most areas. With the update of vehicles, stricter management of high-emission vehicles, and limited coverage for implementation of policies, the traffic control policies were not as effective as before. The limitations of the restriction policies are gradually prominent, and upgrade policies are urgently needed to continuously improve urban air quality in the future.
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Affiliation(s)
- Chao Zou
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Lin Wu
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Yanan Wang
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Shida Sun
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, PR China
| | - Ning Wei
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Bin Sun
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Jingwei Ni
- Henan Tianlang Ecological Technology Co., Ltd., Zhengzhou 450000, PR China
| | - Jing He
- Henan Tianlang Ecological Technology Co., Ltd., Zhengzhou 450000, PR China
| | - Qijun Zhang
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Jianfei Peng
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China
| | - Hongjun Mao
- Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, PR China.
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50
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Chen X, Li X, Liang J, Li X, Li S, Chen G, Chen Z, Dai S, Bin J, Tang Y. Causes of the unexpected slowness in reducing winter PM 2.5 for 2014-2018 in Henan Province. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 319:120928. [PMID: 36565915 DOI: 10.1016/j.envpol.2022.120928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Toughest-ever clean air actions in China have been implemented nationwide to improve air quality. However, it was unexpected that from 2014 to 2018, the observed wintertime PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations showed an insignificant decrease in Henan Province (HNP), a region in the west of the North China Plain. Emission controls seem to have failed to improve winter air quality in HNP, which has caused great confusion in formulating the next air improvement strategy. We employed a deweathering technique to decouple the impact of meteorological conditions. The results showed that the deweathered PM2.5 trend was -3.3%/yr in winter from 2014 to 2018, which had a larger decrease than the observed concentrations (-0.9%/yr), demonstrating that emission reduction was effective at improving air quality. However, compared with the other two megacity clusters, Beijing-Tianjin-Hebei (BTH) (-8.4%/yr) and Yangtze River Delta (YRD) (-7.4%/yr), the deweathered decreasing trend of PM2.5 for HNP remained slow. The underlying mechanism driving the changes in PM2.5 and its chemical components was further explored, using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model simulations indicated that nitrate dominated the increase of PM2.5 components in HNP and the proportions of nitrate to total PM2.5 increased from 22.4% in January 2015 to 39.7% in January 2019. There are two primary reasons for this phenomenon. One is the limited control of nitrogen oxide emissions, which facilitates the conversion of nitric acid to particulate nitrate by ammonia. The other is unfavourable meteorological conditions, particularly increasing humidity, further enhancing nitrate formation through multiphase reactions. This study highly emphasizes the importance of reducing nitrogen oxide emissions owing to their impact on the formation of particulate nitrate in China, especially in the HNP region.
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Affiliation(s)
- Xuwu Chen
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China; School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha, 410205, PR China
| | - Xiaodong Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China.
| | - Jie Liang
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Xin Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Shuai Li
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Gaojie Chen
- College of Mathematics and Econometrics, Hunan University, Changsha, 410082, PR China
| | - Zuo Chen
- College of Information Science and Technology, Hunan University, Changsha, 410082, PR China
| | - Simin Dai
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Juan Bin
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
| | - Yifan Tang
- College of Environmental Science and Engineering, Hunan University, Changsha, 410082, PR China
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