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Li B, Ni J, Liu J, Zhao Y, Liu L, Jin J, He C. Spatiotemporal patterns of surface ozone exposure inequality in China. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:265. [PMID: 38351419 DOI: 10.1007/s10661-024-12426-3] [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: 09/16/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.
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Affiliation(s)
- Bin Li
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Jinmian Ni
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Jianhua Liu
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Yue Zhao
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Lijun Liu
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Jiming Jin
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China
| | - Chao He
- College of Resources and Environment, Yangtze University, Wuhan, 430100, China.
- Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, 430100, China.
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Jiang S, Yu ZG, Anh VV, Lee T, Zhou Y. An ensemble multi-scale framework for long-term forecasting of air quality. CHAOS (WOODBURY, N.Y.) 2024; 34:013110. [PMID: 38198680 DOI: 10.1063/5.0172382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024]
Abstract
The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.
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Affiliation(s)
- Shan Jiang
- School of Science, Hunan University of Technology and Business, Changsha, Hunan 410205, China
| | - Zu-Guo Yu
- National Center for Applied Mathematics in Hunan and Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105, People's Republic of China
| | - Vo V Anh
- School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia
- Department of Mathematics, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
| | - Taesam Lee
- Department of Civil Engineering, Gyeongsang National University, Jinju, GyeongNam 52828, South Korea
| | - Yu Zhou
- School of Urban & Regional Science and Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
- Institute of Future Cities, The Chinese University of Hong Kong, Shatin, Hong Kong, China
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Xu CQ, Hu JJ, Zhang Z, Zhang XM, Wang WB, Cui ZN. Quantifying the contributions of natural and anthropogenic dust sources in Shanxi Province, northern China. CHEMOSPHERE 2023; 344:140280. [PMID: 37758087 DOI: 10.1016/j.chemosphere.2023.140280] [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: 07/18/2023] [Revised: 09/20/2023] [Accepted: 09/24/2023] [Indexed: 10/03/2023]
Abstract
Dust storms have direct or indirect impacts on climate change and human health. Identifying and quantifying natural/anthropogenic dust sources can facilitate effective prevention and control of dust events. Based on surface real-time PM10 monitoring data, satellite remote sensing and the HYSPLIT model, this study determined the specific timing, coverage and sources of dust events in Shanxi Province, Northern China. Thus, a composite fingerprinting technique was established to quantify potential dust sources and dust contributions of single dust events. The dust oxidation model was validated, indicating that the composite fingerprinting technique was well suited to the study region. The results show that natural dust sources (67%) contributed more to the study region than anthropogenic dust sources. They were mainly from the northwest and north of the study region. Particularly, the contributions of Taiyuan (TY) and Linfen (LF) accounted for the largest (82%) and smallest (55%) proportions, respectively, both exceeding 50%. Anthropogenic dust sources contributed 33%, mainly from the east and south of the study region. The contribution of anthropogenic dust sources increased in the study region from north to south. In terms of potential dust sources, the Tengger Desert and Badain Jaran Desert (TDBD) contributed the most (26%), followed by the Otindag Sandy Land (OL) (22%). The Taklimakan Desert (TD) contributed the least (2%). The Middle Farmland region of the Hexi Corridor (HMF) in the west (15%) had the largest proportion of anthropogenic dust sources. Differences in the regional contribution of potential dust sources mainly resulted from winter winds, surface drought severity and particle size. At an insignificant distance from the study region, the contribution of potential dust sources was larger in the west than in the east and increased from south to north overall. These methods and findings can contribute to improving the ecological environment in Northern China.
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Affiliation(s)
- C Q Xu
- College of Geographical Science, Shanxi Normal University, Taiyuan, 030031, China; Institute of Desert Meteorology, China Meteorological Administration, Taklimakan National Field Scientific Observation and Research Station of Desert Meteorology, Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Taklimakan Desert Meteorology Field Experiment Station, Field Scientific Experiment Base of Akdala Atmospheric Background, Urumqi, 830002, China.
| | - J J Hu
- College of Geographical Science, Shanxi Normal University, Taiyuan, 030031, China
| | - Z Zhang
- School of Ecology and Environment, YuZhang Normal University, Nanchang, 330022, China
| | - X M Zhang
- Institute of Ecological Civilization, Jiangxi University of Finance and Economics, Nanchang, 330013, China
| | - W B Wang
- Elion Resources Group Co., Ltd, NO.15 Guanghua Road, Chaoyang District, Beijing, 100026, China
| | - Z N Cui
- Elion Resources Group Co., Ltd, NO.15 Guanghua Road, Chaoyang District, Beijing, 100026, China
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Wang D, Wang Y, Li X, Shen L, Zhang C, Ma Y, Zhao Z. Modeling Impacts of Urbanization on Winter Boundary Layer Meteorology and Aerosol Pollution in the Central Liaoning City Cluster, China. TOXICS 2023; 11:683. [PMID: 37624188 PMCID: PMC10459236 DOI: 10.3390/toxics11080683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023]
Abstract
The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.
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Affiliation(s)
- Dongdong Wang
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; (D.W.)
- Key Opening Laboratory for Northeast China Cold Vortex Research, Shenyang 110166, China
| | - Yangfeng Wang
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; (D.W.)
- Key Opening Laboratory for Northeast China Cold Vortex Research, Shenyang 110166, China
| | - Xiaolan Li
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; (D.W.)
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Lidu Shen
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Chenhe Zhang
- Liaoning Meteorological Observatory, Shenyang 110166, China
| | - Yanjun Ma
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; (D.W.)
| | - Ziqi Zhao
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China; (D.W.)
- Key Opening Laboratory for Northeast China Cold Vortex Research, Shenyang 110166, China
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Calatayud V, Diéguez JJ, Agathokleous E, Sicard P. Machine learning model to predict vehicle electrification impacts on urban air quality and related human health effects. ENVIRONMENTAL RESEARCH 2023; 228:115835. [PMID: 37019297 DOI: 10.1016/j.envres.2023.115835] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 05/16/2023]
Abstract
Air pollution is a prevailing environmental problem in cities worldwide. The future vehicle electrification (VE), which in Europe will be importantly fostered by the ban of thermal engines from 2035, is expected to have an important effect on urban air quality. Machine learning models represent an optimal tool for predicting changes in air pollutants concentrations in the context of future VE. For the city of Valencia (Spain), a XGBoost (eXtreme Gradient Boosting package) model was used in combination with SHAP (SHapley Additive exPlanations) analysis, both to investigate the importance of different factors explaining air pollution concentrations and predicting the effect of different levels of VE. The model was trained with 5 years of data including the COVID-19 lockdown period in 2020, in which mobility was strongly reduced resulting in unprecedent changes in air pollution concentrations. The interannual meteorological variability of 10 years was also considered in the analyses. For a 70% VE, the model predicted: 1) improvements in nitrogen dioxide pollution (-34% to -55% change in annual mean concentrations, for the different air quality stations), 2) a very limited effect on particulate matter concentrations (-1 to -4% change in annual means of PM2.5 and PM10), 3) heterogeneous responses in ground-level ozone concentrations (-2% to +12% change in the annual means of the daily maximum 8-h average concentrations). Even at a high VE increase of 70%, the 2021 World Health Organization Air Quality Guidelines will be exceeded for all pollutants in some stations. VE has a potentially important impact in terms of reducing NO2-associated premature mortality, but complementary strategies for reducing traffic and controlling all different air pollution sources should also be implemented to protect human health.
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Affiliation(s)
- V Calatayud
- Fundación CEAM, Parque Tecnológico, C/Charles R. Darwin, 14, Paterna, Spain.
| | - J J Diéguez
- Fundación CEAM, Parque Tecnológico, C/Charles R. Darwin, 14, Paterna, Spain
| | - E Agathokleous
- Institute of Ecology, Key Laboratory of Agrometeorology of Jiangsu Province, School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - P Sicard
- ARGANS, 260 Route Du Pin Montard, Biot, France
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Ma Y, Cheng B, Li H, Feng F, Zhang Y, Wang W, Qin P. Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 320:121090. [PMID: 36649879 PMCID: PMC9840128 DOI: 10.1016/j.envpol.2023.121090] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 05/05/2023]
Abstract
Air pollution is a serious environmental problem that damages public health. In the present study, we used the segmentation function to improve the health risk-based air quality index (HAQI) and named it new HAQI (NHAQI). To investigate the spatiotemporal distribution characteristics of air pollutants and the associated health risks in Shaanxi Province before (Period I, 2015-2019) and after (Period II, 2020-2021) COVID-19. The six criteria pollutants were analyzed between January 1, 2015, and December 31, 2021, using the air quality index (AQI), aggregate AQI (AAQI), and NHAQI. The results showed that compared with AAQI and NHAQI, AQI underestimated the combined effects of multiple pollutants. The average concentrations of the six criteria pollutants were lower in Period II than in Period I due to reductions in anthropogenic emissions, with the concentrations of PM2.5 (particulate matter ≤2.5 μm diameter), PM10 (PM ≤ 10 μm diameter) SO2, NO2, O3, and CO decreased by 23.5%, 22.5%, 45.7%, 17.6%, 2.9%, and 41.6%, respectively. In Period II, the excess risk and the number of air pollution-related deaths decreased considerably by 46.5% and 49%, respectively. The cumulative population distribution estimated using the NHAQI revealed that 61% of the total number of individuals in Shaanxi Province were exposed to unhealthy air during Period I, whereas this proportion decreased to 16% during Period II. Although overall air quality exhibited substantial improvements, the associated health risks in winter remained high.
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Affiliation(s)
- Yuxia Ma
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China.
| | - Bowen Cheng
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Heping Li
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Fengliu Feng
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Yifan Zhang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Wanci Wang
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
| | - Pengpeng Qin
- College of Atmospheric Sciences, Key Laboratory of Semi-Arid Climate Change, Ministry of Education, Lanzhou University, Lanzhou, 730000, China
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Musarurwa H, Tavengwa NT. Recyclable polysaccharide/stimuli-responsive polymer composites and their applications in water remediation. Carbohydr Polym 2022; 298:120083. [DOI: 10.1016/j.carbpol.2022.120083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 11/02/2022]
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Chen Y, Li D, Karimian H, Wang S, Fang S. The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta. CHEMOSPHERE 2022; 308:136301. [PMID: 36064028 DOI: 10.1016/j.chemosphere.2022.136301] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/18/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R2 ˃ 0.57), summer (R2 ˃ 0.76) and autumn (R2 ˃ 0.38). However, in winter the composite model outperformed others (R2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41<R2 <0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.
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Affiliation(s)
- Youliang Chen
- Department of Geo-informatics, Central South University, Changsha, 410000, China; School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Dan Li
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Hamed Karimian
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China.
| | - Shiteng Wang
- School of Civil and Surveying Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Shuwei Fang
- Institute of Remote Sensing and Geographic Information System, Peking University, Beijing, 100871, China
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