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Wang Y, Wang Z, Zhang Y, Zhang J, Shen J, Tan Y, Zhang Y, Peng M, Zheng H, Zhang Y. Developing and validating intracity spatiotemporal air quality health index in eastern China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175556. [PMID: 39153638 DOI: 10.1016/j.scitotenv.2024.175556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/13/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
BACKGROUND Recently pilot published city-level air quality health index (AQHI) provides a useful tool for communicating short-term health risks of ambient air pollution, but fails to account for intracity spatial heterogeneity in exposure and associated population health impacts. This study aims to develop the intracity spatiotemporal AQHI (ST-AQHI) via refined air pollution-related health risk assessments. METHODS A three-stage analysis was conducted through integrating province-wide death surveillance data and high-resolution gridded estimates of air pollution and climate factors spanning 2016-2019 in Jiangsu Province, eastern China. First, an individual-level case-crossover design was employed to quantify the short-term risk of nonaccidental mortality associated with residential exposure to individual pollutant (i.e., PM2.5, NO2, O3, and SO2). Second, we accumulated and scaled the excess risks arising from multiple pollutants to formulate daily gridded ST-AQHI estimates at 0.1° × 0.1°, dividing exposure-related risks into low (0-3), moderate (4-6), high (7-9), and extreme high (10+) levels. Finally, the effectiveness of ST-AQHI as composite risk communication was validated through checking the dose-response associations of individual ST-AQHI exposure with deaths from nonaccidental and major cardiopulmonary causes via repeating case-crossover analyses. RESULTS We analyzed a total of 1,905,209 nonaccidental death cases, comprising 785,567 from circulatory diseases and 247,336 from respiratory diseases. In the first-stage analysis, for each 10-μg/m3 rise in PM2.5, NO2, O3, and SO2 exposure at lag-01 day, population risk of nonaccidental death was increased by 0.8% (95% confidence interval: 0.7%, 0.9%), 1.9% (1.7%, 2.0%), 0.4% (0.3%, 0.5%), and 4.1% (3.7%, 4.5%), respectively. Spatiotemporal distribution of ST-AQHI exhibited a consistent declining trend throughout the study period (2016-2019), with annual average ST-AQHI decreasing from 5.2 ± 1.3 to 4.0 ± 1.0 and high-risk days dropping from 15.8% (58 days) to 1.6% (6 days). Exposure associated health risks showed great intracity- and between-city heterogeneities. In the validation analysis, ST-AQHI demonstrated approximately linear, threshold-free associations with multiple death events from nonaccidental and major cardiopulmonary causes, suggesting excellent performance in predicting exposure-related health risks. Specifically, each 1-unit rise in ST-AQHI was significantly associated with an excess risk of 2.0% (1.8%, 2.1%) for nonaccidental mortality, 2.3% (2.1%, 2.6%) for overall circulatory mortality, and 2.7% (2.3%, 3.1%) for overall respiratory mortality, as well as 1.7%-3.0% for major cardiopulmonary sub-causes. CONCLUSIONS ST-AQHI developed in this study performed well in predicting intracity spatiotemporal heterogeneity of death risks related to multiple air pollutants, and may hold significant practical importance in communicating air pollution-related health risks to the public at the community scales.
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Affiliation(s)
- Yixiang Wang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Zhen Wang
- Department of Pediatrics, Hubei Provincial Clinical Research Center for Umbilical Cord Blood Hematopoietic Stem Cells, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, Hubei, China
| | - Yuanyuan Zhang
- Wuhan Center for Disease Control and Prevention, Wuhan 430022, China
| | - Jingjing Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Jiajun Shen
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yuxi Tan
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Yalin Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China
| | - Minjin Peng
- Department of Outpatient, Hubei Provincial Clinical Research Center for Precision Diagnosis and Treatment of Liver Cancer, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, China.
| | - Yunquan Zhang
- Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China.
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Natarajan SK, Shanmurthy P, Arockiam D, Balusamy B, Selvarajan S. Optimized machine learning model for air quality index prediction in major cities in India. Sci Rep 2024; 14:6795. [PMID: 38514669 PMCID: PMC10958024 DOI: 10.1038/s41598-024-54807-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/16/2024] [Indexed: 03/23/2024] Open
Abstract
Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.
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Affiliation(s)
- Suresh Kumar Natarajan
- School of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India
| | - Prakash Shanmurthy
- School of Computer Science and Engineering and Information Science, Presidency University, Bengaluru, Karnataka, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
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García G, Martín-Gisbert L, Candal-Pedreira C, Ruano-Ravina A. Does lung function mediate the role of environmental pollution on overall and cardiovascular disease mortality? ERJ Open Res 2024; 10:00141-2024. [PMID: 38686179 PMCID: PMC11057502 DOI: 10.1183/23120541.00141-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 05/02/2024] Open
Abstract
Lung function and its mediation role on environmental pollution and mortality https://bit.ly/3pdVYJX.
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Affiliation(s)
- Guadalupe García
- Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Lucía Martín-Gisbert
- Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Cross-disciplinary Research in Environmental Technologies (CRETUS), University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Cristina Candal-Pedreira
- Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela), Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública), Madrid, Spain
| | - Alberto Ruano-Ravina
- Preventive Medicine and Public Health, University of Santiago de Compostela, Santiago de Compostela, Spain
- Cross-disciplinary Research in Environmental Technologies (CRETUS), University of Santiago de Compostela, Santiago de Compostela, Spain
- Health Research Institute of Santiago de Compostela (Instituto de Investigación Sanitaria de Santiago de Compostela), Santiago de Compostela, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública), Madrid, Spain
- University of Santiago de Compostela–Galician Department of Health, Santiago de Compostela, Spain
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Liu F, Li A, Bilal M, Yang Y. Synergistic effect of combating air pollutants and carbon emissions in the Yangtze River Delta of China: spatial and temporal divergence analysis and key influencing factors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-32197-1. [PMID: 38300496 DOI: 10.1007/s11356-024-32197-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 01/22/2024] [Indexed: 02/02/2024]
Abstract
Synergizing the reduction of air pollutants and carbon emissions (APCE) has become a critical tactic alternative to address the issue of climate change. Taking the Yangtze River Delta (YRD) region of China as a case study, this paper explores the spatial and temporal distribution pattern of the coupling coordination degree (CCD) of combating APCE from 2011 to 2022, analyzes the dynamic change in CCD using the convergence test, and investigates the key factors affecting CCD via the Tobit regression model. The results show that (1) from 2011 to 2022, the air pollutants (AP) and CO2 emission (CE) in the YRD region decrease at the annual rate of 10.32% and 0.85%, respectively; (2) the CCD of reducing APCE in the YRD presents a W-shaped fluctuation before 2016 and then steps into a steady increase status after 2016; (3) the order of CCD in four provincial-level units by 2022 is Shanghai > Zhejiang > Jiangsu > Anhui. The proportion of cities where the CCD of reducing APCE enters the high-coordination period has reached 87.8%; and (4) the Tobit regression results affirm that economic growth, industrial structure, and green technological innovation exacerbate the CCD of combating APCE, while opening-up level mitigates it. The findings offer policymakers valuable insights into the importance of pursuing collaborative governance over APCE and ensuring sustainable development.
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Affiliation(s)
- Fang Liu
- School of Economics and Management, Anhui Polytechnic University, Anhui Province, No. 8 Beijing Middle Road, Wuhu City, 241000, China
| | - Anqi Li
- School of Economics and Management, Anhui Polytechnic University, Anhui Province, No. 8 Beijing Middle Road, Wuhu City, 241000, China
| | - Muhammad Bilal
- School of Economics and Management, Anhui Polytechnic University, Anhui Province, No. 8 Beijing Middle Road, Wuhu City, 241000, China.
| | - Yuwei Yang
- School of Economics and Management, Anhui Polytechnic University, Anhui Province, No. 8 Beijing Middle Road, Wuhu City, 241000, China
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