Anthropogenic activities experienced a pause due to the nationwide lockdown, imposed to contain the rapid spread of COVID-19 in the third week of March 2020. The impacts of suspension of industrial activities, vehicular transport and other businesses for three months (25 March-30 June) on the environmental settings of Chennai, a coastal megacity was assessed. A significant reduction in the key urban air pollutants [PM (66.5%), PM (39.5%), NO (94.1%), CO (29%), O (45.3%)] was recorded as an immediate consequence of the reduced anthropogenic activities. Comparison of water quality of an urban river Adyar, between pre-lockdown and lockdown, showed a substantial drop in the dissolved inorganic N (47%) and suspended particulate matter (41%) during the latter period. During the pandemic, biomedical wastes in India showed an overall surge of 17%, which were predominantly plastic. FTIR-ATR analysis confirmed the polymers such as polypropylene (25.4%) and polyester (15.4%) in the personal protective equipment.
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http://dx.doi.org/10.1016/j.marpolbul.2021.112739 | DOI Listing |
Sci Rep
December 2024
College of Geography and Environment, Shandong Normal University, Jinan, 250358, China.
The urban agglomeration represents the predominant form of new urbanisation, yet the evolution of its internal spatial structure exhibits pronounced spatial and temporal heterogeneity. This study concentrates on the Bohai Rim urban agglomeration, one of three major urban agglomerations in China, which has received comparatively limited research attention but has also undergone substantial urbanisation. Therefore, we reassessed and explored the spatial-temporal evolution of the spatial structure of urban expansion using Exploratory Spatiotemporal Data Analysis (ESTDA), and summarized the driving mechanisms using Geographically and Temporally Weighted Regression (GTWR).
View Article and Find Full Text PDFClin Exp Allergy
December 2024
Department of Paediatrics, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China.
The prevalence of food allergies in China seems to be increasing, but there are limited studies describing the pattern of food allergies across the country. This review highlights regional variations observed across China, with data indicating a higher prevalence in the more economically developed eastern and southern coastal regions compared to inland areas. Egg and milk are the most common allergies among children under 3 years old; for children above 3 years old, specific food allergens also show regional differences, with shellfish allergies being more common in southern and eastern coastal areas, while wheat and fruit allergies are more prevalent in northern regions.
View Article and Find Full Text PDFEcotoxicol Environ Saf
November 2024
State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510535, China. Electronic address:
Environ Sci Pollut Res Int
October 2024
Department of Environmental Science and Engineering (ESED), Indian Institute of Technology Bombay, Mumbai, India.
Sci Rep
September 2024
China University of Geosciences (Wuhan), The Institute of Geological Survey of China University of Geosciences (Wuhan), Wuhan, 430074, People's Republic of China.
As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China's megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rely on knowledge-driven models, and data-driven models have significant potential for application, we proposed a high-accuracy Random Forest-Back Propagation Neural Network Coupling Model. By using nine evaluation factors selected based on field surveys and expert recommendations, along with disaster data, the model's predictive results indicate a 3-40% improvement in model performance metrics such as AUC, accuracy, precision, recall, and F1-score, compared to single models and traditional SVM and logistic regression models.
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