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Water-soluble organic nitrogen (WSON) affects the formation, chemical transformations, hygroscopicity, and acidity of organic aerosols as well as biogeochemical cycles of nitrogen. However, large uncertainties exist in the origins and formation processes of WSON. Submicrometer aerosol particles were collected at a suburban forest site in Tokyo in summer 2015 to investigate the relative impacts of anthropogenic and biogenic sources on WSON formations and their linkages with aerosol liquid water (ALW). The concentrations of WSON (ave. 225 ± 100 ngN m) and ALW exhibited peaks during nighttime, which showed a significant positive correlation, suggesting that ALW significantly contributed to WSON formation. Further, the thermodynamic predictions by ISORROPIA-II suggest that ALW was primarily driven by anthropogenic sulfate. Our analysis, including positive matrix factorization, suggests that aqueous-phase reactions of ammonium and reactive nitrogen with biogenic volatile organic compounds (VOCs) play a key role in WSON formation in submicrometer particles, which is particularly significant in nighttime, at the suburban forest site. The formation of WSON associated with biogenic VOCs and ALW was partly supported by the molecular characterization of WSON. The overall result suggests that ALW is an important driver for the formation of aerosol WSON through a combination of anthropogenic and biogenic sources.
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http://dx.doi.org/10.1021/acs.est.9b05849 | DOI Listing |
Child Adolesc Psychiatry Ment Health
August 2025
Department of Child and Adolescent Health Promotion, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing City, Jiangsu Province, China.
Background: This study investigates the current mental health status among children and adolescents in Jiangsu Province by analyzing symptoms of depression, anxiety, and stress using standardized psychological scales. Machine learning models were utilized to identify key influencing variables and predict mental health outcomes, aiming to establish a rapid psychological well-being assessment framework for this population.
Objective: A cross-sectional survey was conducted via random cluster sampling across 98 counties (cities/districts) in Jiangsu Province, enrolling 141,725 students (47,502 primary, 47,274 junior high, 11,619 vocational high school students, and 35,330 senior high ).
Environ Geochem Health
August 2025
CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110012, India.
Air pollution, once considered a problem of urban and industrial centers, is now increasingly impacting remote and ecologically fragile regions like the Indian Himalayas, threatening both environmental stability and public health. This study presents a comprehensive assessment of PM-bound elements across the Indian Himalayan Region, covering western (Mohal-Kullu), central (Almora and Nainital), and eastern (Darjeeling) Himalayas. Extensive sampling from January 2019 to December 2020 revealed a complex mixture of natural and anthropogenic emissions.
View Article and Find Full Text PDFEnviron Res
August 2025
MOE Key Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China. Electronic address:
Legacy organochlorine pesticides (OCPs) persist as global environmental threats despite international bans, while novel OCPs have been widely adopted as alternatives; however, the spatiotemporal dynamics and regulatory drivers of both legacy and novel OCPs in river systems remain poorly quantified. This study revealed the spatiotemporal distribution, source contributions, drivers, and historical trends of traditional and novel OCPs in 131 river sediment samples. Total OCP concentrations (42.
View Article and Find Full Text PDFSci Rep
August 2025
School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple sources, including fixed and mobile air quality sensors, meteorological inputs, satellite data, and localised demographic information. Using a combination of machine learning techniques such as Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks the system predicts pollutant concentrations and classifies air quality levels with high temporal accuracy.
View Article and Find Full Text PDFAccid Anal Prev
September 2025
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States. Electronic address:
Existing intersection safety analysis studies have primarily focused on macro-level static infrastructure and highly aggregated traffic features. The emergence of Connected Vehicle (CV) has enabled researchers to extract micro-level driving behavior attributes from CVs. Although longitudinal driving behaviors (e.
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