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Using a bottom-up estimation method, a comprehensive, high-resolution emission inventory of gaseous and particulate atmospheric pollutants for multiple anthropogenic sectors with typical local sources has been developed for the Harbin-Changchun city agglomeration (HCA). The annual emissions for CO, NOx, SO, NH, VOC, PM, PM, BC and OC during 2017 in the HCA were estimated to be 5.82 Tg, 0.70 Tg, 0.34 Tg, 0.75 Tg, 0.81Tg, 0.67 Tg, 1.59 Tg, 0.12 Tg and 0.26 Tg, respectively. For PM and SO, the emissions from industry processes were the dominant contributors representing 54.7% and 49.5%, respectively, of the total emissions, while 95.3% and 44.5% of the total NH and NOx emissions, respectively, were from or associated with agricultural activities and transportation. Spatiotemporal distributions showed that most emissions (except NH) occurred in November to March and were concentrated in the central cities of Changchun and Harbin and the surrounding cities. Open burning of straw made an important contribution to PM in the central regions of the northeastern plain during autumn and spring, while domestic coal combustion for heating purposes was significant with respect to SO and PM emissions during autumn and winter. Furthermore, based on Principal Component Analysis and Multivariable Linear Regression model, air temperature, relative humidity, electricity and energy consumption, and the urban and rural population were optimized to be representative indicators for rapidly assessing the magnitude of regional atmospheric pollutants in the HCA. Such indicators and equations were demonstrated to be useful for local atmospheric environment management.
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http://dx.doi.org/10.1016/j.jes.2020.11.026 | DOI Listing |
J Hazard Mater
September 2025
Faculty of Resource and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; National Key Laboratory of Uranium Resources Prospecting and Nuclear Remote Sensing, East China University of Technology, Nanchang 330000, China.
Despite China being the world's largest producer of non-ferrous metals, a comprehensive understanding of heavy metal pollution from this industry is still lacking. This study examines the spatial coupling between heavy metal (Cd, Hg, As, Pb, and Cr) emission hotspots in China's non-ferrous metal mining industry (NFMMI), non-ferrous metal smelting and processing industry (NFMSPI) and environmental media- sensitive hotspots (water body density, cultivated land concentration, and atmospheric PM2.5) to characterize the multi-media pollution risks.
View Article and Find Full Text PDFChem Pharm Bull (Tokyo)
September 2025
Laboratory of Public Health, Faculty of Pharmacy, Kindai University, 3-4-1 Kowakae, Higashi-Osaka, Osaka, 577-8502, Japan.
This study evaluated the cadmium (Cd) adsorption characteristics of sugarcane bagasse (BG) calcined at different temperatures (200-1000°C). The point of zero charge (pH) of the BGs ranged from 4.3 to 6.
View Article and Find Full Text PDFAnal Chim Acta
November 2025
Department of Obstetrics, The Second Hospital of Shandong University, Jinan, 250033, PR China. Electronic address:
Background: Sulfur dioxide (SO) is recognized as a major atmospheric pollutant and its excessive emissions can pose a great threat to the environment, flora and fauna, and human health. Long-term exposure to excessive SO can cause chronic poisoning, leading to neurological disorders and cardiovascular diseases. However, there are two sides to everything.
View Article and Find Full Text PDFJ Contam Hydrol
September 2025
Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Leninsky Pr. 31-4, 119071 Moscow, Russia.
Lead is an extremely hazardous pollutant that poses a severe threat to the ecosystem. It enters the atmosphere in the form of nano- and microparticles and is then carried by wind and water. These particles easily dissolve in water, turning into ions which are easily absorbed by living organisms.
View Article and Find Full Text PDFEnviron Int
September 2025
School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China.
Sichuan Basin (SCB) is a critical region in China facing the dual pressures of air pollution and population aging. This study constructed high resolution (1 km) PM datasets for SCB using advanced machine learning approaches - Super Resolution Generative Adversarial Networks (SRGAN) and Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM). Evaluation results demonstrate good performance of the machine learning model (SRGAN: R = 0.
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