Three theoretical parent frequency distributions; lognormal, Weibull and gamma were used to fit the complete set of PM10 data in central Taiwan. The gamma distribution is the best one to represent the performance of high PM10 concentrations. However, the parent distribution sometimes diverges in predicting the high PM10 concentrations. Therefore, two predicting methods, Method I: two-parameter exponential distribution and Method II: asymptotic distribution of extreme value, were used to fit the high PM10 concentration distributions more correctly. The results fitted by the two-parameter exponential distribution are better matched with the actual high PM10 data than that by the parent distributions. Both of the predicting methods can successfully predict the return period and exceedances over a critical concentration in the future year. Moreover, the estimated emission source reductions of PM10 required to meet the air quality standard by Method I and Method II are very close. The estimated emission source reductions of PM10 range from 34% to 48% in central Taiwan.
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http://dx.doi.org/10.1016/j.chemosphere.2003.10.012 | DOI Listing |
J Environ Manage
January 2025
Section of Basic Research in Horticulture, Department of Plant Protection, Institute of Horticultural Sciences, Warsaw University of Life Sciences-SGGW (WULS-SGGW), Nowoursynowska 159, 02-776, Warsaw, Poland; Centre for Climate Research SGGW, Warsaw University of Life Sciences-SGGW (WULS-SGGW), Nowoursynowska 166, 02-787, Warsaw, Poland. Electronic address:
Air pollution is highest in winter. The high concentration of particulate matter (PM) and trace elements (TE) after the growing season is influenced by increased pollutant emissions, unfavorable meteorological conditions, and the low efficiency of air phytofiltration. Plants that can remove pollutants from the air during the growing season are leafless in autumn/winter, and therefore unable to capture PM/TE effectively.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran.
As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM and PM), CO, NO, SO, and O while decreasing overfitting.
View Article and Find Full Text PDFEnviron Pollut
December 2024
College of Architecture, Chang'an University, Xi'an 710061, Shaanxi, China.
Exposure to air pollution significantly elevates the risk of disease among urban populations. Improving city air quality requires not only traditional emission reduction strategies but also a focus on the intricate impacts of the urban built environment and meteorological elements. The complexity and diversity of factors within the urban built environment pose significant challenges to pollution control.
View Article and Find Full Text PDFEnviron Res
December 2024
School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar, 14210, Mongolia. Electronic address:
Prev Med
December 2024
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China. Electronic address:
Objective: This study aimed to explore the associations between short-term air pollution exposure and acute exacerbation of chronic bronchitis (AECB).
Methods: AECB data were collected from hospital surveillance systems in Shanghai, China, during 2018-2022. Exposure pollution data were obtained from China high resolution high quality near-surface air pollution datasets and assigned to individuals based on their residential addresses.
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