Objective: To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard (AQS) and the reduction of particulate matter emission to meet the AQS from the statistical properties (probability density functions) of air pollutant concentration.
Methods: The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi'an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the lognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method.
Results: The daily PM10 average concentration in the 5 cities was fitted using the lognormal distribution. The exceeding duration was predicted, and the estimated PM10 emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AQS.
Conclusion: Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.
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http://dx.doi.org/10.3967/0895-3988.2013.08.002 | DOI Listing |
Sensors (Basel)
December 2024
Shanghai Institute of Satellite Engineering, Shanghai 201109, China.
Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig 04318, Germany. Electronic address:
Particle-bound mercury (PBM) concentrations in particulate matter (PM), PM10 and PM2.5, were investigated during dust and non-dust events at urban and rural sites in Cabo Verde, Africa. During dust events, PBM averaged 35.
View Article and Find Full Text PDFPLoS One
January 2025
Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10).
View Article and Find Full Text PDFEcotoxicol Environ Saf
January 2025
Division of Toxicology, Institute for Medical Research and Occupational Health, Zagreb 10000, Croatia.
Measurements of polycyclic aromatic hydrocarbons (PAHs) were simultaneously carried out at three different urban locations in Croatia (Zagreb, Slavonski Brod and Vinkovci) characterized as urban residential (UR), urban industrial (UI) and urban background (UB), respectively. This was done in order to determine seasonal and spatial variations, estimate dominant pollution sources for each area and estimate the lifetime carcinogenic health risks from atmospheric PAHs. Mass concentrations of PAHs showed seasonal variation with the highest values during the colder period and the lowest concentration during the warmer period of the year.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Statistics, Faculty of Science, Fasa University, Fasa, 74616-86131, Iran.
Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O, NO, SO, PM, and PM, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!