Size fractionated particulate matter (PM) samples (including PM2.5 and PM10) were collected at Peking University in Northwestern Beijing, China for a 2 week period prior to the Olympics, during the 2 week period of the Olympics, and for a 4 week period following the 2008 Olympics, during both source control and nonsource control periods. PM10 concentrations in this study were high correlated with, but a factor of 1.3 times higher than, the Beijing Environmental Protection Bureau's PM10 concentrations at near-by sites because of differences in the measurement methods used. The mean PM2.5 and PM10 concentrations were statistically different, and lower by 31 and 35%, during the Olympic period compared to the non-Olympic period. However, the PM concentrations were not statistically different between the source control and nonsource control periods. While meteorological parameters (air masses from the south and precipitation) accounted for 40% of the total variation in PM10 concentration, source control accounted for 16%, suggesting that meteorology accounted for more of the variation in PM concentration than source control measures. The PM10 concentrations in Beijing during the Olympic period were 2.9, 3.5, and 1.9 times higher than those in Atlanta, Sydney, and Athens. In addition, the PM2.5 and PM10 concentrations during the Olympic period exceeded the WHO 24-h guideline 100% and 81% of the time, respectively. Finally, the PM10 concentrations in October, November, and December 2008 were reduced by 9-27% compared to the same months in 2007, suggesting that the Olympic source control efforts (and possibly a down turn in the economy) have resulted in lower PM10 concentrations in Beijing.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739612 | PMC |
http://dx.doi.org/10.1021/es9007504 | 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!