Dependence of urban air pollutants on meteorology.

Particulate air pollution in six Asian cities: Spatial and temporal distributions, and associated sources

The daily average PM2.5 concentration forecast is a leading component nowadays in air quality research, which is necessary to perform in order to assess the impact of air on the health and welfare of every living being. The present work is aimed at analyzing and benchmarking a neural-network approach to the prediction of average PM2.5 concentrations. The model thus obtained will be indispensable, as a control tool, for the purpose of preventing dangerous situations that may arise. To this end we have obtained data and measurements based on samples taken during the early hours of the day. Results from three different topologies of neural networks were compared so as to identify their potential uses, or rather, their strengths and weaknesses: Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Square Multilayer Perceptron (SMLP). Moreover, two classical models were built (a persistence model and a linear regression), so as to compare their results with the ones provided by the neural network models. The results clearly demonstrated that the neural approach not only outperformed the classical models but also showed fairly similar values among different topologies. Moreover, a differential behavior in terms of stability and length of the training phase emerged during testing as well. The RBF shows up to be the network with the shortest training times, combined with a greater stability during the prediction stage, thus characterizing this topology as an ideal solution for its use in environmental applications instead of the widely used and less effective MLP.

Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua)

Urban air quality in the Asian region.

. The trends and variability of PM 10 , PM 2.5 and PM coarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM 2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PM coarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM 2.5 except for one station, which exhibits a stationary point. Considering PM coarse , concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM 2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM 10 and PM 2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m −3 yr −1 for PM 10 and PM 2.5 size fractions. PM coarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PM coarse in addition to PM 2.5 can result in a faster decrease of PM 10 in the future. The trends of the 90th quantile of PM 10 and PM 2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m −3 yr −1 ).

/pdf/one-decade-of-parallel-fine-pm-2-5-and-coarse-pm-10-pm-2-5-4f3skpfxls.pdf

One decade of parallel fine (PM 2.5 ) and coarse (PM 10 ???PM 2.5 ) particulate matter measurements in Europe: trends and variability

Influence of meteorological conditions on PM2.5 and PM2.5−10 concentrations during the monsoon season in Hanoi, Vietnam

Temporal trends of sediment accumulation in the Xuan Thuy Natural Wetland Reserve (Ba Lat coastal area of the Red River, Vietnam) and implications for future coastal wetland development

V.T. Bac

Papers

Influence of meteorological conditions on PM2.5 and PM2.5−10 concentrations during the monsoon season in Hanoi, Vietnam

Temporal trends of sediment accumulation in the Xuan Thuy Natural Wetland Reserve (Ba Lat coastal area of the Red River, Vietnam) and implications for future coastal wetland development

Trace element characterization and source identification of particulate matter of different sizes in Hanoi, Vietnam