Relations between Climatic Changes and High Pollution Levels in Bulgaria

One of the important consequences of the climatic changes is the potential danger of increasing the concentrations of some pollutants, which may cause damages to humans, animals and plants. Therefore, it is worthwhile to study carefully the impact of future climate changes on the high pollution levels. The major topic of the discussion in this paper is the increase of some ozone levels in Bulgaria, but several related topics are also discussed. The particular mathematical tool applied in this study is a large-scale air pollution model, the Unified Danish Eulerian Model (UNI- DEM), which was successfully used in several investigations related to potentially dangerous pollution levels in several European countries. This model is described by a non-linear system of partial differential equations, which is solved numerically by using (a) advanced numerical algorithms and (b) modern computer architectures. Moreover, (c) the code is parallelized and (d) the cache memories of the available computers are efficiently utilized. It is shown that in Bulgaria, as in the other European countries, the climatic changes will result in permanent increases of some quantities related to the ozone pollution. The important issue is that in our study the changes of the dangerous pollution levels are followed year by year. In this way, an attempt is made both to capture the effect of the interannual variations of the meteorological conditions on the levels of the ozone concentrations and to follow directly the influence of the climatic changes on the pollution levels. Moreover, the sensitivity of the pollution levels to variations of the human made (anthropogenic) and natural (biogenic) emissions is also discussed.

A decomposition model to analyze effect of SO_2 emission density of China

A decomposition model was applied to study the resource-saving and environment-friendly effects of air pollutant emissions(taking industrial SO2 emission as an example) in China.From the results,it is found that 38.93% and 61.07% are contributed to environment-friendly and resource-saving effects,respectively,by the dramatic decrease in industrial SO2 emission density(nearly 70% from 2001 to 2010).This indicates that China has achieved important progress during the 11th FYP(five-year plan) compared with the 10th FYP.A simultaneous equations model was also employed to analyze the influencing factors by using data from 30 provinces in China.The results imply that the influence of environmental regulation on environment-friendly effect is not obvious during the 10th FYP but obvious during the 11th FYP.Thus,the government should continue promoting the environment-friendly effect by further enhancing environmental regulation and strengthening the role of environmental management.

An Analytical Formulation for Pollutant Dispersion Simulation in the Atmospheric Boundary Layer

In this work we present the solution of the two-dimensional advection-diffusion equation by the GILTT method. The GILTT approach uses, in the series expansion, eigenfunctions given in terms of cosine functions. Here, a different expansion for the solution of the advection-diffusion equation will be explored. In other words, a Sturm-Liouville problem carrying more information of the original problem is considered, given by Bessel functions. Numerical simulations and comparisons with experimental data are presented.

PyLUR:Efficient software for land use regression modeling the spatial distribution of air pollutants using GDAL/OGR library in Python

Land use regression(LUR)models have been widely used in air pollution modeling.This regressionbased approach estimates the ambient pollutant concentrations at un-sampled points of interest by considering the relationship between ambient concentrations and several predictor variables selected from the surrounding environment.Although conceptually quite simple,its successful implementation requires detailed knowledge of the area,expertise in GIS,statistics,and programming skills,which makes this modeling approach relatively inaccessible to novice users.In this contribution,we present a LUR modeling and pollution-mapping software named PyLUR.It uses GDAL/OGR libraries based on the Python platform and can build a LUR model and generate pollutant concentration maps efficiently.This self-developed software comprises four modules:a potential predictor variable generation module,a regression modeling module,a model validation module,and a prediction and mapping module.The performance of the newly developed PyLUR is compared to an existing LUR modeling software called RLUR(with similar functions implemented on R language platform)in terms of model accuracy,processing efficiency and software stability.The results show that PyLUR out-performs RLUR for modeling in the Bradford and Auckland case studies examined.Furthermore,PyLUR is much more efficient in data processing and it has a capability to handle detailed GIS input data.