Building a decision support system based on fuzzy logic to reduce the total harmful emissions from a fossil fuel boiler or power plant

This article discusses the software implementation using the theory of fuzzy sets. To build the model, we use data on wind direction, wind strength, the distance of residential buildings and the population of the expected area of pollution distribution. To do this, the most critical parameters must be ranked such as size of population. wind speed and wind direction. We will consider them as input data. We use Scilab as the processing environment.

CFD Simulation of Air Pollution Distribution and Its Effect on Patients in Hospital Operating Rooms

This paper uses the CFD technique to simulate the internal environment of a hospital operating room, including the surgical area that includes the patientand the surgical team. Moreover, this work provides mathematical simulation of many models to be placed in the air entry and air exit areas in the operating room to obtainthe optimal model of air distribution within the operating room. The results obtained matched well with experimental data from approved literature and standards. These results found that airflow and air velocity significantly affect the patient’s thermal comfort in the operating room.Besides, it is observed that some recirculation zone is formed, and this is a result of the heat generated by the surgical team and the accelerated air. This region is considered a source for the survival of the polluted air in the room. Keywords: CFD, Operating room, Numerical analysis.

Pollution Distribution of Potentially Toxic Elements in a Karstic River Affected by Manganese Mining in Changyang, Western Hubei, Central China

This study investigated the distribution, pollution level and potential ecological risk of potentially toxic elements (PTEs) from manganese mining in a karstic Danshui River, in Changyang, Western Hubei, Central China. River water and sediments were collected for seven PTEs measurement (As, Cd, Cr, Cu, Mn, Pb and Zn), as well as pH and Eh of the river water were measured. Results showed that the major pollutant was Mn, the river water environment was mainly acidic and oxidizing (288 < Eh, pH < 6.3), and the pollution distribution of Mn in the study area was dominated by the combination of natural processes and anthropogenic activities. In the river water, according to the contamination factor (CF) and pollution load index (IPL) results, Mn was considered the main pollutant. There was low As and Pb pollution downstream as well as Cu pollution upstream. Upstream and downstream areas were the main polluted river sections of the river water samples collected. In river sediments, based on the results of the geo-accumulation index (Igeo) and potential ecological risk index (IPER), it was determined that there was only considerable Mn pollution. The IPER of the PTEs from the river sediments was at acceptable levels, only Mn upstream performed at a moderate ecological risk level. According to Pearson correlation and principal component analysis, Mn originated from manganese mining activities, Cd, Cr and Zn were of natural origin, and Cu may have come from both mining and natural origin, whereas Pb and As were mainly related to the daily activities. Consequently, elemental speciation, mining activities and the distribution of water conservancy facilities were the main impacts of PET pollution distribution in this river.