Detection And Control Of Indoor Air Inhalable Particles

Inhalable particles are contaminants that exist in the inhaled air of human body and cannot be visually recognized,which pose a great threat to the health of people who have been living indoors for a long time.At present,the study of indoor air quality is limited to the monitoring of CO,TVOC,formaldehyde and other representative pollutants,ignoring parameters such as indoor inhalable particles.Therefore,the acquisition of indoor inhalable particles and study of its quality evaluation can get the real time status of these parameters,and also can reliably evaluate the quality of indoor inhalable particles,realizing pollution warning.Indoor inhalable particles quality is an indicator of strong fuzziness.There are many mutually coupled factors that are related to indoor inhalable particles concentration.Among them,temperature and relative humidity have highest relation degree.Therefore,four different parameters including PM2.5,PM10,indoor temperature and relative humidity were selected in this paper,and these data were monitored and used as the evaluation index to build indoor inhalable particles evaluation model.Considering the uneven distribution of indoor inhalable particles,the results of single point monitoring have a certain inaccuracy.Therefore,this paper took multi-point data acquisition of the same parameters,using comprehensive processing results instead of single point detection,so that enhanced system reliability.This paper described the inhalable particles detection system setup by PM2.5 and PM10 particle sensor and temperature and humidity sensor,Zig Bee wireless communication networking and data transmission module and upper computer,and also introduced the principle of light scattering and the detection progress of the inhalable particle sensor in detail.The system realized data acquisition,transmission,storage and analysis.On the basis of this,a normalized weighted average algorithm and fuzzy neural network algorithm were introduced to evaluate the quality of indoor inhalable particles.First,the monitoring results of PM2.5,PM10,temperature and relative humidity were normalized and weighted averaged,forming a set of evaluation factors.According to the relevant national standard,each parameter was divided into for grades: excellent,good,medium and poor.After the training of the experimental data,the evaluation model of indoor inhalable particles matter has been successfully established.The evaluation model was used to give a scientific evaluation of quality of indoor inhalable particles.The innovations of this paper are as follows: Multi-point monitoring of parameters was proposed and realized,which solved the problem of uncertainty of single point monitoring.This paper combined fuzzy algorithm with neural network algorithm,which was applied to indoor air inhalable particles quality assessment then,and established evaluation model.Compared to the traditional objective evaluation or subjective evaluation,this method is more accurate.The system is stable and reliable,and realized the indoor inhalable particles monitoring and quality evaluation.The system can be applied to the monitoring and control of inhalable particles in other indoor places.All that is need is to change the number of monitoring nodes or choose the corresponding monitoring point according to the environmental characteristics of the monitoring sites.