Study On The Emission Characteristics And Modeling Of Building Construction Dust

With the development of China’s construction industry,building construction dust has become an important part of air pollution,which not only adversely affects construction workers,but also the surrounding residents and the air environment.On-site monitoring of building construction dust to obtain relevant data,so as to grasp the emission characteristics of building construction dust,can be targeted to prevent and control building construction dust.However,it is time-consuming and labor-intensive to carry out standardized on-site monitoring of building construction dust,and it is troublesome to obtain data.Therefore,this study carried out in-depth research on the emission characteristics and modeling of building construction dust,with a view to providing a scientific basis for on-site managers to carry out dust prevention and control and government departments to formulate building construction dust emission standards.In this study,a set of building construction dust monitoring programs was developed based on relevant national standards.TSP,PM₁₀,and PM_2.5 were selected as the dust monitoring indicators,and the up-down wind direction method was adopted to set up eh monitoring points,and incremental the building construction dust concentration was used to quantify the building construction dust emission.At the same time,information such as meteorological information,construction intensity,and dust prevention measures were obtained on site.After that,this study conducted on-site monitoring on 7 typical construction sites,and used data analysis to explore the emission characteristics of building construction dust,and gave suggestions on dust prevention measures based on the analysis results.Finally,combined with the above research and literature research results,the input indicators and output indicators were selected,and the building construction dust emission model was established based on BP neural network modeling technology.The model was used to conduct a case analysis for an actual construction project.The following conclusions and results were obtained in this study:1)In the selected monitoring samples,there is a large gap between the compliance rates of different monitoring indicators.The compliance rates of TSP,PM₁₀ and PM_2.5 were respectively 90%,68.33%and58.33%.2)During the monitoring period,the average daily incremental concentration of TSP,PM₁₀,and PM_2.5 was 70.63μg/m3,16.42μg/m3,and 8.37μg/m3,respectively.3)In building construction dust,the ratio of particle concentration of different particle sizes are TSP:PM₁₀:PM_2.5=1:0.239:0.116.4)Construction vehicles is one of the important factors that affect the building construction dust.5)Combined the results of this paper and literature research,three categories of meteorological factors,construction intensity and dust prevention measures were selected as input indicators,and TSP incremental concentration,PM₁₀ incremental concentration and PM_2.5 incremental concentration were used as output indicators.The model of building construction dust emission based on BP neural network was established.6)Taking the construction project of a people’s court in Guangzhou as an example,the prediction accuracy of TSP,PM₁₀and PM_2.5 incremental concentration were verified to be 1.32%,3.22%and 7.11%,respectively,indicating that the building construction dust emission model has a good prediction effect.