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Receptor Modelling of particulate matter at residential area near industrial region in Indonesia using Positive Matrix Factorization
a Department of Environmental Engineering, Bandung Institute of Technology (ITB), Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 The Authors, published by EDP Sciences.Positive Matrix Factorization (PMF) was used to identify the sources of ambient TSP and to estimate respective contribution to the total ambient TSP concentration in the residential area surrounding iron and steel industry in Cilegon city. Total of 34 daily samples (24 hours) were collected during the sampling period (August-November 2015) using a High Volume Sampler. The samples then were analyzed for black carbon and 18 metal elements (Si, Al, Fe, S, Cu, Pb, V, Cr, Ni, Zn, Mn, Sn, K, Ca, Cl, Ti, Ba, and Co) using Diffusion Systems EEL 43m Smoke Stain Reflectometer (SSR) and Energy Dispersive X-Ray Fluorescence (ED-XRF), respectively. From the PMF results were found that 10 factors as the optimum solution. The five major sources are crustal matter (40.13%), iron and steel production (22.23%), coal combustion (16.54%), biomass burning (11.83%), smelting (8.63%). Meanwhile, the other sources detected are diesel vehicle (0.28%), sea salt (0.17%), fuel-oil combustion (0.07%), road dust (0.07%), and cement industries/construction (0.05%). The patterns of conditional probability function analysis results were adequately appropriate with the potential locations of the known sources around study site.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Conditional probability function,Energy dispersive X-ray fluorescence,Fuel oil combustion,High-volume sampler,Iron and steel productions,Particulate Matter,Positive Matrix Factorization,Receptor modelling[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1051/e3sconf/202014803003[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]