University of Ghana http://ugspace.ug.edu.gh CHARACTERIZATION OF ATMOSPHERIC PARTICULATE MATTER AT E-WASTE LANDFILL SITE IN AGBOGBLOSHIE, ACCRA A dissertation presented to the: Department of NUCLEAR SCIENCES AND APPLICATIONS SCHOOL OF NUCLEAR AND ALLIED SCIENCES COLLEGE OF BASIC AND APPLIED SCIENCES UNIVERSITY OF GHANA by Hyacinthe Ahiamadjie (ID: 10235632) BSc (Cape Coast), 2005 MPhil (Legon), 2008 In partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in APPLIED NUCLEAR PHYSICS 2017 University of Ghana http://ugspace.ug.edu.gh Declaration This thesis is the result of research work undertaken by Hyacinthe Ahia- madjie in the Department of Nuclear Sciences and Applications, School of Nuclear and Allied Sciences, University of Ghana, under the supervision of Rev. Dr Samuel Akoto-Bamford (SNAS, UG-Legon, Ghana) and Prof. Innocent Joy Kwame Aboh (NRA, Accra-Ghana). Sign: ........................................... Hyacinthe Ahiamadjie (Student) Sign: ................................................ Sign: ................................................ Rev. Dr Samuel Akoto-Bamford Prof. Innocent Joy Kwame Aboh (Supervisor) (Co-Supervisor) ii University of Ghana http://ugspace.ug.edu.gh This thesis is dedicated to the Almighty God and to the blessed memory of my mother Mercy Aku Dedoo . . . iii University of Ghana http://ugspace.ug.edu.gh Acknowledgements First and foremost, I am most grateful to the Almighty God for bringing me this far by ordering my footsteps and for His abundant grace through these years. May His name be glorified forever. In a special way, I wish to express my sincere gratitude to all my supervisors, Rev. Dr. S.A. Bamford and Prof. I.J.K. Aboh for their support, guidance, encouragement and for believing in me. My sincere thanks go to Dr. Christian K. Nuviadenu for his help during the analysis of the samples, teaching of latex and Proof reading of my thesis. I find it appropriate to acknowledge you, Dr C.K. Nuviadenu really made my life easier. May the Lord bless you. Special thanks to my colleagues and friends and Prof. G.K. Banini ( our Manager) at the Accelerator Research Center (ARC) of GAEC for their encourage- ments and support. I wish to single out Dr. Joseph B. Tandoh, Dr. Oscar K. Adukpo, Dr. Amos Forson, and Dr. Owiredu Gyampo for their assis- tance throughout the writing period. Finally I wish to express my heartfelt gratitude to my dear wife Evelyn and our children Fafali and Kekeli for lending their support and care throughout this study. iv University of Ghana http://ugspace.ug.edu.gh Abbreviations ADC Analogue-to-Digital Converter APM Atmospheric Particulate Matter AQG Air Quality Guidelines ARC Accelerator Research Centre CA Cluster Analysis CBD Central Business District CFC Charge-Frequency-Converter DF Deposited Fraction EF Enrichment Factor EIA Environment Impact Assessment EMP Environmental Management Plan EDXRF Energy Dispersive X-ray Fluorescence EDPXRF Energy Dispersive Polarised X-ray Fluorescence EU-Dir European Union Directorate EPA Environmental Protection Agency e-waste Electronic and Electrical Waste GASDA Greater Accra Scrap Dealers Association Gmet Ghana Meteorological Service IBA Ion Beam Analysis IF Inhalable Fraction IAEA International Atomic Energy Agency LI Legislation Instrument v University of Ghana http://ugspace.ug.edu.gh NRA Nuclear Regulatory Authority NILU Norwegian Institute for Air Research INFN National Institute of Nuclear Physics MCA Multichannel Analyser NSAP Nuclear Science and Applications SDD Silicon Drift Detectors PCA Principal Component Analysis PIGE Particle Induced Gamma-ray Emission PIXE Particle Induced X-ray Emission PM Particulate Matter PMF Positive Matrix Factorization SFU Stack Filter Unit SRM Standard Reference Material SNAS School Of Nuclear and Allied Sciences UG University of Ghana UG University of Gent UK United Kingdom UNEP United Nations Environment Programme USAID United States Agency for International Development USEPA United States Environmental Protection Agency WHO World Health Organization XRF X-Ray Fluorescence vi University of Ghana http://ugspace.ug.edu.gh Table of Contents Declaration ii Acknowledgements iv Abbreviations v List of Tables xii List of Figures xiii Physical Constants xvi Symbols xvii Abstract 1 1 Chapter 1: General Introduction 3 1.1 What is the current situation of air pollution in Ghana? . . 6 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Current situation at Agbogloshie . . . . . . . . . . . . . . . 8 1.4 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.5 Scope of the project . . . . . . . . . . . . . . . . . . . . . . . 12 vii University of Ghana http://ugspace.ug.edu.gh 1.6 Challenges of the project . . . . . . . . . . . . . . . . . . . . 13 1.7 Overview of the thesis . . . . . . . . . . . . . . . . . . . . . 14 2 Chapter 2: Literature Review 15 2.1 Work done on APM in Africa . . . . . . . . . . . . . . . . . 16 2.2 Characteristics of particulate matter . . . . . . . . . . . . . 17 2.2.1 Size distribution . . . . . . . . . . . . . . . . . . . . . 17 2.2.2 Different modes of formation . . . . . . . . . . . . . . 19 2.2.3 Chemical composition of APM . . . . . . . . . . . . . 21 2.2.4 Transport of atmospheric particulate matter . . . . . 22 2.2.5 Residence time . . . . . . . . . . . . . . . . . . . . . 22 2.2.6 Particle removal . . . . . . . . . . . . . . . . . . . . . 23 2.3 Particle size distributions . . . . . . . . . . . . . . . . . . . . 25 2.4 Mechanisms of Particle Deposition in Lung . . . . . . . . . . 26 2.4.1 Inertial impaction . . . . . . . . . . . . . . . . . . . . 27 2.4.2 Gravitational settling . . . . . . . . . . . . . . . . . . 28 2.4.3 Diffusion . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.4 Interception . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.5 Electrostatic attraction . . . . . . . . . . . . . . . . . 30 2.5 APM Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1 Types of samplers . . . . . . . . . . . . . . . . . . . . 32 2.5.2 The GENT sampler . . . . . . . . . . . . . . . . . . . 32 2.5.3 Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.4 Fibrous membrane filters . . . . . . . . . . . . . . . . 34 2.5.5 Porous membrane filters . . . . . . . . . . . . . . . . 35 2.6 Particulate matter measurement tools . . . . . . . . . . . . . 36 2.6.1 Proton induced x-ray emission, PIXE . . . . . . . . . 37 2.6.2 Quantification analysis using PIXE . . . . . . . . . . 39 2.7 APM analysis using PIGE technique . . . . . . . . . . . . . 41 viii University of Ghana http://ugspace.ug.edu.gh 2.7.1 Concentration calculation using PIGE . . . . . . . . 41 2.7.2 Quantification of APM samples using PIGE . . . . . 42 2.8 X-Ray Fluorescence Analysis . . . . . . . . . . . . . . . . . . 43 2.8.1 X-Ray Emission . . . . . . . . . . . . . . . . . . . . . 44 2.8.2 Continuum X-rays . . . . . . . . . . . . . . . . . . . 45 2.8.3 Characteristic Emission . . . . . . . . . . . . . . . . 45 2.8.4 X-Ray Interactions with Matter . . . . . . . . . . . . 46 2.8.5 Energy dispersive x-ray fluorescence ( EDXRF ) spec- trometry . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.8.6 Basic configuration of XRF . . . . . . . . . . . . . . 50 2.8.7 XRF quantitative analysis . . . . . . . . . . . . . . . 50 2.8.8 Fundamental parameters approach . . . . . . . . . . 51 2.8.9 Elemental sensitivity approach . . . . . . . . . . . . . 51 2.8.10 Direct comparison of count rates . . . . . . . . . . . 52 2.9 Source apportionment of APM . . . . . . . . . . . . . . . . . 52 2.9.1 Receptor modelling methods . . . . . . . . . . . . . . 53 2.9.2 Positive matrix factorization . . . . . . . . . . . . . . 54 2.9.3 Enrichment Factor ( EF ) . . . . . . . . . . . . . . . 58 3 Chapter 3: Methodology 59 3.1 Aerosol characterisation at Agbogbloshie . . . . . . . . . . . 59 3.1.1 Selection of sampling area . . . . . . . . . . . . . . . 61 3.1.2 Sampling and gravimetric procedures . . . . . . . . . 61 3.1.3 Analysis of APM sample . . . . . . . . . . . . . . . . 63 3.1.4 Experimental setup for ion beam analysis . . . . . . 64 3.1.5 Sample preparation in PIXE and PIGE analytical techniques . . . . . . . . . . . . . . . . . . . . . . . . 66 3.1.6 Sample analysis using PIGE and PIXE techniques . . 67 3.2 PIXE elemental quantification . . . . . . . . . . . . . . . . . 68 ix University of Ghana http://ugspace.ug.edu.gh 3.2.1 Spectrum integration . . . . . . . . . . . . . . . . . . 68 3.2.2 Sensitivity calculation of elements . . . . . . . . . . . 69 3.2.3 Calculation of concentration of elements . . . . . . . 69 3.3 Determination of Na and Al using PIGE technique . . . . . 70 3.4 Analysis of Pb using EDPXRF . . . . . . . . . . . . . . . . 71 3.5 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.5.1 Enrichment Factor ( EF ) . . . . . . . . . . . . . . . 74 3.5.2 Source identification and apportionment of APM sam- ples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 3.6 Quality control measures taken . . . . . . . . . . . . . . . . 75 3.6.1 Sampling area . . . . . . . . . . . . . . . . . . . . . . 76 3.6.2 QA/QC as applied to APM data . . . . . . . . . . . 76 4 Chapter 4: Results and Discussions 77 4.1 Mass Concentration of Particulate Matter . . . . . . . . . . 77 4.2 Influence of Meteorological Parameters on Particulate matter 90 4.3 APM elemental determination . . . . . . . . . . . . . . . . . 92 4.3.1 Validation process . . . . . . . . . . . . . . . . . . . 92 4.3.2 Acquisition of the Spectrum . . . . . . . . . . . . . . 94 4.3.3 Elemental Concentration (ngm−3) of APM in both Fractions . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.4 Enrichment Factor (E.F) . . . . . . . . . . . . . . . . . . . . 99 4.5 Source Apportionment of APM . . . . . . . . . . . . . . . . 101 4.5.1 Coarse fraction . . . . . . . . . . . . . . . . . . . . . 104 4.5.2 Fine fraction . . . . . . . . . . . . . . . . . . . . . . 110 5 Chatper 5: Conclusions and Recommendations 117 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . 120 x University of Ghana http://ugspace.ug.edu.gh References 122 Appendix 139 A Elemental Concentarion in Coarse Fraction . . . . . . . . . . 139 xi University of Ghana http://ugspace.ug.edu.gh List of Tables 4.1 Summary of daily Mass Concentration gcm−3 of APM in Agbogbloshie during the entire sampling period . . . . . . . 78 4.2 Correlation Between Particulate Matter Concentrations and Meteorological Parameters . . . . . . . . . . . . . . . . . . . 90 4.3 Validation of quantification method (SRM 2783) ngm−3 . . 92 4.4 Summary of Elemental Concentration (ngm−3) in PM10−2.5 . 95 4.5 Summary of Elemental Concentration (ngm−3) in PM2.5 . . 97 4.6 Summary of Elemental Concentration (ngm−3) in PM2.5 . . 101 1 Summary of daily Mass Concentration gcm−3 of APM in Agbogbloshie during the entire sampling period . . . . . . . 140 2 Summary of daily Mass Concentration gcm−3 of APM in Agbogbloshie during the entire sampling period . . . . . . . 150 3 Summary of daily Mass Concentration gcm−3 of APM in Agbogbloshie during the entire sampling period . . . . . . . 160 xii University of Ghana http://ugspace.ug.edu.gh List of Figures 1.1 Location Map of Agbogbloshie . . . . . . . . . . . . . . . . . 10 2.1 Types, and size distribution of APM . . . . . . . . . . . . . 21 2.2 : Respiratory System and particle deposition . . . . . . . . . 26 2.3 : Gent Sampler Unit . . . . . . . . . . . . . . . . . . . . . . 33 2.4 : Stack filter Unit . . . . . . . . . . . . . . . . . . . . . . . . 34 2.5 : Electromagnetic Spectrum . . . . . . . . . . . . . . . . . . 43 2.6 : Bremsstrahlung . . . . . . . . . . . . . . . . . . . . . . . . 45 2.7 : the transmitted intensity of the photon . . . . . . . . . . . 47 3.1 Designed protocol for the investigation . . . . . . . . . . . . 60 3.2 LABEC laboratory of INFN in Florence, Italy . . . . . . . . 64 3.3 A filter being mounted on a holder-cup (left); and samples loaded in the spectrometer (right) . . . . . . . . . . . . . . . 71 3.4 Schematic diagram of Epsilon 5 Spectrometer in 3 D . . . . 72 4.1 Time Series Plot of Particulate Mass Concentrations (µg/cm3) for the Entire Sampling Period . . . . . . . . . . . . . . . . . 86 4.2 Average Monthly Particulate Mass Concentrations (µg/cm3) for the Entire Sampling Period . . . . . . . . . . . . . . . . 87 4.3 Percentage Ratio of PM2.5/PM10 for the Entire Sampling Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.4 Average Monthly Particulate Mass Concentrations . . . . . . 88 xiii University of Ghana http://ugspace.ug.edu.gh 4.5 Annual Average Air quality standards/guideline for coun- tries and organizations . . . . . . . . . . . . . . . . . . . . . 89 4.6 Spectrum of a Nuclepore Filter Measured with PIXE . . . . 94 4.7 Enrichment Factors . . . . . . . . . . . . . . . . . . . . . . . 100 4.8 Typical Output of the PMF . . . . . . . . . . . . . . . . . . 104 4.9 Sea spray source profile in coarse fraction . . . . . . . . . . 105 4.10 Time Series of PM10−2.5 Source Contributions for sea spray 105 4.11 Soil dust source profile in coarse fraction . . . . . . . . . . . 106 4.12 Time Series of PM10−2.5 Source Contributions for soil dust . 106 4.13 Heavy oil burning source profile in coarse fraction . . . . . . 107 4.14 Time Series of PM10−2.5 Source Contributions for heavy oil burning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 4.15 Biomass burning source profile in coarse fraction . . . . . . 107 4.16 Time Series of PM10−2.5 Source Contributions for biomass burning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.17 E-waste burning source profile in coarse fraction . . . . . . 108 4.18 Time Series of PM10−2.5 Source Contributions for e-waste burning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4.19 Elemental Source Profiles of PM10−2.5 . . . . . . . . . . . . 109 4.20 Source apportionment in coarse fraction . . . . . . . . . . . 110 4.21 E-waste burning source profile in fine fraction . . . . . . . . 111 4.22 Time Series of PM2.5 Source Contributions for e-waste burn- ing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.23 Industrial activities source profile in fine fraction . . . . . . 112 4.24 Time Series of PM2.5 Source Contributions for industrial ac- tivities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 4.25 Resuspended dust source profile in fine fraction . . . . . . . 113 xiv University of Ghana http://ugspace.ug.edu.gh 4.26 Time Series of PM2.5 Source Contributions for resuspended dust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 4.27 Sea spray source profile in fine fraction . . . . . . . . . . . . 114 4.28 Time Series of PM2.5 Source Contributions for Sea spray . . 114 4.29 Vehicular traffic source profile in fine fraction . . . . . . . . 115 4.30 Time Series of PM2.5 Source Contributions for vehicular traf- fic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 4.31 Elemental Source Profiles of PM2.5 . . . . . . . . . . . . . . 115 4.32 Source apportionment in fine fraction . . . . . . . . . . . . . 116 xv University of Ghana http://ugspace.ug.edu.gh Physical Constants Constant Name Symbol = Constant Value (with units) Acceleration due to gravity g = 9.8m/s2 Universal Gas constant R = 8.314kJ/K/mol pi π = 8.314 xvi University of Ghana http://ugspace.ug.edu.gh Symbols Symbol Name σ Standard deviation θ Potential Temperature  virtual source matrix β Entrainment parameter ρ Density kg/m3 xvii University of Ghana http://ugspace.ug.edu.gh Abstract A year-long investigation has been carried out on atmospheric particulate matter (APM). Elemental composition,mass concentration, sources contri- butions and their fingerprints were determined in APM samples collected, in Agbogbloshie scrap market, between May 2010 and April 2011. PM2.5 and PM102.5 size fractions of APM were collected 24-hourly on 47 mm diam- eter nuclepore filters of pore sizes 0.4 µm and 8 µm respectively, using the Gent sampler. Gravimetric analyses were carried out to determine the APM mass concentrations. The minimum, maximum and annual mean mass con- centration values for PM2.5 obtained for the sampling site throughout the period of investigation were 35.43 (µg/cm3), 349.68 (µg/cm3) and 88.62 (µg/cm3) respectively. For PM10−2.5, the minimum, maximum and an- nual mean mass concentration values obtained were 76.83(µg/cm3), 448.67 (µg/cm3) and 138.31 (µg/cm3) respectively. These measured concentra- tion levels are all substantially higher than the WHO, USEPA, EU, JAPAN EQS, UK National Air quality objective, World Bank and Canada air qual- ity standards. Particle Induced X-Ray Emmission (PIXE), Particle Induced Gamma Emmission (PIGE) and Energy Dispersive X-Ray Fluorescence (EDXRF) were used to determine elemental composition and concentration of the atmospheric particulate matter. The five most abundant elements in coarse fraction PM102.5 are Si average 6545.05 (range: 261.3359950.56) ngm−3, Al average 3743.25 (range: 218.9424719.25) ngm−3, Fe average 1 University of Ghana http://ugspace.ug.edu.gh Abstract 2 2464.77 (range: 182.0818982.45) ngm−3 , Cl average 5918.47 (range: 587.76 15796.13) ngm−3, Na average 4040.17, (range: 418.2515010.32) ngm−3. In the fine fraction (PM2.5) concentrations reordered were in the follow- ing order: Si average 5466.44 (range: 1339.9361029.16) ngm−3 , Al aver- age 3073.44 (range: 888.7425389.05) ngm−3, Fe average 1877.57 ( range : 769.2819569.65) ngm−3, Cl average 4972.86 (range: 1533.3616741.73) ngm−3, Na average 3163.27 (range: 1295.1515887.22) ngm−3. Enrichment Factor evaluation was first used as one of the means to separate elements in terms of natural and anthropogenic sources. PMF was employed in fin- gerprint and source contributions identifications, PMF resolved five sources each for both PM2.5 and PM10−25. The following sources were identified for the PM2.5:E-waste burning (49%), Industrial activities (23%), vehic- ular traffic (16%), resuspended dust (7%) and sea spray (5%). In the coarse fraction, soil dust (45%),biomass burning (17%), Heavy oil burn- ing (16%),sea spray (12%) and e-waste burning (10%) were apportioned. Natural sources (45%) were the major contributors in the coarse fraction while anthropogenic sources(88%) were the major contributors in the ne fraction. E-waste burning contributions were identied in both fractions. In this work, the following elements (Zn, As, Br, Sn, Cd, Hg, Pb) have been identified as ngerprint for e-waste burning and this source contributed 49% of the pollutants identied in the study area. University of Ghana http://ugspace.ug.edu.gh Chapter 1 Chapter 1: General Introduction The advances in information technology and demand for electronic and electrical gadgets have increased astronomical, and however the disposal of used and obsolete equipment is becoming global issues. Since in the third world countries, disposal of e-waste has become a huge challenge, people turn to openly burn the e-waste, since we do not have any proper regula- tion on how to dispose of e-waste. However anytime e-waste is burned, it emits toxic fumes into the air. Air pollution occurs when solid, liquid and gaseous materials have been emitted into air environment. The emission can originate from stationary or mobile sources and can sometimes involve chemical or/and physical transformations before being returned to surfaces such as the soil, plants, trees, monuments, buildings etc...(Bandyopadhyay, 2010). 3 University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 4 Particles exist in the environment as the suspensions in air and are com- monly called aerosol. Solid particles and liquid droplets are combined in the atmosphere to form an aerosol. The aerosol particles have a very wide size range from molecular clusters of 0.001 µm to fog and dust particles as large as few hundred micrometers. Particulate Matters are always grouped into two main particulate matter size fractions: coarse PM10−2.5 and fine PM2.5 (PM <10 and 2.5 µm in aerodynamic diameter, respectively). The depth of PM deposition in the humans respiratory system is determined by size of the particles. Also the impact of the PM on humans depend on the size. The smaller the PM the more dangerous is to humans. The origin of the particles could be from natural process (e.g sea spray, dust etc....) or from man made processes also known as anthropogenic process (vehi- cle emission, emission from industry, e-waste burning, waste incineration etc....). Aerosol particles in the atmosphere are caused by wide range of sources and have diverse compositions and characteristics. As a result,the effects of aerosol particles on the environment and humans are many and varied. Some of the effect could just be a nuisance like dust deposited on clean surface to serious climatic effect like environmental degradation, acid rains, impact on climate, impact on humans and animal health. The fine particles are more hazardous, it is due to the size and morphology. Thus PM2.5: University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 5 • can easily traveled over long distances far away from the pollution sources • can easily be inhaled and deposited in the lower part of the human respiratory tract • have a larger surface area per unit mass and hence higher ability to absorb gas molecules and transport them to any part of the respira- tory system to catalyze chemical and biochemical reactions • consist of large soluble fraction The coarse fraction includes particles greater than 2.5 µm which are usually released directly into the atmosphere by mechanical processes or from natu- ral sources. PM10−2.5 can not be transported beyond our upper part of the human respiratory tract (the nose and throat), it is impossible for PM10−2.5 to reach the lungs (Hinds, 2012) (Smolders et al., 2008). Particle size is one of the most important parameter in characterizing the physical behaviour of particulate matter. The size of a particle affects many of its properties such as volume, mass and settling velocity. Size also influences a parti- cles transport, deposition and migration through the environment. Also concentration of particulate matter dependent on these factors: weather patterns, wind, stability (vertical movement of air), turbulence, precipita- tion, topography, and smokestack height and temperature of gases toxic (Hinds, 2012) (Smolders et al., 2008). University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 6 1.1 What is the current situation of air pol- lution in Ghana? Since the establishment of the Environmental Protection Council in 1974, government of Ghana has been working very hard to achieve air quality management strategy for Ghana. Government of Ghana has made it a general requirement by law, for new businesses to provide an environment impact assessment (EIA) and for the existing ones to provide environmen- tal management plan (EMP). National environmental standards to serve as guideline for compliance have also been introduced. For some specific sources however additional legislation have been put in place to control their emission. Government of Ghana has approved a legislation instru- ment LI 1732 to ban the activities of leaded gasoline in the system since January 2004. The Environmental Protection Agency of Ghana has been doing a lot of collaborated research works with the United States Agen- cies in charge of environmental issues, In march 2005 and December 2008 results were found that emissions from vehicles exhaust, uncontrolled burn- ing of waste (domestic and industrial), industrial emission sources, domestic cooking, commercial activities and wind-blown dust are the major contrib- utors to the air quality. A lot of studies have been conducted by both researchers and students on issues pertaining to air in Ghana (Aboh et al., 2009) (Atiemo et al., 2012) (Ofosu et al., ) (Ofosu et al., 2012). Despite University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 7 various legislative mechanisms being in place and with all these researches, we are still not able to have regular and systematic air quality monitoring for air quality assessment because our information on the concentrations as well as the characteristics of atmospheric particulate matter is still not enough to do extensive work. Some works have been done on the PM10 particulate in some major cities in Ghana by the Environmental Protection Authority of Ghana (EPA-Ghana),but that of fine particulate there is no records because air quality standard for PM2.5 has not been established in Ghana. 1.2 Problem statement E-waste burning is one of the major processes in the exploitation of e-waste at the landfill sites. Consequently contribution of e-waste burning to air pollution has become one of the most serious environmental concerns in most cities throughout the world especially in developing countries (Diaz- Barriga, 2013) (Needhidasan et al., 2014) (Modak, 2010) (Samson, 2009). Ewaste particulate matter is on the rise in sub-Saharan Africa. Rise in the e-waste burning at Agbogbloshie has led to emission of air borne par- ticulate matter into the ambient environment. A lot of works have been done in other media of pollution such as soil, water etc.... Nevertheless no data on environmental pollution impact of e-waste burning on ambient air University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 8 quality. No comprehensive study on the nature of atmospheric particulate matter (APM), their concentration levels and sources of emission. In the absent of such data no effective management and mitigation procedures can be implemented. In order to assess and to lessen the impacts of e-waste particulate matter pollution, any program aimed at regulating the levels of particles in the urban atmosphere through generating knowledge on the size distribution, chemical composition and sources of aerosol particles. 1.3 Current situation at Agbogloshie The Agbogbloshie scrap yard, as shown in figure 1.1, is located on a flat ground along the side of the Odaw River, and of the Korle Lagoon in Ac- cra. Agbogbloshie has two major recycling sites. The main recycling site is close to the South Industrial area at bank of the Odaw River. The other recycling site is located north of the Korle lagoon. Agbogbloshie scrap yard is adjacent to the food market, a market where onion, tomato and yam are sold to the growing urban population of Accra. Proximate to the scrap yard are also food joints. Apart from these, there are also other in- formal activities proliferating near the scrap yard including women selling used clothes. There are also artisans who use metallic materials from the scrap to make coal pots cooking and grilling pots etc. These activities are University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 9 flourishing along the road connecting Agbogbloshie to the Central Busi- ness District. The growing tendencies of the informal activities nearby the scrap yard have led to overcrowding and traffic jams in Agbogbloshie. The Agbogbloshie scrap yard is also adjacent to some large formal industrial enterprises such as brewing, paint manufacturing, engineering etc. There are some banking institutions Merchant Bank, Eco bank and micro finance institutions operating in the area close to the scrap yard. The two major recycling sites are in close proximity to the slum ( popularly called Sodom and Gomorra) which serves as dwelling place for the e-waste workers and other traders in the area. The slum is known to be the largest and the most notorious slum in Ghana housing about 79,684 people. Agbogbloshie, like most (sub)-urban areas in Ghana has no structured ewaste disposal. Scrap dealers have traditionally relied on open burning as means of recycling or disposal of e-waste. Open burning of e-waste emits particulate matter into the air. Air pollution occurs when a chemical or substance enters the at- mosphere; it could be harmful to the health of humans and animals or cause damage to plants and materials. Since Agbogbloshie is closed to the urban cities, there is the need to assess the health impact from the e-waste site on the inhabitants in the city. For that matter, there is need to do detailed work to determine the percentage contribution of e-waste burning in Agbogbloshie. For the characterization or measurement of aerosols elemental and trace University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 10 Figure 1.1: Location Map of Agbogbloshie University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 11 composition, etc. numerous analytical techniques can be employed for the analysis, however, the chosen technique must have the following qualities: for its sensitivity, multi-elemental capability, non-destructiveness, and the accuracy of the information it provides over the rest. Ion Beam Analysis (IBA) can best be described to have all the stated qualities above. IBA is capable of providing a wide range of information on aerosol samples col- lected at Agbogbloshie; however, it requires statistical tools to link them to, and disentangle the pollution sources from which they emanate. Key among such tools is positive matrix factorization (PMF) method for pol- lution sources identification (speciation) and the apportionment of their contributions as one of the key subjects of this thesis. 1.4 Objectives The main objective of this work is to assess the effect of e-waste burning at the landfill sites at Agbogbloshie on the ambient air quality. Specifically this work seeks to: 1. Identify particle composition of APM and assess their compliance to air quality criteria. 2. Derive the source profile or finger print of pollutants associated with e-waste burning. University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 12 3. Carry out source apportionment in order to determine relative con- tribution of identified pollutants. The outcome of this work will be contribution to the already existing public debate on the air pollution issues,and it will go a long way in helping the agencies responsible for drafting pollution levels policies, protecting the public health, and managing environmental pollution. 1.5 Scope of the project In this work, the Gent Stack Filter Unit (SFU) sampler and nuclepore fil- ters were used for almost two years, that is from May 2010 to August 2011 continuously to sample for both fine PM2.5 and coarse PM10−2.5 fractions of particulate matter (PM) at Agbogbloshie. Daily PM concentration was done using micro-sensitive balance as mean of the determining gravimetric analysis. The techniques employed in the determination of the elemental concentration : Particle Induced X-ray Emission (PIXE), Particle Induced Gamma-ray Emission (PIGE), and Energy Dispersive Polarised X-ray Flu- orescence (EDPXRF). These techniques were used to help in the character- ization of aerosol in the Agbogbloshie e-waste burning sites. To determine factors and sources that contribute to the deterioration of the air qual- ity in Agblogloshie, Positive Matrices Factorization(PMF) and Enrichment University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 13 Factor (EF) are employed on both the mass and elemental concentration data. 1.6 Challenges of the project Levels of particles in the atmosphere are not solely dependent on the emis- sion rates of particles, no matter how well characterised, but meteorological factors played a very important role. Any change in parameters like wind speed, temperature, relative humidity, and precipitation, along with ter- rain and geographical factors, demands many measurements in space and time before levels can be adequately characterized for health and welfare. Knowledge on any changes occurring in the meteorology will enable us de- rive a better explanation of the source-to-concentration relationship. In this work, due to lack of needed instrument, meteorology data could not be obtained at the site. The data were then supplied by the meteorological sta- tion (the Ghana Meteorological Service (Gmet)) is in Accra. Erratic power supply and and equipment breakdowns did not allow continuous sampling over the entire duration. University of Ghana http://ugspace.ug.edu.gh Chapter 1. General Introduction 14 1.7 Overview of the thesis The thesis comprises five chapters. Chapter 1 provides an overall intro- duction and briefly describes particulate matter PM, its health problems, status of the air pollution in the country, the objectives of the study, scope of the project and the challenges face throughout the project.The second chapter provides background information on the urban nature of PM, sources, health and environmental effects, air quality monitoring and the various techniques available, meteorological parameters and relevant air-quality guidelines and standards. Chapter 3 describes the monitoring site, the criteria applied, the approach used and the sampling plan and presents in detail the sampling methodology adopted as well as the chosen monitoring methods. In Chapter 4, the results of PM10 and PM2.5 and their implications are explored and discussed in comparison with national and international annual guideline values. Chapter 5 presents the final con- clusions from the thesis together with recommendations for monitoring PM in urban areas and possible issues for further research. University of Ghana http://ugspace.ug.edu.gh Chapter 2 Chapter 2: Literature Review Particulate matter PM is one of the most dangerous forms of air pollution. United States Environmental Protection Agency USEPA, described PM as combination of solid particles and liquid droplets formed in the atmosphere (Hinds, 2012), variable in size and chemical constituents (Murray et al., 2010). These are particles that are small enough to remain air-borne for an extended time, for example hours or days and that can travel considerable distances from their sources. For sake of Research PM have been grouped into two main particulate matter size fractions: PM10 and fine PM2.5 ( PM <10 and 2.5µm in aerodynamic diameter, respectively) (Balakrishnaiah et al., 2011) (Ostro et al., 2011). The size of particles play a major role in both transportation and health effect in humans. Mostly fine particle (<2.5 µm ) and especially ultra fine (<0.1 µm ). They are able to travel to deeper areas of the lung. Both fine and ultra fine come as result of combustion 15 University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 16 sources,they have a stronger association with adverse health effects than coarse particles. (Hinds, 2012) (Smolders et al., 2008) ((Bezemer, 2009), (Nel, 2005), (Oberdörster et al., 1995), (Oberdörster and Utell, 2002), (Utell and Frampton, 2000), (Peters et al., 1997), (Samet et al., 2000). Coarse particles can not go further than the area of the nose and throat. According to this research work (Smolders et al., 2008) Fine particles are present in greater numbers and have greater surface area than larger parti- cles of the same mass, and they are generally considered to be more toxic. 2.1 Work done on APM in Africa The causes of air pollution in many urban cities in developing countries can be attributed to multiple sources. Air pollution can be transported over long distances and have impacts far from their point of emission. Most of the cities in the developing countries are now experiencing similar air pol- lution problems, especially anthropogenic particulate matter arising from the rapid growth of vehicle traffic, open waste burning, and the presence of major industries close to residential areas. In the areas of air pollution studies in Africa, some works have been done, however level of studies so far is still in its infancy. Only a few source apportionment studies of par- ticulate matter have been carried out in Africa countries. Chemical char- acteristics and source apportionment of particulate matter using receptor University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 17 modelling have been studied in South Africa (Engelbrecht et al., 2001), (Kgabi, 2010), Ghana and Gambia (Zhou et al., 2013), (Zhou et al., 2014), Nigeria (Oluyemi and Asubiojo, 2001) , Tanzania (Bennet et al., 2005), (Mkoma et al., 2010), Ethiopia (Etyemezian et al., 2005) and Benin(Fourn and Fayomi, 2006). Some studies were carried out in the characterization of APM and their sources in some parts (cities) of the country in the past years, their findings have shown the presence of different sources. These sources were emissions from exhaust vehicles, burning of waste and other material openly, industrial emissions sources, domestic cooking, commercial activities and wind-blown dust are the major contributors to the air quality measured at the monitoring sites.(Aboh, 2000), (Ofosu et al., 2012). The outcome of their findings revealed that there were high particulate matter concentrations in the most of the areas. In the wet season, APM concen- tration recorded were very low as compared to the concentration of APM recorded in the harmattan period. 2.2 Characteristics of particulate matter 2.2.1 Size distribution Size is the single most important determinant of the properties of parti- cles and it has implications on formation, physical and chemical properties, University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 18 transformation, transport, and removal of particles from the atmosphere (Vallius, 2005).The size of particle also plays the most important role in the understanding of the deposition in the human respiratory system. Work done by both Finlayson Pitts and Pitts in 1986 and Seinfeld and Pandis in 1998 described the various changes that occurred when a particle is emit- ted into the atmosphere (Seinfeld and Pandis, 2016), (Finlayson-Pitts and Pitts Jr, 1986). They have also shown that there were a change in the size and the chemical composition by processes like condensation of vapour species, evaporation, deposition (both wet and dry) and coagulation. The chemical compositions of these particles are determinants for the types of effects caused by particulate matter on humans, vegetation, and materials. Characterization of the physical behaviour of particulate matter depends greatly on the size of the Particle. Properties such as as volume, mass, set- tling velocity,transport, deposition and migration through the environment are affected by the size of a particle (Aboh, 2000), (Angle, 2014). Particles can be classified by their physical size; the size is from a few nanometres (nm) to tens of micrometres µm in diameter (Mkoma, 2008). Particle size is a very important factor when related to health and environmental effects (Grover, 2006). According to Finlayson-Pitts and Pitts particulate matter of the size range from 0.1 to 10 µm is noted to have serious health, climatic and environmental effects (Finlayson-Pitts and Pitts Jr, 1999). The most University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 19 commonly used definition of particle diameter is the aerodynamic diame- ter. Aerodynamic diameter (Dp) compares the terminal falling speed of the particle under consideration with a spherical particle that has a density of 1gcm3 (Finlayson-Pitts and Pitts Jr, 1999), (Hinds, 2012). √ ρp Dp = Dgk (2.1) ρo whereDg is the particle geometric diameter,ρp is the density of the particle,ρo is the reference density (1gcm3),and k is a shape factor,which is 1.0 in the case of a sphere (Finlayson-Pitts and Pitts Jr, 1999).In this work,particle diameter unless otherwise indicated,refers to the aerodynamic diameter. 2.2.2 Different modes of formation For instance, the life time of particulate matter is defined by the size of the aerosol particles. Particle size formations are mostly classified in three major groups, they are nuclei- (6 0.1µm), accumulation (0.1-2.5 µm), and coarse particle-mode (2.5-100 µm). When primary and secondary particles are found close to their emission sources that process can be described as the nuclei mode (also known as Atiken mode), examples of these primary pol- lutants are carbonaceous dusts and sulphur dioxide (SO2) (Foltescu, 1995), University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 20 (Hinds, 2012). They can be easily clear out of the atmosphere by rain. An- other characteristic of the nuclei particles is that they can move very fast into the accumulation mode due to gas to particle conversion and by coag- ulation with other aerosols. Due to the diversity of primary particles and the different chemical processes the secondary particles have an enormous mixture in characteristics and reactivity. Ozone (O3) and (H2SO4) are examples of secondary formed pollutants. Second stage of aerosol forma- tions is known as the accumulation mode, in this stage particles remained there for very long time, for about one to two weeks. Particle coagulation still occurs in the accumulation mode; however particles do not become big enough to end up in the coarse mode.The main removal mechanisms in the accumulation mode are rain-, and washout (Sillanpää et al., 2006). The particles in the coarse mode have different origin than the nuclei-, and accumulation-particles. Examples of coarse particles are coal-, cement-, and agriculture-dust and sea salt particles. These relatively large particles usually settle out within a few hours or days in the atmosphere depend- ing on their size, prevailing meteorological conditions, and altitude (Aboh, 2000). In literature particle size ranges are often classified as coarse ( <2.5 µm in diameter), fine ( <2.5 µm) and ultra fines (0.001-0.1 µm). Summary of aerosol origins and size distributions are shown in figure 2.1 University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 21 Figure 2.1: Types, and size distribution of APM 2.2.3 Chemical composition of APM According to report from Air Quality Expert Group (Grover, 2006), (Harri- son and Yin, 2000), (Kaur et al., 2005), each of the aerodynamic diameters have many different chemical substances, indicating that these particles originate from different sources.The location of the polluting source, chem- ical reactions in the atmosphere, time of the day, and meteorological con- ditions results in significant variations in the chemical composition of these particles (Finlayson-Pitts and Pitts Jr, 1999). University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 22 2.2.4 Transport of atmospheric particulate matter According to Zufall and Davidson, they described the process where aerosol particles are transported in the atmosphere between emission and removal process as the particle transport. the source -transport -sink sequence de- termines the residence (life)time of the particle in the atmosphere (Caffrey et al., 1998). 2.2.5 Residence time Nucleation mode particles in the atmosphere have very short life of the or- der of a few minutes to several hours; this is due to their rapid coagulation with larger particles or growth into larger sizes by condensation. They are removed through diffusion of falling rain drops. Ultrafine particles include the nucleation mode and much of the Aitken mode. Accumulation mode particles the largest surface area and therefore account for a significant frac- tion of the total particle mass. As the mode name suggests, the particle removal mechanisms are least efficient in this size range. Hence, they can be transported over thousands of kilometer and remain airborne for days to weeks. They also account for most of the visibility impairment in the dry atmosphere. Accumulation mode particles can be removed from the atmo- sphere by rainout or washout (Sillanpää et al., 2006). Coarse or sedimen- tation mode particles originate mainly from mechanical processes such as University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 23 grinding, wind or erosion. Particles in the coarse range are relatively large and usually consist of mostly anthropogenic and natural dust particles that normally settle out of the atmosphere relatively quickly by sedimentation and impaction on surfaces(Seinfeld and Pandis, 2016). Coarse or sedimen- tation mode particles residence time in the atmosphere is only a few hours or days depending on their size, prevailing meteorological conditions, and altitude (Aboh, 2000) 2.2.6 Particle removal Many factors, such as inflow conditions (wind speed, wind direction and tur- bulence), building geometry (height, width, roof type), surrounding build- ing layouts (street width and configuration), thermal stratification ( solar insulation and orientation, building and street thermal capacitance) and traffic conditions (traffic intensity, vehicle emissions, vehicle movement), have influenced the transport behaviours and mechanisms of PM10 and PM2.5 (Chang et al., 2009). However the motion of APM is governed by two forces, they are gravity and resistance of the gas due to particle mo- tion. The gravitation settling is very important for large or coarse particles. Dues to this rapid depletion by gravitational sedimentation, coarse parti- cles are mainly present in the atmosphere close to their sources. One of the most important parameters in the analysis of complex particle motion is known as The particle relaxation time (τ). It is proportional to the square University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 24 of the diameter and inversely proportional to the viscosity of the gaseous medium. It also characterizes the time required for particle to adjust or relax its velocity to new conditions in the atmosphere. It depends on the mass and mobility of the particle and is not affected by the nature and magnitude of the external forces acting on it. Hence the particles have long ( large ) relaxation time. The particle stopping distance Sx or inertial range is defined as the the distance traveled by particle relative to the gas medium before catches up with the flow. The particle stopping distance Sx is related to the relaxation time (τ), and the particle initial velocity relative to the gas. Sx = Vxoτ (2.2) where The particle stopping distance Sx, particle initial velocity relative to gas Vxo, the relaxation time (τ) On the displacement scale, the stopping distance represents a measure of a particles effective initial momentum, which is diminished to zero by air friction over a distance equal to the stopping distance. The relaxation time (τ) = mB, where m is the particle mass and B is the particle mobility and its initial momentum is given by University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 25 Sx = BmVxo (2.3) Stopping distance represents the distance a particle will travel in still air if an external force acting on the particle were turned off. 2.3 Particle size distributions Modeling respiratory deposition The fraction of particles that enters the nose or mouth is called the inhalable fraction (IF). Factors influencing in- halability are the size and density of the particle and the wind velocity and direction. Wind tunnels with mannequins that are connected to a breath- ing machine are used to experimentally determine the IF. Upon inhalation, particles can either deposit somewhere along the respiratory tract or they can be exhaled. The fraction of inhaled particulates that deposit in the res- piratory tract is called respiratory tract deposited fraction (DF). Human in vivo experiments are used to calculate the DF under different conditions; however regional deposition doses cannot be obtained using this technique. Moreover there are restrictions in the particles that can be used for human exposure. The site of deposition within the respiratory tract influences the effects of particles within the organism. Regional dosimetry is impor- tant for assessing the potential hazard of inhaled particles. Mathematical University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 26 Figure 2.2: : Respiratory System and particle deposition models have been developed to predict regional particle deposition. These models are based on (1) lung morphometry, (2) physical and chemical par- ticle properties, (3) gas and vapor properties of the air and (3) breathing patterns(Rostami, 2009). 2.4 Mechanisms of Particle Deposition in Lung Deposition mechanisms of particles in the lung are usual based on five pro- cesses, they are inertial impaction, gravitational settling, diffusion, inter- ception and electrostatic attraction, its modeling is made simply by using these five processes(Rostami, 2009), (Stuart, 1984). University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 27 2.4.1 Inertial impaction Inertial impaction occurs when airflow changes direction and the particle, which happens to be closed to the airway wall, follows its original direc- tion instead of adjusting to the airflow. The probability of deposition by impaction depends on the ratio of particle stopping distance to airway di- mensions at an airstreams velocity. The stopping distance is equal to the velocity before the change in current, times the relaxation time, which is the time required for a particle to adjust its velocity to a new condition. The higher the particle mass and its mobility, the more difficult it is for the particle to adjust for a curving air stream. Large particles have a higher relaxation time compared to small particles . The Stokes number (Stk) is used to predict impaction driven impaction. It combines all parameters that are important for inertia . When the size of a particle approaches the mean free path of air its properties change because of slip at its surface. This error becomes significant for particles less than 1 µm. The slip correc- tion factor (Cc) is inserted in the deposition equations to correct for this error. Uρ 2pdpCc Stk = (2.4) 9Djη where U: Flow velocity, ρp : particle density, Cc : slip correctionfactor, η : air viscosity, Dj: airway diameter. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 28 The Stk number shows that the probability of a particle to deposit due to impaction is directly proportional to particle density, diameter 2 and flow velocity whereas it is inversely proportional to the distance to the surface (the airway diameter). Thus: deposition by inertial impaction is most important for larger particles at high velocity close to the surface. Particles come close to the surfaces at high bend angles, which can be found in the head airways and the conducting airways (nasal passages, the larynx, the carnia and further bifurcations). 2.4.2 Gravitational settling Settling or sedimentation is driven by the influence of gravity, which makes particles to depart their original air stream. The probability of a particle to deposit due to gravitational settling depends on the ratio of particle set- tling distance to airway dimensions at an airstreams velocity. The particle settling distance is equal to its terminal settling velocity Vts times the res- idence time in each airway compartment. Vts is given by the gravitational acceleration times the relaxation time. gρpd 2Cc Vts = (2.5) 18η University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 29 where g: acceleration of gravity; ρp : particle density; Cc: slip correction factor; η air viscosity. Equation above shows that the probability of a par- ticle to deposit due to gravitational settling is directly proportional to its particle size 2 and density. In contrast to inertia it is inversely proportional to the air stream velocity because the residence time decreases at increas- ing velocity. This mechanism is therefore most important in the smaller airways and in the gas exchange region where the air velocities are low. Sedimentation has its maximum removal effect when airway surfaces ap- proaches horizontal configurations, which is the case in the alveolar region. 2.4.3 Diffusion Diffusion is a disperse mass transfer that is caused by random molecular motions also called Brownian motion. Small particles displace when they collide with air molecules. This mechanism can be explained by the dif- fusion coefficient (D) of an aerosol particle which is given in the Stokes- Einstein equation. KBTCc D = (2.6) 3dpπη University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 30 Where KB: Boltzmanns constant; T: Temperature; Cc :slip correction fac- tor; dp :particle density; η :viscosity. The probability of a particle to de- posit due to diffusion depends on the ratio of the root-mean-square of the displacement during residence (2 Dt ) to airway dimensions. Similar to gravitational settling, deposition due to diffusion is also increased where airway diameters are small and where residence time is long. 2.4.4 Interception Interception is the result of physical contact of a particle with the airway surface because of geometrical features. An assumption made for pure interception suggested that the particles follow the airstream and thus its inertia, settling and Brownian motion are negligible,. The particle does not depart its original air streamline but makes contact due to its physical size. Interception depends on the ratio of particle size to airway diameter which means that interception is most important for elongated particles, like fibers, which are long in one dimension but have small enough diameters to reach the small airways. 2.4.5 Electrostatic attraction Electrostatic attraction is important for electrically charged particles. These particles can deposit when large numbers of mutual charged particles drive University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 31 them towards the airway wall. In absence of mutual repulsion, a neutral surface by image forces is attracted by a charged particle. An image force is created by the particle itself and is equal but opposite of its own charge. Image forces are weaker than Coulomb forces and are only created when the particle is at close range to the surface. Freshly generated particles have higher surface reactivity than aged aerosols. These freshly generated particles may be more charged which results in higher deposition when compared to aged particles. 2.5 APM Sampling The choice of the APM sampling instrument depends on the substance to be measured, the properties it has, and the information to be gained from the measured values. The first aim of the measurements is to determine the levels of air pollutants but the mass flow rates are also very important to determine effect on the emission. However all APM samples collected are the representative samples for analysis. Both concentration and size distribution should be true representative of the airborne particles. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 32 2.5.1 Types of samplers There are different types of samplers in APM sampling methods. How- ever in this work, the GENT sampler, which is a low volume, filter-based sampler having a stacked filter unit for simultaneous, sequential collection of coarse and fine airborne particulates was employed. Gent sampler was used because it allowed for collection of particles on a filter that is weighed and subsequently subjected to analytical analysis such as PIXE and XRF easily (Wilson et al., 2002). 2.5.2 The GENT sampler The Gent Stacked Filter Unit (SFU) PM10 sampler owed its name to the designer and manufacturer, which is the the University of Ghent. The international Atomic Energy Agency (IAEA) has several coordinated re- search programmes in which the participants are to collect and analyze samples of the ambient aerosol. In order to have comparable samples taken in the variety of locations around the world, the IAEA contracted with the University of Gent to design and construct a sampler that would generally follow the requirements of a PM10 sampler. The air enters the unit through an impactor stage designed to have a 50% collection efficiency at 10 µm equivalent aerodynamic diameter. It then is drawn through a stacked- filter unit (SFU). The SFU consists of a holder for two sequential filters University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 33 Figure 2.3: : Gent Sampler Unit constructed by the Norwegian Institute for Air Research (NILU). The initial filter is an 8 µm pore 47 mm Nuclepore filter and the second filter is an 0.4 µm pore Nuclepore filter. At a flow rate of 16 l/min, the unit act as a dichotomous sampler. The flow through the 8 µm pores will result in collection of 2.2 µm particles with 50% efficiency (Cahill et al., 1979). The <2.2 µm particulates are then collected on the 0.4 µm filter (Cahill et al., 1979) 2.5.3 Filters The material on which the aerosol sample is collected is very important if any meaningful results are to be obtained from analysis. The material University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 34 Figure 2.4: : Stack filter Unit should have a very good properties like the ability of retaining the particle, without it coming off easily, but must permit air flow readily through it. the sample collected should be provided in manner that would make it easy to analyze both for mass concentration and chemical composition. Fibrous and porous membrane filters are most commonly and important filter types used in aerosol sampling. 2.5.4 Fibrous membrane filters Glass fiber, quartz and cellulose (paper) are the most common types of fibrous membrane filters. They have high porosity from 70 to greater than 99% and range in size from sub-micrometer to 100 µm . They have lower University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 35 pressure drops across them and are well suited for high volume sampling. High -efficiency filters have a low air velocity through them. Thus to obtain a large filter area in an element of convenient size the filter element are pleated. The glass and quartz fiber filters have retention of particles with sizes above 0.3µm . Glass fiber filter has very high capacity but the only problem is its ability to convert sulphur dioxide to suplhate hence increasing the load. It also has very high blank values for a lot of elements and therefore it is unsuitable for trace elements analysis especially with nuclear analytical techniques. Quartz filter also has very high blank values for a lot of elements. Cellulose (paper) filter has only one problem, its inability to maintain the same humidity for the exposed and unexposed filters. In general the penetration of particles into the filter mat of fibrous filter is not uniform thus they are not useful for EDXRF, PIXE and PIGE. 2.5.5 Porous membrane filters Most common filters used in nuclear analytical techniques are Nuclepore and Teflon filters. Researches conducted have really shown the efficiency of porous membrane filters and it has been found that the collection effi- ciency depends on pore size, especially with the nuclepore filters(Dzubay and Barbour, 1983) (John and Reischl, 1978), (Liu and Lee, 1976). One problem that is associated with these filters is the loss of coarse particles University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 36 from the filters in handling and transport from sampling site to the labora- tory which has been noticed by a number of researchers around the world. Dzubay and Barbour have suggested the use of oil coating on the filters to prevent such shake off effects(Dzubay and Barbour, 1983) 2.6 Particulate matter measurement tools There are several instruments for measuring different characteristics of par- ticulate matter. The most important measurements of particles are particle concentration and particle size. IBA (PIXE and PIGE) and EDXRF these methods are the most widely used because they are relatively more acces- sible to many users, are non-destructive techniques, and require no sample preparation. After the analysis, the filters can be reused in the other meth- ods. PIXE, PIGE and EDXRF techniques can be used in the determina- tion of the concentration of elements with atomic numbers ranging from 11 (sodium) to 92 (uranium). In this work the analytical methods used for the determination of elemental concentration in the aerosol samples are: • Proton Induced X-ray Emission (PIXE) analysis • Energy Dispersive X-ray Fluorescence (ED XRF) analysis • Proton Induced Gamma-ray Emission (PIGE) analysis University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 37 (Sandström et al., 2005), (Schwarze et al., 2006). A multi-national team of researchers have really shown that these techniques provide an excellent elemental concentrations with hourly resolution and the Lower Detection Limits (PIXE) of the order of ngm3 for most elements (Annegarn et al., 1988). These techniques are the best option in the area of determination of elemental concentrations in airborne particulate matter . Moreover, el- ements with atomic number below 11 (Na) cannot be detected by PIXE, because of the X-rays self-absorption in the sample and in the detectors dead-layers. However, the elements detected by PIXE contribute to a frac- tion of particulate matter ranging from 10 30% but comprises the tracers used to identify the particulate matter sources and potentially harmful el- ements (like As, Se, Ni, Cr, Zn, Cu). A lot of researches have shown and proven the qualities of PIXE in the field of aerosol studies in the world (Johnson et al., 2006), (Maenhaut et al., 1996) (Marcazzan et al., 1987), (Prati et al., 2000). 2.6.1 Proton induced x-ray emission, PIXE This is technique where x-rays from inelastic collision of the projectile with inner core electrons are detected. PIXE is especially sensitive to trace ele- ments and allows the determination of concentrations at a ppm level (Gar- man and Grime, 2005). However, PIXE generally does not yield depth information. When lower Z elements are detected , the preferred technique University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 38 is PIGE. PIXE is one of the variants of X-ray emission techniques. This technique has also been using in the field APM analysis(Lannefors et al., 1983) of PIXE, the ion knocks an inner-shell electron out of the atom and an X-ray is emitted as the vacancy is filled by an outer-shell electron. The energies of the X-rays are characteristic of the elements in the sample and the intensities of the X-ray lines can be used to calculate the concentration of the elements. This technique provides information on selected elements from Si to U with good sensitivity. Minimum detectable limits for PIXE analysis of aerosol samples are typically on the order of a few ngm3 (Jo- hansson et al., 1995). A beam of energetic protons are accelerated onto a target (sample) of interest. This causes ionization and atomic excitation, leading to the ejection of inner-shell electrons from atoms in the target. The inner vacancies created are filled by outer shell electrons resulting in the emission of X-rays which are characteristic of the targets elemental com- position and concentration. The X-ray spectrum is usually recorded in an energy dispersive mode using a Si(li) detector. The energies of the emitted X-rays are used to identify the atoms or elements in the target, and the X-ray intensities used to determine the element concentration. The PIXE facility, like any accelerator-based facility, consists essentially of ion sources, an accelerator system, a beam transport system, end stations (scattering chambers), and computer control system. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 39 2.6.2 Quantification analysis using PIXE The main parameters needed to quantify the elements of interest in a sample is the number of x-rays from a transition j of the element z detected during PIXE measurement. It is known as yield and written as as follows: Ω Q NAv.(ρtz) Y0Xj(Z) = σXj(Z,E0).αZ .det . (2.7) 4π e A Where: σXj(Z,E0) is the x-rays production cross section; αZ is the fraction of transmitted x-rays from the target to the detector sensitive area; Ω are the solid angle covered by the detector and its intrinsic efficiency; ρtz is the mean a real density of the element Z in the surface impinged by the beam, NAv the Avogadro number and A the atomic mass of the element Z; Q is the integrated beam charge, e the unit charge of the particles. For a given x- ray, beam energy, detector, and measurement geometry, an efficiency factor η can be defined as follows: Y0Xj(Z) = η.Q.(ρt)z (2.8) Equation ?? hence becomes: Provided η is known, the areal density (ρt)z of a given element can be obtained by measuring the corresponding x-ray yield on the detector and University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 40 the integrated beam charge on the target. However the efficiency factors η(Z) are usually obtained experimentally using equation ??. This is per- formed by irradiating elemental standards with known quantities (ρt)z of various elements and measuring the corresponding x-ray yields. The same conditions (same geometry and same beam energy) must be used for the standard and the samples to be analysed. When target cannot be assumed to be thin, corrections to the x-ray yield are required due to energy loss of the proton beam in the target matrix and attenuation of the emitted x-rays passing through the target. A matrix correction factor FXj can hence be introduced. The factor is defined as the ratio between the ideal yield Y0 that would be obtained from the same target, in the absence of matrix effects, and the actual yield Y: Y0Xj(Z) FXj(Z) = (2.9) YXj(Z) Putting equation ?? and equation 2.9 the corrected elemental areal density (ρt)z can be rewritten as : YXj(Z) (ρt)Z = .FXj(Z) (2.10) Q.ηz, E0 According to Boni and Jex, apart from these low Z elements (Na, Mg, Al, and Si) which cannot be detected due to their self-absorption effects University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 41 within the sample. The equation 2.10 can be used to approximate an aerosol sample to a thin target with the aim of simplifying the quantitative analysis, and apply the needed correction afterward (Boni et al., 1990), (Jex et al., 1990) . 2.7 APM analysis using PIGE technique PIGE technique, (PIGE is an acronym (Proton Induced Gamma Emis- sion)), is used for detection of gamma rays. Proton induced γ-ray emission PIGE is a nondestructive technique for material analysis with ion acceler- ators. When charged particles, such as MeV protons, hit matter nuclear reactions can be induced and characteristic γ-radiation is emitted. These γ-rays allow to identify the elemental composition of the sample. 2.7.1 Concentration calculation using PIGE The level of concentrations Nt in PIGE are obtained from measured quanti- ties(i.e γ-ray peak areas) and the microscopic data such a stopping powers and differential cross sections of the interaction as well as the detector absolute efficiency. The gamma-yield ηγ for a detected element is given as: ∫ E0 dσ dE ηγ = ηp.Nt.4Ω. (E, θ) (2.11) E0−4 dΩ S(E)E University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 42 Where ηγ is the beam intensity; 4Ω is the detector solid angle;  is detection efficiency; dσ (E, θ) is energy dependent differential cross section; dE is the dΩ S(E) stopping power of the target. 2.7.2 Quantification of APM samples using PIGE Light elements like Na, Mg, Al and Si with no self-absorption of emitted gamma radiation inside the sample can be determined quantitatively using PIGE, the unknown elemental concentration are deduced by comparing the γ-ray yields with those of thin elemental standard. Choosing the beam en- ergy is crucial since the gamma emission cross sections have to be constant over the energy loss in the sample. If the PIGE cross section is constant over energy range 4E the equation ??can be rewritten as ηγ = ηp.σ..4Ω.Nt (2.12) The unknown concentrations can be deduced by comparing the γ-ray yield to those of thin elemental standards as follows: In this work, approximation of thin target were done for Na and Al. This was to enable us correct for underestimation of Na and Al that occurs in PIXE analysis due to low energy x-ray absorption in the aerosol particles. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 43 Figure 2.5: : Electromagnetic Spectrum 2.8 X-Ray Fluorescence Analysis X rays are electromagnetic radiation. All X-rays represent a very ener- getic portion of the electromagnetic spectrum and have short wavelengths of about 0.1 to 100 Å. They are bounded by ultraviolet light at long wave- lengths and gamma rays at short wavelengths X-rays in the range from 50 to 100 Å are termed soft X-rays because they have lower energies and are easily absorbed. X-Rays have the following general properties: • Invisible; • traveled at the speed of light (3 108m/s) • not affected by both electrical and magnetic fields University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 44 • Differentially absorbed in passing through matter of varying compo- sition, density and thickness; • Reflected, diffracted, refracted and polarized; • ionizing of gases is possible ; • affect electrical properties of liquids and solids ; • blacken a photographic plate; • capable of liberating photoelectron and recoils electrons • Emitted in both a continuous spectrum and with a line spectrum characteristic of the chemical element; • have absorption spectra characteristic of the chemical element 2.8.1 X-Ray Emission X-rays are produced from the disturbance of the electron orbitals of atoms. This may be achieved in so many ways, the bombardment of a target ele- ment with high energy electrons is regarded as the most common, X-rays or accelerated charged particles. The first two are frequently used in X-ray spectrometry, either directly or indirectly. Electron bombardment results. in both a continuum of X-ray energies and radiation that is characteristic of the target elements. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 45 Figure 2.6: : Bremsstrahlung 2.8.2 Continuum X-rays They are produced when electrons or high energy charged particles lose en- ergy in passing through the Coulomb field of a nucleus. In this interaction, the radiant energy (photon) lost by the electron is called Bremsstrahlung as shown in the figure 2.6. The emission of continuous X-rays finds a simple explanation in terms of classic electromagnetic theory, since according to this; the acceleration of charged particles should be accompanied by emis- sion of radiation. In the case of high energy electrons striking a target, they must be rapidly decelerated as they penetrate the material of target, and such a high negative acceleration should produce a pulse of radiation. 2.8.3 Characteristic Emission The purpose of X-ray fluorescence is to determine chemical elements both qualitatively and quantitatively by measuring their characteristic radiation. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 46 To do this, the chemical elements in a sample must be caused emit X-rays. As characteristic X-rays only rise in the transition of atomic shell electron to lower, vacant energy levels of the atom, a method must be applied that is suitable for releasing electrons from the innermost shell of an atom. This involves adding to the inner electrons amounts of energy that are higher than the energy bonding them to the atom. This can be done in a number ways: • Irradiation using elementary particles of sufficient energy ( electrons, protons, a-particles) that transfer the energy necessary for release to the atomic shell electrons during collision processes. • Irradiation using X- or γ-rays from radionuclides. • Irradiation using X-rays from an X-ray tube 2.8.4 X-Ray Interactions with Matter When X-rays of intensity, I0 are directed into an object of finite thickness, d, and density, , the transmitted intensity of photons which have not suffered interactions, I, in the object is given by the Beer-Lambert law. I = I0e µρd (2.13) University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 47 Figure 2.7: : the transmitted intensity of the photon The linear absorption coefficient has the dimension [1/cm] and is depend on the energy or the wavelength of the X-ray quantity and the special density (in gcm3 of the material that was passed through. It is not the linear absorption coefficient that is specific to the absorptive properties of the element, but the coefficient applicable to the density ρ of the material that was passed through: The mass attenuation coefficient accounts for the various interactions and is therefore composed of here major components: µ(E) = τ(E) + σcoh(E) + σinc(E) (2.14) University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 48 τ(E) is the photoelectric mass absorption coefficient; σcoh(E) is the coher- ent mass absorption coefficient; σinc(E) is the incoherent mass absorption coefficient. some of the photons interact with the particles of the matter and their energy can be absorbed or scattered. This absorption and scattering is called attenuation. Other photons travel completely through the object without interacting with any of the materials particles. The number of pho- tons transmitted through a material depends on the thickness, density and atomic number of the material, and the energy of the individual photons. Even when they have the same energy, photons travel different distances within a material simply based on the probability of their encounter with one or more of the particles of the matter and the type of encounter that occur. Since the probability of an encounter increases with the distances traveled, the number of photons reaching a specific point within the matter decreases exponentially with distance traveled. 2.8.5 Energy dispersive x-ray fluorescence ( EDXRF ) spectrometry Energy dispersive X-ray fluorescence (EDXRF) spectrometry is one of the most commonly used analytical technique to determine the elemental com- position of airborne particulate matter collected onto filter (Bandhu et al., 2000). It is capable of providing the composition of a variety of materials University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 49 in a non-destructive manner. This technique is able to provide qualitative and quantitative information on multi-elemental concentration data of par- ticulate matter. This data is essential for source identification work and for application of receptor models for source apportionment. A large num- ber of studies on the elemental concentration of particulate matter using EDXRF spectrometry technique have been reported (Aboh, 2000), (Hung et al., 2002), (Watson et al., 1999). In EDXRF method, the sample on the filter is irradiated with a high intensity beam of X-rays. This causes electrons to be ejected from the inner shell orbital of atoms in the sam- ple and the vacancies created are filled by electrons in the outer atomic shells. The excess energy is released in the form of (fluorescence) X-rays which is seen as a line in a spectrum whose energy is characteristic of the elements present. The intensity of the characteristic (fluorescent) X-rays is proportional to the concentration of the element in the sample. The X-ray fluorescence emitted from the irradiated sample is captured by the semiconductor detector and processed by the signal processing unit. Some advantages of EDXRF over other analytical techniques are: • It is non-destructive; therefore samples are left intact after analysis so they can be submitted for additional analyses by other methods as needed. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 50 • It requires little or no sample preparation or operator time after the samples are loaded into the analyser. • It is fast and can be used to simultaneously quantify the concentra- tions of elements with atomic numbers ranging from 11 (Na) to 92( U ). 2.8.6 Basic configuration of XRF A basic X-ray spectrometer system consists of a source that generates Xrays, a solid state detector that measures the different energies of the characteristic radiation from the sample, a sample holder and a signal pro- cessing unit that records the emission or fluorescence energy signals and calculates the elemental concentrations in the sample. 2.8.7 XRF quantitative analysis Quantification of the element of interest in XRF is given by the equation below 1− exp[−µs(E0, Ei)ξ] Ia(Ei) = I0.G.σi(E0).fab(Ei).d.Ca (2.15) µs(Ei) University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 51 where I0: incident beam intensity; Ia(Ei):characteristic x-ray intensity; G:geometrical factor; σi(E0:cross section of element i; fab(Ei: air absorp- tion; d: detector efficiency; Ca: Analyte concentration;µs(E0, Ei:Selfattenuation coefficient; ξ: target thickness There are three ways by which this equa- tion can be applied, they are fundamental parameters approach,elemental sensitivities approach and the direct comparison of count rates. 2.8.8 Fundamental parameters approach In this approach,the concentration can be calculated from the intensities of characteristics lines of the elements and a lot of theoretical values are al- ways taken into account. Theoretical values like: the spectral distribution of the excitation source,the fundamental parameters, various absorption corrections, the inter-elements(enhancement) effect, and the detector effi- ciency are needed in the concentration calculation. The scatter peaks are used to correct the sample self-absorption by the dark matrix. 2.8.9 Elemental sensitivity approach In this method, the sensitivity of characteristics lines from standards, taking into account the self absorption standard. The sensitivity calculated can now be used to determine the concentration of elements in unknown sample. But in this case the composition of the dark matrix is known. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 52 2.8.10 Direct comparison of count rates The simplest techniques for quantitative analysis is direct comparison of count rates. By using the sensitivities determined from standards,concentrations of elements in unknown can be determined. No absorption correction are calculated. Therefore it is advised to work with this technique only as long as these corrections are equal for standards and unknown samples. 2.9 Source apportionment of APM A better policy strategies can be drawn for the reduction of APM concen- tration in ambient air, if the original sources of the particulate matter is known, and we can easily quantitatively apportion the observed aerosol par- ticle concentrations among the various source types. Source apportionment methods provide the tools to formulate efficient and effective particulate matter control strategies and to develop policy to prevent human expo- sure. Pant and Harrison defined SA as the methods used to quantify the contributions of different source categories to the particulate matter concen- trations measured in the atmosphere (Pant and Harrison, 2012). Emissions of APM are the combination of the different chemical species from various sources. Source apportionment models use the mass and chemical compo- sitions of the particulate matter measured at a sampling site to resolve the University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 53 main source types and estimate their contributions to the measured ambi- ent particulate matter at that site (Belis et al., 2013). Particulate matter emissions from specific sources often have unique fingerprint by which the contribution of these sources to the total aerosol particles at the receptor can be recognized(Watson, 1984). 2.9.1 Receptor modelling methods Receptor models are mathematical or statistical techniques used to identify and quantify the sources of airborne pollutants at a receptor or sampling site. The fundamental principle of receptor modelling is that mass conser- vation is assumed and on this basis a mass balance analysis can be used to determine and apportion ambient particulate matter concentrations to indi- vidual emitting sources (Dominici et al., 2010). Most receptor models used for source apportionment are in principle based on the assumption that the concentration of the pollutants measured at the receptor site is equal to the sum of the concentrations induced by the surrounding emission sources emitting the pollutants(Belis et al., 2013), (Fabretti et al., 2009). The main objective of receptor models is, therefore, to identify the possible sources of particulate contaminant and to obtain data on their contributions to the bulk particle mass. Receptor models are commonly divided into two pri- mary categories: chemical mass balance and multivariate methods(Pollice, University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 54 2011). The selection of the appropriate method depends on prior knowl- edge on the sources and source profiles. If the sources are known and detailed information on source profiles is available, Chemical Mass Balance (CMB) models can be applied. Alternatively, in cases where the sources are unknown and only the concentration of ambient pollution is known, multivariate methods such as Positive Matrix Factorization (PMF) meth- ods are preferred. Examples of the commonly used receptor model software in physical and chemical sciences applications include the USEPAs Positive Matrix Factorisation ((PMF 5.0) (Norris, 2008), Unmix 6.0 and Chemical Mass Balance (Chan et al., 2011). Enrichment factor analysis and multi- variate techniques such as correlations and Positive Matrix Factorization (PMF) were used to define a relationship between the sources and the recep- tor. These analytical methods were combined to assist in the identification of sources and the apportionment of the observed pollutant concentrations to those sources in Agbogbloshie ,Accra, Ghana. Receptor models of pos- itive matrix factorization ( PMF ) and enrichment factor (EF) were used in this study. 2.9.2 Positive matrix factorization USEPA Positive matrix factorization (PMF) is a powerful and widely used multivariate method used extensively for source apportionment of ambient particulate matter (Paatero, 1997), (Paatero and Tapper, 1994). USEPA University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 55 PMF model resolves the dominant positive factors that contribute to PM samples without prior knowledge of the sources. This is achieved by us- ing measured concentrations of particulate matter to estimate the number of sources, the source composition, and the source contribution to each sample. The goal of PMF modelling is to • determine the number of factors (sources or chemical/physical pro- cesses) that adequately explain the input data set variability and • find correlation among the measured variables. The advantages of the PMF relative to the traditional factor analysis meth- ods such as Cluster Analysis (CA) and Principal Component Analysis (PCA) are: • Unreliable data, such as observations below detection limit or miss- ing values,can be included in the PMF analysis by giving them low weights to decrease their influence in the modelling (Paatero and Tap- per, 1994). • Data characterized by heavy positive skewed distribution can also be handled by down-weighting those extreme points to reduce undue influence in the model (Huang and Conte, 2009). • PMF model assumes the non-negativity of factors and does not rely on information from the correlation matrix but utilizes a point-bypoint University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 56 least- squares minimization scheme by taking into account the uncer- tainty of each data point(Pongkiatkul and Oanh, 2012). In PMF, a matrix X(m × n), where n is the number of samples and m is the number of chemical species is factored into two matrices, G(n× p) and F (p ×m) , where p is the number of independent source types or factors extracted, and a residual matrix E is used to account for the unexplained part of X. The factor analysis model can be written in matrix form as shown in equation 2.16. in component form as X = G.F + E (2.16) in component form as ∑p xij = gijfkj + eij (2.17) k=1 where xij is the measured concentration at a receptor for the jth species in the ith air sample, gik is the particulate mass concentration from the kth source type contributing to the ith air sample,fik is the jth species mass fraction from the kth source, and eij is the residual between measured and modelled concentrations for the jth species in the ith sample. The objective of the multivariate receptor modelling is to determine the number University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 57 of sources(p), the source contributions gik , and the chemical profiles fik of the identified sources that best reproduce xij. To obtain the best fit of the model, PMF minimizes the object function, Q or chi-squared based upon the uncertainties of σij of each observation. The object function is given by the equation: ∑m ∑m ( )2eij Q = (2.18) s i=1 j=1 ij ∑m ∑m ( )2 Q = x ∑ 2ij− p gikfkj ÷ s (2.19)k=1 ij i=1 j=1 where sij is the uncertainty in the jth species in the ith sample; gik ≥ 0; and fik ≥ 0; k = 1,...,p. PMF simultaneously adjust the elements of G and F in each iterative step until a minimum value of Q is obtained (Paatero, 1997), (Polissar et al., 1998). The Q value can be used to determine the optimum number of the factors. The theoretical Q value should be approximately equal to a value ofX(n ×m) , the number of entries of data array or the number of degree of freedom of the datum in the data set. University of Ghana http://ugspace.ug.edu.gh Chapter 2. Literature Review 58 2.9.3 Enrichment Factor ( EF ) Enrichment factor model is an indicator used to assess the presence and intensity of anthropogenic contamination and those from natural sources. It also helps in the estimation of the degree of anthropogenic contamination. A lot of research works have been done using Enrichment factor (EF) in the field of APM. It is used in particle source apportionment studies to identify the major sources of air pollution and to quantify contributions of all sources of all measured pollutants(Cao et al., 2003), (Chao and Wong, 2002),(Davidson et al., 1986),(Winchester et al., 1981),(Zhang et al., 2008). ( ) CX CRef Sample EF = ( ) (2.20) CX CRef Background where CX is the concentration of the element of interest and CRef is the concentration of reference element for normalization. University of Ghana http://ugspace.ug.edu.gh Chapter 3 Chapter 3: Methodology 3.1 Aerosol characterisation at Agbogbloshie Agbogbloshie is situated between the latitude 05◦35′45′′N and the longi- tudes 00◦06′45′′W . Approximately, it covers an area of 16km2, and has an estimated population of 80000 people (Amankwaa, 2013). Agbogbloshie is connected to the capital city of Ghana by road with less than a kilometre. Agbogbloshie is a suburb of Accra. Accra is located along the coast of Gulf of Guinea. The location and the major activities of the area give reason for one to expect air pollutant contributions from various sources. However the major potential source of air pollutants in that area is expected to be e-waste burning from the scrap markets; other potential sources of air pollutants include vehicular emission, open burning of refuse, and windblown dust 59 University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 60 Figure 3.1: Designed protocol for the investigation from the market as a result of the activities of buyers and sellers. At Agbogbloshie, measurements were made for the particulate matter ( PM ) mass and several elements in PM2.5 and in PM10−2.5. The aim was to characterize the aerosol composition in the Agbogbloshie scrap market. Data were collected for a period spanning from May 2010 to April 2011 in order to cover both dry and wet seasons. These data were used to examine to what extent the gravimetric PM mass could be explained by the measured components, and thus aerosol source apportionment could be achieved. A designed strategy used for the entire project is shown in figure 3.1. University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 61 3.1.1 Selection of sampling area Agbogbloshie is small area and densely populated area, moreover on that small area there are lots of mixed activities like industries, markets , banks and the e-waste (dismantling and burning). APM samples were collected at the scrap market in Agbogbloshie, where they usually openly burned the e-waste between this period of time that is from May 2010 to April 2011. The field measurements of fine and coarse PM was carried out at a monitoring site located at the Agbogbloshie Unilever depot premises, which is 0.5 km away from the burning yard. 3.1.2 Sampling and gravimetric procedures The operation procedure of the Gent sampler used in this work was adopted from Andreas Markowitz instructions and installation manual(Maenhaut et al., 1994). The Gent sampler was placed at about 2.0 m above the ground level. The Gent sampler was set up in an open area, so that the circulation of air around it is not hindered by anything. The Gent sampler is made up of compact vacuum pump system that is controlled by a timer and connected to the stacked filter head unit, into which the different size faction filters are loaded. Its flow rate is regulated between 16-17 l per min. Particles with aerodynamic diameters greater than 10 micrometers are removed from the air sample by inertia separation on a pre-impaction University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 62 plate with a greased surface. Particles smaller than 10 m are drawn through the tube onto the stacked filter cassette unit containing two filters but the bigger particles are caught up in the grease. The nuclepore polycarbonate filters used for the coarse and fine participate collection are arranged in series. The particles are first directed onto the coarse filter where the coarse particles > 2.5µm settles but the fine particles <2.5 µm pass through and continue onto the fine filter. The samples were collected during the period May 2010 to April 2011. The sampling time were approximately Twenty-four (24) hours of continuous sampling. However, the actual time of collection of PM on the filter varied between 8 and 16 hours depending on weather conditions so as to avoid overloading of the filters, also to make sure the flow rate remained within the recommended range of the sampler. The filters were conditioned for 48 hours, before and after sampling in the laboratory. The weighed filters were put in Petri dishes, and put in the desiccator. The net mass of each samples collected can be calculated by subtracting that of the empty filters from the loaded filters. The total volume of air sampled is determined from the total volumetric flow rate (l per min) and sampling time in seconds. The mass concentrations of coarse and fine particulates in the APM are calculated as total mass concentration of collected particulates divided by the volume of air sampled in actual conditions (Ayers et al., 1999). It is usually measured in µg/m3 University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 63 MLF −MBF Cm = (3.1) VFR × ST Where Cm = concentration in µg/m 3; MLF = loaded filter mass in g; MBF = blank filter mass in g; VFR = volume flow rate in m3h1, St = sampling time in hours. Fisher brand PS-60 micro-balance with readability of 0.1 mg was used for the mass measurement. The mass of the APM of the two fractions is tabulated in table 4.1. Consequently, calculated daily mass concentrations of all the particle size fractions (fine, coarse and PM10 are shown in table 3.1.3 Analysis of APM sample PIXE techniques is used for multi-elemental determination, is non-destructive, no sample preparation is needed and sensitive to trace elements. Measure- ment time of the sample ranges between (2-10 min), and detection element as low as (0.10 ppm). Since the APM samples are thin samples, it is an appropriate technique for this work (JOHANSSON, 1992). University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 64 Figure 3.2: LABEC laboratory of INFN in Florence, Italy 3.1.4 Experimental setup for ion beam analysis Analysis of the APM samples were done at the National Institute of Nuclear Physics (INFN) Accelerator Laboratory in Florence, Italy as shown in the figure 3.2. The laboratory has an external beam for the measurements of elemental composition of APM. The PIXE and PIGE techniques were employed in the analysis of all APM sample collected. Ion source was used to generate proton beam energy of about 3 Mev and University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 65 a charge of 11 µC. The beam is then extracted in air through a 500 nm thick Silicon Nitride window. The APM samples were placed at distance of about 1 cm,from the detector perpendicular to the beam. The size of the beam was determined by the bare collimation in vacuum in the last section of the beam line. Efficiency of all the elements can be obtained by balancing the counting rates of the different X-rays. This can be achieved by using high beam current (5-15 nA) and the optimisation of the two silicon drift detectors (SDD-1 and SDD-2) were done for low and medium to high X-ray energies respectively. SDD-1 has a resolution of 170 eV, thickness of 8 mm and an active area of 13 mm2, it was placed at about 45◦ relative to the beam direction, at a distance of about 6 cm from the target. SDD-1 has an ultrathin entrance window (8m of Be) and with the use of a helium (He) gas flow into the volume in front of the detector, it can detect with good efficiency X-rays of very low energies, up to about 1 keV (K line of Sodium). The SDD-2 has a resolution of 190 eV, thickness of 25 mm and an active area of 80 mm2, it is positioned at an angle of 55, at a distance of about 2-3 cm. A Mylar foil of about 400 mm is used to attenuate the low energy X-rays; SDD-2 was used to measure high-Z elements (Ca and above). High purity germanium (HPGe) detector has a resolution of 1 keV with thickness of 23 mm and an active area of 2800 mm2, it was placed at an angle of 125◦ and at a distance of about 10 cm from the target was used for the PIGE analysis. Since this detector University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 66 was to measure characteristic γ rays form the target, lead blocks was used to shield the background γ rays produced by interaction of beam protons with elements of the beam-line. The X-rays and γ-rays measured by each detector were processed by an acquisition system that consists of conventional preamplifier-shaper-ADC electronic components for nuclear spectroscopy (one for each detector). Each preamplifier produces a signal (whose rise time is about 100 ns), sends it to a shaper-amplifier to obtain a quasi-gaussian output; a shaping time 61 s) is carefully chosen to obtain a good energy resolution without producing high pile-up effects (at a rate of about 1-2 kHz). The shaped pulses are fed to an Analogue-to-Digital Converter (ADC) which is coupled to a computer for the visualization and storage of the spectrum through a multichannel analyser (MCA). Likewise, the charge integrated by the Faraday cup is converted into logic pulses by a Charge-Frequency-Converter ( CFC); these pulses are also shaped and sent to one of the shaping amplifiers together with the real preamplifier signals. 3.1.5 Sample preparation in PIXE and PIGE analyt- ical techniques No sample preparation is required, the APM samples do not need any chemical preparations or treatment before analyses. However care need to to be taken when mounting them on the sampler holder. Naturally aerosol University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 67 filters are fragile, they must be handle well, to avoid destroying them. Total number of filters analysed are summarised in AppendixA and AppendixB. 3.1.6 Sample analysis using PIGE and PIXE tech- niques Both PIGE and PIXE techniques were used in the analysis of APM samples. For easy correction of matrix absorption of low-Z elements (Na, Mg, Al and Si) PIGE measurements of Na and Al were carried out. This required a careful choice of the proton beam energy so as to produce proton-gamma reactions 23Na(p,p0)23 Na and 27Al(p, p0)27 Al for Na and Al respectively. To achieve this half of the samples were analysed at a beam energy of 2870 keV and the other half at the energy of 2930 keV. Gamma-ray cross sections at energy regions around 2870 keV and 2930 keV vary very slowly for Na and Al respectively; hence, PIGE analyses for both elements were performed successfully. In both cases PIXE analyses were also carried out successfully as x-ray cross section does not vary much with changing energy. A beam spot size of 3 mm diameter was used to scan filter area of about 1 cm2 for an irradiation time of 3 minutes per sample. Spectra for PIXE and PIGE were collected for onward analyses. The spectrum acquisition time was 180 seconds per sample, and the Procemxr software was used for this purpose. University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 68 3.2 PIXE elemental quantification 3.2.1 Spectrum integration The PIXE x-ray spectra (collected with the two silicon detectors) deconvo- lution was done using the Iterative Least Squares fitting of GUPIXWINr software package (Maxwell et al., 1989) which uses an X-ray spectrometry library and some information about the experimental set-up. Before a spec- trum is fitted to obtain the net x-ray counts the continuous background, made up of the bremsstrahlung (break radiation) emitted by secondary elec- trons ejected in the sample and the Compton background, is subtracted ap- plying a digital filter operator. The net peak area for each (in both samples and standards) are recorded for use in elemental concentration calculation. The choice of GUPIXWINr is based on several considerations, the main ones being the better status of the databases (cross sections, stopping pow- ers and attenuation coefficients). Another advantage is its availability as well useful options such as matrix iterations, layer thickness iteration and analyzing spectra in batches. In this study, GUPIXWINr was used only to calculate the peak areas because of data imperfection it exhibits when it is used to calculate concentration values for thin target samples. In addi- tion, the INFN Accelerators end station has good shaping time analyzers which automatically measure experimental parameters (live-time, pile-ups, charge, etc.) required for absolute calculation of elemental concentrations University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 69 3.2.2 Sensitivity calculation of elements According to Maenhaut and Raemdonck, the detection efficiency for each element was computed from data collected on all the three detectors (SDD- 1, SDD-2, and SDD-3) for PIXE using equation ?? (Maenhaut and Raem- donck, 1984). ( ) PA ItQ rtstd + pµstd z (E) = . . (3.2) CstdQme Itstd rtstd Where,Cstd : thin standard areal density; PA : net peak area; Qme : mea- sured charge; ItQ : live time recorded on the charge detector; Itstd : live- time recorded on the PIXE detectors, rtstd : real-time of measurement; and pµstd : detector pile-up time. 3.2.3 Calculation of concentration of elements Data from GUPIXWINr peak fitting were used for the concentration of el- ement and uncertainty calculations in a thin target approximation. Blank nuclepore filter concentrations of element were deducted from sample com- positions to obtain the net aerosol concentrations of element. University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 70 3.3 Determination of Na and Al using PIGE technique Majority of high -Z elements were determined by PIXE technique but low -Z elements are always underestimated due to their x-ray absorption in the sample. This happened only in the coarse fractions. For that matter, the concentration of low -Z elements (Na and Al) in the coarse fraction were measured by PIGE technique. However the concentration of Mg and Si were determined by an interpolation. PIGE spectrum fitting was done using jRadView software. Elemental concentrations were calculated using the absolute reference method expressed in equation ?? (Calzolai et al., 2010). This has been rewritten to take into account the blank filter values and the background counts recorded on the Faraday cup: ( ) ( ) Ysamp − Yblank Qstd (ρzt)samp = (ρzt)std . (3.3)Ystd − YFC Qsamp The measurement uncertainty was given as follows: √ δYsamp δYstd 5 δ = ( )2.( )2.( ) (3.4) Ysamp Ystd 100 where δYsamp and δYstd are errors in peak fitting for sample and standard respectively; Ysamp and Ystd are net peak areas for sample and standard University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 71 Figure 3.3: A filter being mounted on a holder-cup (left); and sam- ples loaded in the spectrometer (right) respectively; 5/100 (5%) is the error associated with the measurement in- struments. PIGE results were plotted against those obtained with PIXE to determine the correction factor for the underestimation of Na and Al, whiles interpolated factors were used Mg and Si. 3.4 Analysis of Pb using EDPXRF Some of the samples (fine and coarse) were analysed for elemental compo- sition using energy dispersive X-ray fluorescence spectrometry (ED-PXRF) at the National Institute of Nuclear Physics (INFN) Accelerator Labora- tory in Florence, Italy. Here again the sample did not need any special preparation and were mounted at the bottom of stainless steel holder-cups and place directly in the spectrometer for irradiation figure 3.3. University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 72 Figure 3.4: Schematic diagram of Epsilon 5 Spectrometer in 3 D The spectrometer used in this study was an Epsilon 5r (PANalytical, Almelo, The Netherlands) equipped with a Gd anode x-ray tube operated at max- imum values of 100 kV, 600 W, and a current of 7.5 mA. The Samples were irradiated by X-rays from Gd tube under vacuum equipped with a liquid nitrogen (N2) cooled PAN-32 Ge X-ray detector having a beryllium window thickness of 8 µm. The primary x-ray from the Gd tube irradi- ated a secondary target to produce a near monochromatic x-ray which in turn impinge on the sample. The resultant characteristic x-rays are then collected on the detector figure 3.4. The systems in-built software (Epsilon 5 software) automatically analysed the sample spectrum and determined the net peak intensities corresponding University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 73 to detected elements. Concentration in (µg/cm2) of element of interest were calculated using the direct comparison of counts approach with the equation 3.5: ( ) Ysamp − Yblank (ρt)z = (ρt)std . (3.5)Ystd − YblankMylar where, nuclepore blank (used in sampling) and Mylar blank (used in prepar- ing the thin standards) filters were used to account for filter components. Minimum detection limits (MDLs) were calculated using three times the square root of the background as recommended by L. Currie (Currie, 1999). ( )i M LLD = (3.6) A min √ BGi LLD = 3. (3.7) Sit √ ( )i BGi M LLD = 3. . (3.8) Nit A standard where LLD is lower limit of detection, BG: background, University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 74 3.5 Data Analysis 3.5.1 Enrichment Factor ( EF ) A very good description of APM elemental concentration was obtained by calculating the EF. It was computed by using Wedepohls values for the elemental composition of crustal rock (Wedepohl, 1995). However silicon (Si) was chosen as the normalizing element, because of its abundance in the earth crust and less affected by anthropogenic pollution. ( ( )CXS)i SampleEF = (3.9) CX Si Background where Cx is the concentration of the element of interest and Si is the con- centration of reference element for normalization. 3.5.2 Source identification and apportionment of APM samples The results obtained for the gravimetric and elemental analyses of APM samples for one year sampling have given enough data for the comprehen- sive interpretation of data using statistical tool. Both size fractions PM2.5 and PM10 were employed in the identification of APM sources and their various percentages portion occupied. The identification of various types University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 75 of sources were done on both coarse and fine fractions PM2.5, it was done by using the gravimetric and elemental composition data analysed in Pos- itive Matrix Factorization (USEPA PMF 5). Both error estimates of the measured concentration and the measured concentration of APM samples collected were input in the USEPA PMF 5 software. The concentrations of all species for all of the samples form a matrix. This matrix was de- composed into two matrices representing source contributions and source profiles. The identification of source categories was undertaken by exami- nation of their profiles. The USEPA PMF 5.0 software was used to run the model for 25 runs with different combinations of number of factors until a meaningful solution was found. The PMF is one of the best tools used in the determination of the number of factors. The results of a PMF analysis of higher dimension solution does not necessarily contain all the factors of the lower dimensions, because orthogonality is not required. 3.6 Quality control measures taken Very rigorous and rigid quality assurance and quality control ( QA/QC ) measures were adopted to achieve the following properties, i.e the results are truly representative of the sample that they are traceable and can be trusted by other laboratories or analyst. University of Ghana http://ugspace.ug.edu.gh Chapter 3. Methodology 76 3.6.1 Sampling area A lot of measures were put in place so as to achieve quality sample collec- tion, and a good quality protocols were adopted. Though I was not able to measure Meteorology data could not be measured at site, GMetro provided all the needed information. 3.6.2 QA/QC as applied to APM data Each measurement contains a degree of uncertainty due to the limits of measurement equipment and the human error using the equipment. The major sources of error concerning the sampling and analyses of particulate matter samples.However it is good to adopt quality control and quality assurance (QA/QC) measures in sampling, sample preparation and, more importantly, sample analysis. Issues such as uncertainty, sensitivity, de- tection limits, precision, reproducibility, data reporting, etc. that affect the quality of results obtained by techniques used require serious attention. Hence, irradiation facility inherent characteristics such as measurement ge- ometry, energy calibration, beam diagnostics, beam charge integration, and detection efficiencies which have a major influence on the quality of results have all been evaluated. The method performance has also been assessed for the analysis of APM samples by techniques. University of Ghana http://ugspace.ug.edu.gh Chapter 4 Chapter 4: Results and Discussions 4.1 Mass Concentration of Particulate Mat- ter A summary of PM10, PM2.5 and PM10−2.5 mass concentrations measure- ments for the monitoring site in Agbogbloshie covering the period May 2010 April 2011 is presented in table 3. The parameters recorded in ta- ble 3 are total numbers of samples sampled throughout the study period, the minimum , maximum and median mass concentration of PM10, PM2.5 and PM10−2.5 , and the ratios PM2.5/PM10 and PM10−2.5/PM10 and their percentage ratios respectively. Observations of the variation pattern of the mass concentrations have shown some influence of meteorological conditions 77 University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 78 on the particulates mass concentrations. In this work, the results obtained have also shown sharp differences in atmospheric particulate matter (APM) concentrations between the two main seasons. The lowest PM mass con- centrations were recorded during rainy seasons whilst the sharp increment of PM concentrations were recorded in the harmattan season (December to February). The mass concentrations and the trends observed are in agree- ment with the results obtained by other researchers in Ghana, Africa and the world (Aboh et al., 2009), (Afeti and Resch, 2000), (Aryal et al., 2013), (Canha et al., 2011), (Gomǐsček et al., 2004), (Lee and Hieu, 2011), (Ofosu et al., ). Table 4.1: Summary of daily Mass Concentration gcm−3 of APM in Agbogbloshie during the entire sampling period Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 05-May-10 51.31 52.37 103.68 49.49 07-May-10 55.57 55.66 111.23 49.96 09-May-10 52.88 54.53 107.41 49.23 11-May-10 49.5 51.1 100.6 49.21 13-May-10 56.13 45.71 101.84 55.11 15-May-10 38.82 60.17 98.99 39.22 17-May-10 51.93 46.33 98.27 52.85 20-May-10 50.89 46 96.88 52.52 23-May-10 43.89 63.27 107.15 40.96 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 79 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 26-May-10 56.04 60.08 116.12 48.26 30-May-10 52.03 53.39 105.42 49.36 02-Jun-10 45.02 45.95 90.97 49.48 04-Jun-10 50.43 40.98 91.4 55.17 06-Jun-10 47.5 40.92 88.43 53.72 08-Jun-10 53.11 52.67 105.78 50.21 10-Jun-10 42.23 41.67 83.91 50.33 12-Jun-10 41.24 40.84 82.08 50.24 14-Jun-10 38.24 59.42 97.66 39.15 16-Jun-10 36.09 59.99 96.09 37.56 18-Jun-10 56.33 42.44 98.78 57.03 20-Jun-10 49.48 41.2 90.67 54.56 22-Jun-10 38.75 68.91 107.66 35.99 24-Jun-10 46.93 49.7 96.63 48.57 26-Jun-10 37.81 57.43 95.24 39.7 28-Jun-10 48.61 43.4 92.02 52.83 30-Jun-10 49.72 43.93 93.65 53.09 02-Jul-10 40.31 36.52 76.83 52.47 04-Jul-10 57.57 37.23 94.8 60.73 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 80 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 06-Jul-10 53.96 30.9 84.86 63.59 08-Jul-10 51.03 43.85 94.88 53.78 10-Jul-10 51.64 53.93 105.56 48.92 12-Jul-10 51.48 34.3 85.78 60.01 14-Jul-10 39.63 49.57 89.2 44.42 16-Jul-10 52.46 41.28 93.74 55.96 18-Jul-10 56.84 37.03 93.87 60.55 20-Jul-10 53.44 29 82.45 64.82 22-Jul-10 51.38 37.11 88.5 58.06 24-Jul-10 50.58 49.34 99.92 50.62 26-Jul-10 45.45 35.91 81.36 55.86 28-Jul-10 56.27 51.59 107.87 52.17 30-Jul-10 58.33 38.23 96.57 60.41 01-Aug-10 54.35 34.65 89 61.07 03-Aug-10 49.78 36.42 86.19 57.75 05-Aug-10 52.07 36.06 88.13 59.08 07-Aug-10 59.33 38.58 97.91 60.6 09-Aug-10 68.58 29.36 97.94 70.02 11-Aug-10 51.76 25.6 77.36 66.91 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 81 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 13-Aug-10 54.77 32.79 87.56 62.56 15-Aug-10 55.27 29.59 84.87 65.13 17-Aug-10 63.04 47.25 110.29 57.16 19-Aug-10 58.88 39.8 98.68 59.66 21-Aug-10 58.09 40.98 99.08 58.63 23-Aug-10 58.57 28.78 87.34 67.05 25-Aug-10 66.5 43.14 109.64 60.65 27-Aug-10 61.22 36.27 97.49 62.8 02-Sep-10 65.1 41.13 106.23 61.28 04-Sep-10 57.59 44.87 102.46 56.2 06-Sep-10 73.07 71.47 144.54 50.55 08-Sep-10 57.89 65.99 123.88 46.73 10-Sep-10 58.47 47.35 105.81 55.26 12-Sep-10 59.91 64.79 124.7 48.05 14-Sep-10 72.66 69.03 141.68 51.28 16-Sep-10 52.42 35.4 87.82 59.69 18-Sep-10 88.43 37.27 125.7 70.35 20-Sep-10 64.74 47.11 111.85 57.88 24-Sep-10 60.64 49.1 109.74 55.26 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 82 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 26-Sep-10 71.61 62.77 134.38 53.29 30-Sep-10 87.8 46.08 133.88 65.58 07-Oct-10 55.35 42.38 97.73 56.63 09-Oct-10 57 45.75 102.75 55.47 11-Oct-10 52.96 40.58 93.54 56.62 13-Oct-10 55.27 41.96 97.23 56.85 15-Oct-10 57.23 42.8 100.03 57.21 17-Oct-10 53.68 42.45 96.13 55.84 19-Oct-10 59.77 55.08 114.85 52.04 21-Oct-10 55.92 40.9 96.82 57.75 23-Oct-10 56.92 39.19 96.12 59.22 25-Oct-10 56.28 45.04 101.32 55.55 01-Nov-10 82.5 42.76 125.27 65.86 03-Nov-10 76.58 33.63 110.21 69.49 05-Nov-10 76.04 38.76 114.8 66.24 07-Nov-10 77.41 41.73 119.14 64.98 09-Nov-10 80.17 41.84 122.01 65.71 11-Nov-10 78.65 47.12 125.77 62.53 13-Nov-10 75.62 44.6 120.21 62.9 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 83 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 21-Nov-10 67.71 40.13 107.84 62.79 23-Nov-10 80.58 38.75 119.33 67.53 25-Nov-10 75.08 36.36 111.45 67.37 27-Nov-10 81.13 32.99 114.12 71.09 01-Dec-10 147.97 26.39 174.36 84.87 03-Dec-10 158.86 40.25 199.11 79.79 05-Dec-10 151.36 59.68 211.03 71.72 07-Dec-10 136.8 47.5 184.3 74.23 09-Dec-10 152.76 104.09 256.85 59.47 11-Dec-10 153.15 23.03 176.18 86.93 13-Dec-10 206.52 164.66 371.18 55.64 15-Dec-10 153.67 99.05 252.72 60.81 21-Dec-10 159.26 37.35 196.61 81 23-Dec-10 150.84 26.06 176.9 85.27 27-Dec-10 171.94 96.35 268.29 64.09 29-Dec-10 149.01 40.82 189.83 78.5 04-Jan-11 263.38 79.02 342.4 76.92 06-Jan-11 259.52 129.29 388.81 66.75 08-Jan-11 282.26 122.66 404.92 69.71 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 84 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 10-Jan-11 257.24 57.47 314.71 81.74 12-Jan-11 287.3 65.04 352.34 81.54 18-Jan-11 344.68 89.33 434.01 79.42 20-Jan-11 257.41 82.47 339.89 75.74 22-Jan-11 349.68 98.99 448.67 77.94 24-Jan-11 258.92 71.93 330.85 78.26 28-Jan-11 282.79 110.54 393.34 71.9 30-Jan-11 265.1 97.53 362.63 73.1 05-Feb-11 147.32 43.67 190.99 77.13 07-Feb-11 141.59 36.4 177.99 79.55 09-Feb-11 139.68 35.27 174.95 79.84 11-Feb-11 150.71 37.95 188.66 79.89 13-Feb-11 138.94 25.01 163.95 84.75 17-Feb-11 145.72 34.28 180.01 80.95 19-Feb-11 132.3 28.53 160.83 82.26 21-Feb-11 137.8 33.8 171.6 80.3 23-Feb-11 132.39 26.85 159.23 83.14 27-Feb-11 129.49 34.41 163.9 79 07-Mar-11 77.66 32.87 110.53 70.26 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 85 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 09-Mar-11 80.71 40.09 120.8 66.81 11-Mar-11 68.81 36.89 105.7 65.1 13-Mar-11 67.15 41.14 108.28 62.01 15-Mar-11 82.45 58.28 140.73 58.59 17-Mar-11 65.96 50.72 116.68 56.53 19-Mar-11 74.88 47.33 122.21 61.27 21-Mar-11 67.78 53.88 121.66 55.71 23-Mar-11 62.58 57.4 119.98 52.16 25-Mar-11 62.19 49.39 111.58 55.74 27-Mar-11 62.4 54.13 116.54 53.55 02-Apr-11 37.18 47.95 85.13 43.68 04-Apr-11 49.56 57.16 106.72 46.44 06-Apr-11 46.39 49.26 95.65 48.5 08-Apr-11 40.21 51.1 91.31 44.04 10-Apr-11 48.46 56.5 104.96 46.17 16-Apr-11 54.01 45.72 99.73 54.16 18-Apr-11 35.43 62.31 97.74 36.25 20-Apr-11 46.39 45.37 91.76 50.55 22-Apr-11 55.97 56.17 112.14 49.91 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 86 Table 4.1 – Continued from previous page Date PM2.5 PM10−2.5 PM10 %(PM2.5/PM10) 24-Apr-11 51.56 34.83 86.39 59.68 28-Apr-11 45.22 40.63 85.85 52.67 30-Apr-11 65.22 50.25 115.48 56.48 Average 88.62 138.31 N 145 145 145 145 Min 35.43 23.03 76.83 35.99 Max 349.68 164.66 448.67 86.93 Median 58.57 43.93 107.41 58.59 Figure 4.1: Time Series Plot of Particulate Mass Concentrations (µg/cm3) for the Entire Sampling Period Figure 4.1, figure 4.2 and figure 4.3 show the particulate mass concentration variations observed daily, monthly and the percentage ratio of PM2.5/PM10 University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 87 Figure 4.2: Average Monthly Particulate Mass Concentrations (µg/cm3) for the Entire Sampling Period Figure 4.3: Percentage Ratio of PM2.5/PM10 for the Entire Sam- pling Period respectively. The highest PM mass concentrations were observed in the months of December to February. This period is charactised by northeast trade winds which blow large amounts of dust from the Sahara desert, laden with emissions from dry season bush fires, towards the Gulf of Guinea in a south-westerly direction at an altitude of about 1500 m above sea University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 88 level (Afeti and Resch, 2000); that is harmattan (Sunnu, 2006). Moreover harmattan is period where there is less or no rainfall (Adefolalu, 1984), (Adetunji et al., 1979). It could be one of the reason for having elevated or very high particulate mass concentrations. The ratio of PM2.5/PM10 is crucial and important to identify emission sources of PM. High ratio of PM2.5/PM10 shows that the majority contributions are accumulated by fine particle. The results of PM2.5/PM10 recorded show that the dominance of fine fractions at the sampling area, Agbogbloshie, could be attributed to smoke from the e-waste burning and other forms of burning (open burning of refuse, domestic waste and commercial waste). Environmental Protection Agency, Ghana has a standard limit for PM10; but, has currently no standard limit for PM2.5. However, the USEPA standard is generally adopted for PM2.5. Table ??shows the Air Quality Guidelines (AQG) for some countries and organizations. Figure 4.4: Average Monthly Particulate Mass Concentrations University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 89 This graph 4.4 shows the seasonal dependence of particulate matter. The blue and red bar graph are showing average monthly mass concentration for both fine and coarse fractions over the entire sampling period however the yellow bar graph represents the monthly average rainfall data for the area over the entire sampling period. We can easily recognize the two rainfall seasons in the southern part of the country. One in June and one in september-October, whenever the rainfall peaks that is where we have the least of particulate matter average mass concentration. In December to February which is the dry season. These are the areas where particu- late matter mass concentrations are very high. One will obviously see the influence of the seasonal on the particulate matter level. Figure 4.5: Annual Average Air quality standards/guideline for countries and organizations This graph 4.5 shows the comparison between the annual Average mass University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 90 concentration measured in this work and that of national and international air quality standards/guideline. In this work, APM annual Average mass concentration obtained at Agbogbloshie are relatively higher than expected treshold values for both national and international (WHO, USEPA, EU etc...) standards and guidelines. 4.2 Influence of Meteorological Parameters on Particulate matter Meteorological factors such as temperature, relative humidity, rainfall and wind speed play an important role in particulate matter behavior in the atmosphere (El-Sharkawy and Zaki, 2015), (Habeebullah et al., 2015). The relationship between particulate matter mass concentration data and me- teorological factors observed in this work are presented in Table 4.3 Table 4.2: Correlation Between Particulate Matter Concentrations and Meteorological Parameters Particulars PM10 PM2.5 PM10−2.5 PM10 1 PM2.5 .977** 1 PM10−2.5 .727** .562** 1 Minimum Temperature 0.118 0.083 0.189* Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 91 Table 4.2 – Continued from previous page Particulars PM10 PM2.5 PM10−2.5 Maximum Temperature 0.352** .355** 0.221** Rainfall -0.086 -0.107 0.011 Relative Humidity -.175* -.209* -0.007 Wind Speed -.440** -.437** -.299** Table 4.3 shows the Pearsons correlation between meteorological factors and particulate matters. The outcome of the correlation calculation recorded are negative for rainfall, relative humidity and wind speed but only tem- peratures recorded positive correlation. Negative correlation between these factors (relative humidity and rainfall) and particulate matter is normal because it might be due to washout process as rain exhibits wet deposition effect on particulate matter. Rainfall removes atmospheric particulates and moisture restricts the possibility of resuspended soil particulate by making the soil humid (Leise et al., ),(Misra et al., 2008), so for that reason par- ticulate matter in the atmosphere will have less mass concentration. Wind speed also recorded negative correlation, it plays a major role in the trans- portation of the particulate matter from one point to another, wind speed affects the turbulence near the ground (Guerra et al., 2006). The greater the wind speed, the greater the dispersion of particulates, the greater the University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 92 dilution effects and transport of the particulate hence the lower the mass concentration(Mkoma and Mjemah, 2011),(Oren et al., 2001). Since the temperature recorded positive correlation, this must due to the reason that warm air is good for more atmospheric dispersion. 4.3 APM elemental determination 4.3.1 Validation process To detect the accuracy and reliability of the quantification method I have used in evaluate the concentrations. Quality control and quality assurance of method validation were applied using Standard Reference Material (SRM 2783) which is an air Particulate sample deposited on nuclepore filter just like the same filter I have in my analysis. I have used my quantification method to calculate the concentrations and and comparing them with the certified values, I realised that the accuracy range fall between 87-97%. This showed that my results are within the acceptable limit. Table 4.3: Validation of quantification method (SRM 2783) ngm−3 Element Measured Error Certified Error Measured/Certified Element Measured Error Certified Error Measured/Certified Na 1910 112.69 1860 100 1.03 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 93 Table 4.3 – Continued from previous page Element Measured Error Certified Error Measured/Certified Mg 8880 523.92 8620 520 1.03 Al 23667 591.68 23210 530 1.02 Si 59389 1722.28 58600 1600 1.01 S 863 215.75 1050 260 0.82 K 5479 487.63 5280 520 1.04 Ca 14794 1287.08 13200 1700 1.12 Ti 1676 284.92 1490 240 1.12 V 52.6 3.63 48.5 6 1.08 Cr 152.5 12.96 135 25 1.13 Mn 329.7 13.52 320 12 1.03 Fe 27498.7 1594.92 26500 1600 1.04 Ni 75.4 6.71 68 12 1.11 Cu 437.8 42.47 404 42 1.08 Zn 1897.8 113.87 1790 130 1.06 As 12.67 1.09 11.8 1.2 1.07 Ba 363.9 40.03 335 50 1.09 Pb 361.2 46.96 317 54 1.14 University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 94 4.3.2 Acquisition of the Spectrum Figure 4.6 shows the spectrum of nuclepore filter measured with PIXE, the peak profiles are shown clearly, the position of energy determine the elements in the filter while the areas under the curve determine the con- centration of the elements. The spectra were acquired simultaneously with silicon drift detectors (SSD1 and SSD2). Both detectors have different en- ergy resolutions, the detector (SSD1) was only used to detect elements with characteristic X-ray energies up to 7 keV but the second detector (SSD2) was used to detect elements with energies between 7 keV and 30 keV. Figure 4.6: Spectrum of a Nuclepore Filter Measured with PIXE University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 95 4.3.3 Elemental Concentration (ngm−3) of APM in both Fractions Elemental concentrations of the following elements: Na, Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Mo, Pd, Cd, Sn, Cs, Ba, Pt, Hg and Pb were determined in 145 samples analysed. The minimum, maximum, median, and the enrichment factors of all the 32 elements were calculated and tabulated as shown in tables 4.5 and 4.8. The five highest elemental concentration in PM102.5 are Si (261.33 to 59950.56 ngm−3) , Al (218.94 to 24719.25 ngm−3 ), and Fe (182.08 to 18982.45 ngm−3), Cl (587.76 to 15796.13 ngm−3), Na (418.25 to 15010.32 ngm−3); and for the fine fraction they occurred in the following order: Si (1339.93 to 61029.16 ngm−3), Al (888.74 to 25389.05 ngm−3), and Fe (769.28 to 19569.65 ngm−3),Cl (1533.36 to 16741.73 ngm−3), Na (1295.15 to 15887.22 ngm−3). High levels of Na and Cl are recorded in the samples because the sampling site is just about 4 km away from the Atlantic Ocean, hence it received a lot particles from sea spray. Iron, aluminum and silicon are naturally abundant in the soil. Table 4.4: Summary of Elemental Concentration (ngm−3) in PM10−2.5 Element Sample Median Min Max EF Element Sample Median Min Max EF Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 96 Table 4.4 – Continued from previous page Element Sample Median Min Max EF Na 145 2831.49 1295.15 15887.22 0.77-22.18 Mg 145 771.45 259.92 3857.14 0.26-4.22 Al 145 2498.2 888.74 25389.05 ******* Si 145 3354.95 1339.93 61029.16 0.06-6.00 P 145 95.25 54.35 1125.8 0.69-25.42 S 145 689.3 249.06 3058.79 19.26-363.49 Cl 145 5167.96 1533.36 16741.73 154.82-5138.80 K 145 577.75 226.31 7624.03 0.11-10.10 Ca 145 1040.36 278.07 12892.93 0.11-5.51 Ti 145 67.71 18.03 1135.01 0.02-6.08 V 145 4.86 0.88 60.84 0.12-12.58 Cr 145 10.4 2.49 67.68 0.28-22.51 Mn 145 26.28 3.27 451.84 0.08-6.22 Fe 145 1369.15 769.28 19569.65 0.19-5.13 Ni 145 5.48 3.15 23.93 0.55-10.04 Cu 145 5.66 3.61 32.82 0.85-20.46 Zn 145 44.17 15.65 229.12 5.37-72.16 As 145 46.69 6.97 1113.52 40.51-18574.37 Se 145 0.86 0.27 9.38 78.13-9784.90 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 97 Table 4.4 – Continued from previous page Element Sample Median Min Max EF Br 145 32.69 9.29 124.85 59.83-2643.35 Rb 145 5.61 4.66 16.67 0.19-6.49 Sr 145 3.86 1.09 29.37 0.05-1.50 Zr 145 3.8 1.72 20.18 0.09-3.51 Mo 145 39.58 28.54 99.48 154.50-2894.68 Pd 145 38.78 17.78 73.2 ********** Cd 145 120.74 14.65 788.35 1494.57-206127.04 Sn 145 1219.54 833.12 4018.75 2416.79-113494.33 Cs 145 163.98 30.75 915.7 188.17-12963.95 Ba 145 247.82 10.81 1455.11 0.95-158.17 Pt 145 185.02 18.09 1040.99 *********** Hg 145 2.59 1.79 13.59 92.75-4297.64 Pb 145 4.91 1.45 117.57 1.83-238.53 Table 4.5: Summary of Elemental Concentration (ngm−3) in PM2.5 Element Sample Median Min Max EF Element Sample Median Min Max EF Na 145 2831.49 1295.15 15887.22 0.77-22.18 Mg 145 771.45 259.92 3857.14 0.26-4.22 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 98 Table 4.5 – Continued from previous page Element Sample Median Min Max EF Al 145 2498.2 888.74 25389.05 ******* Si 145 3354.95 1339.93 61029.16 0.06-6.00 P 145 95.25 54.35 1125.8 0.69-25.42 S 145 689.3 249.06 3058.79 19.26-363.49 Cl 145 5167.96 1533.36 16741.73 154.82-5138.80 K 145 577.75 226.31 7624.03 0.11-10.10 Ca 145 1040.36 278.07 12892.93 0.11-5.51 Ti 145 67.71 18.03 1135.01 0.02-6.08 V 145 4.86 0.88 60.84 0.12-12.58 Cr 145 10.4 2.49 67.68 0.28-22.51 Mn 145 26.28 3.27 451.84 0.08-6.22 Fe 145 1369.15 769.28 19569.65 0.19-5.13 Ni 145 5.48 3.15 23.93 0.55-10.04 Cu 145 5.66 3.61 32.82 0.85-20.46 Zn 145 44.17 15.65 229.12 5.37-72.16 As 145 46.69 6.97 1113.52 40.51-18574.37 Se 145 0.86 0.27 9.38 78.13-9784.90 Br 145 32.69 9.29 124.85 59.83-2643.35 Rb 145 5.61 4.66 16.67 0.19-6.49 Continued on next page University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 99 Table 4.5 – Continued from previous page Element Sample Median Min Max EF Sr 145 3.86 1.09 29.37 0.05-1.50 Zr 145 3.8 1.72 20.18 0.09-3.51 Mo 145 39.58 28.54 99.48 154.50-2894.68 Pd 145 38.78 17.78 73.2 ********** Cd 145 120.74 14.65 788.35 1494.57-206127.04 Sn 145 1219.54 833.12 4018.75 2416.79-113494.33 Cs 145 163.98 30.75 915.7 188.17-12963.95 Ba 145 247.82 10.81 1455.11 0.95-158.17 Pt 145 185.02 18.09 1040.99 *********** Hg 145 2.59 1.79 13.59 92.75-4297.64 Pb 145 4.91 1.45 117.57 1.83-238.53 4.4 Enrichment Factor (E.F) To differentiate between the elements originating from human activities and those from natural processes and to assess the degree anthropogenic inuence E.F. was used. Elements with E.F approximately to 1 have strong natural component. Elements with high E.F could have anthropogenic origin or other natural sources such as marine aerosol. The contamination levels were University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 100 calculated by the normalization of metal concentration with respect to the concentration of a reference element. Silicon (Si) was used as a reference element because of its abundance and also being a major constituent of clay minerals and it is less affected by anthropogenic influence (Balls et al., 1997), (Ryan and Windom, 1988), (Sinex and Wright, 1988). Figure 4.7: Enrichment Factors Enrichment Factor evaluation was carried for both PM2.5 and PM10−2.5 to ascertain whether the contamination is from natural or anthropogenic sources. By the rule of the enrichment factor evaluation, EF <1 is consid- ered as natural sources whiles EF ≥ 1 is regarded as man-made or anthro- pogenic sources(Braga et al., 2005) , (Chimidza et al., 2001) , (Kothai et al., 2011). The standard and approved equation 3.9 for the calculation EF was used. The results of EF for both PM2.5 and PM10−2.5 were tabulated as shown in figure 4.7. The enrichment factor evaluation classifies pollution University of Ghana http://ugspace.ug.edu.gh Chapter 4. Results and Discussions 101 sources as either natural or anthropogenic, some elements analyzed at the sampling site having storng natural component (Cadle et al., 1999), (Garg et al., 2000), (Schauer et al., 2006). However, those elements having strong anthropogenic pollution are known elements associted with electrical and electronic products (Tonetti, 2000). 4.5 Source Apportionment of APM The USEPA Positive Matrix Factorization (PMF) version 5.0 modeling software was used in the source identification and apportionment of APM. In both PM2.5 and PM10−2.5, PMF resolved five sources for each fractions. The change in individual elements signal-to-noise ratio(S/N) can give differ- ent meaning to the model. S/N provides some indications about whether the measured concentrations are the actual concentrations or within the measurement noise level. Any calculated S/N which fall within the interval 0.2