Numerical Methods in Environmental Data Analysis

242 Pages·2022·8.459 MB·English

by  Moses Eterigho Emetere

Numerical Methods in
Environmental Data
Analysis
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Numerical Methods in
Environmental Data
Analysis
Moses Eterigho Emetere
Department of Mechanical Engineering Science,
University of Johannesburg, South Africa
Department of Physics, Covenant University, Ota, Ogun, Nigeria
Elsevier
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Contents
Preface..................................................................................................ix
CHAPTER 1 Overview on data treatment.....................................1
1 Introduction........................................................................1
1.1 Mathematicaltechnique...................................................6
1.2 Computational technique..................................................7
1.3 Statistical data treatment..................................................7
References.............................................................................11
CHAPTER 2 Case study in environmental pollution research......13
1 Introduction.......................................................................13
1.1 Air pollution................................................................14
1.2 Land pollution..............................................................21
1.3 Waterpollution.............................................................24
1.4 Noise pollution.............................................................32
1.5 Radioactivepollution.....................................................33
1.6 Electronic waste pollution..............................................35
References.............................................................................38
Further reading.......................................................................39
CHAPTER 3 Typical environmental challenges...........................41
1 Introduction.......................................................................41
1.1 Thermal comfortasa source ofenvironmental concern.......41
1.2 Rainfallas asource ofenvironmentalconcern...................44
1.3 Recent environmental crisisand the problem ofclimate
change........................................................................47
References.............................................................................51
CHAPTER 4 Generating environmental data: Progress and
shortcoming...........................................................53
1 Method ofgenerating environmental data: common
challenges, safety,and errors.................................................53
1.1 Data quality and errors..................................................55
1.2 Satellite measurement....................................................60
1.3 Modelingprocedure......................................................63
1.4 Experimental procedure.................................................69
2 Common errors in laboratory practice.....................................74
3 Maintaining laboratory apparatus...........................................75
References.............................................................................76
v
vi Contents
CHAPTER 5 Root finding technique in environmental research...79
1 Application of root finding technique toenvironmental
data..................................................................................79
1.1 The root finding method.................................................79
1.2 Modification of the root finding method todata
application...................................................................82
1.3 Computational application of root finding method to
data application..........................................................103
Reference............................................................................117
CHAPTER 6 Numerical differential analysis in environmental
research..............................................................119
1 Introduction.....................................................................119
1.1 Euler method.............................................................121
1.2 Improved Euler method...............................................122
1.3 RungeeKuttamethod..................................................123
1.4 PredictorCorrector method...........................................126
1.5 Midpointmethod........................................................128
1.6 Application of numerical methods ofsolving
differentiationin environmental research.........................128
1.7 Computational processing of numerical methods
for solvingdifferential equation.....................................136
1.8 Computational application of derivativesto
environmentaldata......................................................142
1.9 Case 1:derivativeofexperimental data...........................142
References...........................................................................147
Further reading.....................................................................148
CHAPTER 7 Numerical integration application to
environmental data..............................................149
1 Introduction.....................................................................149
1.1 Midpoint...................................................................149
1.2 Trapezoidal rule..........................................................151
1.3 Simpson’srule............................................................154
1.4 Computational application of numerical integration..........158
References...........................................................................168
CHAPTER 8 Numerical interpolation in environmental
research..............................................................169
1 Introduction.....................................................................169
2 Application ofinterpolation toenvironmental data..................170
3 Lagrange interpolation.......................................................172
Contents vii
4 Newton interpolation.........................................................176
5 Spline interpolation...........................................................179
6 Computational application ofinterpolation............................181
References...........................................................................189
CHAPTER 9 Environmental/atmospheric numerical models
formulations: model review..................................191
1 Introduction.....................................................................191
1.1 Global forecast system...............................................191
1.2 NOGAPS-ALPHA model...........................................192
1.3 Global Environmental Multiscale Model (GEM).............195
1.4 European Centerfor Medium Range Weather Forecasts...196
1.5 UnifiedModel (UKMO).............................................197
1.6 French global atmospheric forecastmodel(ARPEGE).....199
1.7 Weather Research and Forecasting (WRF).....................200
1.8 Japan Meteorological AgencyNonhydrostaticModel
(JMA-NHM)............................................................203
1.9 Thefifth generation mesoscale model...........................205
1.10 Advanced RegionPrediction System (ARPS).................206
1.11 High Resolution Limited Area Model (HIRLAM)...........207
1.12 Global Environmental Multiscale limitedarea model......208
1.13 ALADIN model........................................................210
1.14 Eta model................................................................213
1.15 Microscale model(MIMO).........................................215
1.16 Regionalatmospheric modelingsystem (RAMS)............216
References...........................................................................217
Further reading.....................................................................221
Index...................................................................................................223
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Preface
Environmental data may be described in terms of quantitative, qualitative, or
geographically referenced facts that represent the state of the environment and its
changes. Quantitative environmental data consist of data, statistics and indicators
of databases, spreadsheets, compendia, and yearbook type products. Qualitative
environment data are descriptions (e.g., textual, pictorial) of the environment or
its constituent parts that cannot be adequately represented by accurate quantitative
orgeographicallyreferenceddescriptors.Geographicallyreferencedenvironmental
data are described in digital maps, satellite imagery, and other sources linked to a
location or map feature. Summarily, it can be postulated that dataset in environ-
mental studies is like blood to the human body. All decisions in environmental
studiesarebasedonobservablesthataremeasurable,reliable,realistic,andconsis-
tentwiththeories.Environmentaltheoriesareformulatedfromobservables.Hence,
a faulty observable can lead to a colossal failure in processes, prediction, model
formulation, and decision.
Theinevitableoutcomesofclimatechangehaveredefinedobservablessuchthat
newtheoriesandmodelsarenecessaryduetodatainconsistency,noise,andspikes.
Asidefromjustgettingdatasetandsimulating,itisnowexpedientthattheintegrity
ofadatasetbethefirstlineofoperationindataanalytics.Thisfeatcanbeachieved
through the guidance of proven theories. The knowledge of this theory, when to
apply it on a dataset, how to apply it, and ways to validate emerging results are
salient in any field of environmental sciences. Hence, the focus of this book is to
educate beginnersandprofessionals on the above.
Environmental indicatorsare usually the environment statistics thatare inneed
offurtherprocessingandinterpretation.Basedonthis,thereistheneedoftheappli-
cation of numerical methods to validate, expatiate, predict, back-trace, and create
new possibilities. Validation technique through numerical methods enables the
researcher to ascertain the pattern trend of series of observables and tie them to
certain established theories. Expatiation technique through numerical methods
enables the researcher to take an informed numerical guess to replace missing
data, noise, and data anomalies. Missing data is common in atmospheric research.
Missing data makes the genuity of the data to be questionable especially when
theuserisabeginnerornovice.Assumeifthesatellitemeasurementofaparameter
showsmissingvaluesfor7monthsinayearlydataset.Ignoringthemissingdatafor
theremaining5monthswouldcertainlybeerroneoustoanalyzemonthlyorseason-
ally.Thesamescenarioappliestonoiseindataanddataanomalies.Thisbookseeks
totrain beginnersandprofessionals on the aforementioned expertise.
ix