TORUS 3 - Toward an Open Resource Using Services E-Book
139,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
This book, presented in three volumes, examines �environmental� disciplines in relation to major players in contemporary science: Big Data, artificial intelligence and cloud computing. Today, there is a real sense of urgency regarding the evolution of computer technology, the ever-increasing volume of data, threats to our climate and the sustainable development of our planet. As such, we need to reduce technology just as much as we need to bridge the global socio-economic gap between the North and South; between universal free access to data (open data) and free software (open source). In this book, we pay particular attention to certain environmental subjects, in order to enrich our understanding of cloud computing. These subjects are: erosion; urban air pollution and atmospheric pollution in Southeast Asia; melting permafrost (causing the accelerated release of soil organic carbon in the atmosphere); alert systems of environmental hazards (such as forest fires, prospective modeling of socio-spatial practices and land use); and web fountains of geographical data. Finally, this book asks the question: in order to find a pattern in the data, how do we move from a traditional computing model-based world to pure mathematical research? After thorough examination of this topic, we conclude that this goal is both transdisciplinary and achievable.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 358
Veröffentlichungsjahr: 2020
Ähnliche
Table of Contents
Cover
Preface
1 Introduction to Environmental Management and Services
1.1. Introduction
1.2. Environmental components
1.3. Environmental pollution
1.4. Environmental quality management
1.5. Data services for the environment
1.6. References
PART: Environmental Case Studies
2 Air Quality Monitoring with Focus on Wireless Sensor Application and Data Management
2.1. Introduction
2.2. Development of air pollution monitoring techniques
2.3. Wireless sensor network for air monitoring
2.4. Summary: toward application of cloud computing for air quality monitoring data management
2.5. References
3 Emission Inventories for Air Pollutants and Greenhouse Gases with Emphasis on Data Management in the Cloud
3.1. Introduction
3.2. Methodology for development of EI database
3.3. Case studies
3.4. Summary and conclusion
3.5. References
4 Atmospheric Modeling with Focus on Management of Input/Output Data and Potential of Cloud Computing Applications
4.1. Introduction
4.2. Model architecture of chemistry transport model
4.3. Output data processing
4.4. Potential applications of cloud computing in atmospheric modeling
4.5. Case studies of air pollution modeling in Southeast Asia
4.6. Summary and conclusion
4.7. References
5 Particulate Matter Concentration Mapping from Satellite Imagery
5.1. Introduction
5.2. Relation of aerosol optical thickness, meteorological variables and particulate matter concentration
5.3. PM
2.5
mapping from moderate resolution satellite images
5.4. PM
10
mapping from high resolution satellite images
5.5. Conclusion
5.6. References
6 Comparison and Assessment of Culturable Airborne Microorganism Levels and Related Environmental Factors in Ho Chi Minh City, Vietnam
6.1. Introduction
6.2. Materials and methods
6.3. Results and discussions
6.4. Conclusion
6.5. References
7 Application of GIS and RS in Planning Environmental Protection Zones in Phu Loc District, Thua Thien Hue Province
7.1. Introduction
7.2. Materials and research methods
7.3. Research methods
7.4. Conclusion
7.5. References
8 Forecasting the Water Quality and the Capacity of the Dong Nai River to Receive Wastewater up to 2020
8.1. Introduction
8.2. Materials and methods
8.3. Results and discussion
8.4. Conclusion
8.5. Appendix
8.6. References
9 Water Resource Management
9.1. Introduction
9.2. Hydrological models for water resource management
9.3. Setting up of a SWAT model for the Blue Nile basin
9.4. Scenario analysis using SWAT
9.5. Cloud computing and SWAT
9.6. References
10 Assessing Impacts of Land Use Change and Climate Change on Water Resources in the La Vi Catchment, Binh Dinh Province
10.1. Introduction
10.2. Materials and methodology
10.3. Primary results
10.4. Conclusion
10.5. Acknowledgments
10.6. References
Conclusion and Future Prospects
List of Authors
Index
Summary of Volume 1
Summary of Volume 2
End User License Agreement
List of Tables
Chapter 2
Table 2.1. Advantages and disadvantages of active and passive monitoring methods...
Table 2.2. Several types of available sensing technologies for air pollution mon...
Chapter 3
Table 3.1. Summary of several EF compilations
Table 3.2. Available sources of activity data for emission inventory
Table 3.3. Existing global and regional emission inventory databases
Table 3.4. Summary of activity data from the considered emission sources in thre...
Table 3.5. EI results for base year in the Southeast Asia and the modeling domai...
Table 3.6. Major EI sources and data collection (Huy 2015)
Table 3.7. Annual emissions from different sources in Vietnam (Gg/year), 2010 (H...
Table 3.8. EI updated to 2016 for the BMR domain, Gg/year (Ha Chi 2018)
Table 3.9. Emission from the Kuan Kreng forest fires during 2003–2012
Table 3.10. Emission inventory for PNH, 2013 (GIZ 2015)
Chapter 4
Table 4.1. Commonly applied air quality dispersion models (modified from Kim Oan...
Table 4.2. USEPA (1991) recommended statistical measures for air quality model f...
Table 4.3. Evaluation criteria for model performance for PM air quality simulati...
Chapter 5
Table 5.1. Summary statistics of 24 h PM
10
in four patterns. Thailand NAAQS for ...
Table 5.2. Correlation coefficients between PM
10
and AOD (MODIS Aqua and Terra) ...
Table 5.3. MOD and MYD regression model results on filtered dataset using 4/(n-p...
Table 5.4. Results of universal Kriging cross-validation are considered separate...
Table 5.5. Overall validation of satellite-derived PM
2.5
maps over Phu Tho, Hano...
Table 5.6. Satellite-derived PM
2.5
is validated with ground-based PM
2.5
by stati...
Table 5.7. Two SPOT 4 images for air pollution mapping
Table 5.8. Correlation between satellite-derived aerosol and ground PM data in d...
Table 5.9. Least square fitting performance on PM estimation
Table 5.10. Correlation between derived PM model and ground PM
Chapter 6
Table 6.1. Summary of the characteristics of the studied areas. For a color vers...
Table 6.2. Sampling process in Ho Chi Minh City
Table 6.3. Descriptive statistics of environmental factors in Ho Chi Minh City
Table 6.4. Results of fungal identification
Table 6.5. Results of bacterial identification
Table 6.6. The paired t-tests used to compare the bacterial and fungi dataset fo...
Table 6.7. Correlation coefficients showing the effect of meteorological factors...
Table 6.8. Correlation coefficients showing the effect of meteorological factors...
Table 6.9. Linear regression models of the effects of environmental factors on t...
Table 6.10. Linear regression models of the effects of environmental factors on ...
Table 6.11. Principal component loading of bacteria
Table 6.12. Principal component loading of fungi
Chapter 7
Table 7.1. Area for environmental protection planning in Phu Loc District
Chapter 8
Table 8.1. Waste sources to the Dong Nai River
Table 8.2. Load of pollutants in wastewater in 2013
Table 8.3. Load of pollutants in wastewater to 2020 in scenarios (1, 2 and 3)
Chapter 9
Table 9.1. Data used for setting up the Blue Nile SWAT model
Chapter 10
Table 10.1. Soil types in the La Vi catchment (source: DONRE, Binh Dinh province...
Table 10.2. Land use type in the La Vi catchment, Binh Dinh province (source: DO...
Table 10.3. Sources and types of data collected for SWAT simulation
List of Illustrations
Preface
Figure P.1. At the beginnig of AI, Dartmouth Summer Research Project, 1956. Sour...
Figure P.2. From freehand potatoid to the cloud icon. The first figure is a sche...
Figure P.3. The heart of TORUS, partnership between Asia and Europe. For a color...
Chapter 1
Figure 1.1. Environmental components
Figure 1.2. The crust structure and its mineral composition (adapted from: Osman...
Figure 1.3. The distribution of Earth’s water (adapted from: Gleick 1993). For a...
Figure 1.4. The water cycle (source: USGS 2018). For a color version of this fig...
Figure 1.5. Interactions of environmental components (adapted from: Osman 2013)....
Figure 1.6. Air quality management framework (adapted from Kim Oanh and Polprase...
Figure 1.7. Framework for water quality (Huang 2007)
Figure 1.8. The global change of forest cover area during 1990–2010. Source: FAO...
Figure 1.9. The global area used for agriculture 1960–2014. Source: FAO (2016). ...
Chapter 2
Figure 2.1. High-Vol PM
10
sampler with an inserted PM
10
inlet (Pfeiffer 2005). F...
Figure 2.2. Beta attenuation monitor for PM (Gobeli et al. 2008)
Figure 2.3. Wireless sensor network architecture (Brienza et al. 2015). For a co...
Figure 2.4. Monitoring stations in Philadelphia and Washington DC (USEPA 2015)
Figure 2.5. Wireless sensor arrangement and sensor node with a solar panel (Loi ...
Figure 2.6. Calibration results for CO and PM
10
at different sites in Bangkok (L...
Figure 2.7. Field deployment of sensors for haze monitoring in Chiang Rai (Loi 2...
Figure 2.8. Sensor node: components and a sensor box with shelter. For a color v...
Figure 2.9. Use of sensor nodes for trial monitoring and publicizing data (Sathi...
Chapter 3
Figure 3.1. Framework of emission inventory (source: adapted from MoE NZ 2001 an...
Figure 3.2. Gridded (0.25°×0.25°) annual emissions for selected pollutants (BC a...
Figure 3.3. Emission share of different sources in Vietnam inland domain in 2010...
Figure 3.4. Spatial distributions of PM
2.5
and SO
2
emissions (tonnes/km
2
) from i...
Figure 3.5. Location and provincial boundaries of the BMR EI domain (Ha Chi 2018...
Figure 3.6. Study area and typical vegetation cover of swamp forest. For a color...
Figure 3.7. Satellite hot spots (a) and ground observation of forest fires (b). ...
Figure 3.8. Map of the EI domain of PNH
Chapter 4
Figure 4.1. General model structures of CTMs: (a) CHIMERE (www.lmd.polytechnique...
Figure 4.2. Framework of air quality dispersion model application with data flow...
Figure 4.3. The modeling domain, Vietnam (12 km grid size), Hanoi Metropolitan R...
Figure 4.4. Simulated hourly ground-level O
3
in the ERS domain, Vietnam (Danh et...
Figure 4.5. Monthly average of PM
2.5
for August and December in Vietnam by model...
Figure 4.6. Location and coverage of CENTHAI and the BMR domain
Figure 4.7. Monthly wind fields and spatial distribution of grid average PM
2.5
(...
Figure 4.8. Spatial distribution of monthly average PM
10
, PM
2.5
and BC in the se...
Chapter 5
Figure 5.1. Spatial–temporal window for extracting satellite and ground-based me...
Figure 5.2. Scatter plots between MOD04-, MYD04- and AERONET-AOT at seven AERONE...
Figure 5.3. Meteorological factor correlation with PM
1
, PM
2.5
and PM
10
at Phu Th...
Figure 5.4. MODIS aerosol data correlation with PM
1
, PM
2.5
and PM
10
at Phu Tho, ...
Figure 5.5. Ground PM stations. For a color version of this figure, see www.iste...
Figure 5.6. Estimated PM vs ground PM
Figure 5.7. PM map over Hanoi at a resolution of 60 meters. For a color version ...
Chapter 6
Figure 6.1. Average fungal levels in Ho Chi Minh City. NOTES.– (i) LN-Faculty of...
Figure 6.2. Average bacterial levels in Ho Chi Minh City
Figure 6.3. Plotting bacteria as PC loadings by three major PC components
Figure 6.4. Plotting fungi as PC loadings by three major PCs
Chapter 7
Figure 7.1. Phu Loc District, Thuan Thien Hue Province, Vietnam. For a color ver...
Figure 7.2. Constructing process of environmental protection planning map
Figure 7.3. Environmental protection planning map for Phu Loc District. For a co...
Chapter 8
Figure 8.1. Sites for MIKE 11 application. For a color version of this figure, s...
Chapter 9
Figure 9.1. Hydrological cycle processes
Figure 9.2. Initial processes in the SWAT model. For a color version of this fig...
Figure 9.3. Steps for setting up a SWAT model in QSWAT [CHA 18]
Figure 9.4. Output of step 2 in QSWAT. Delineate watershed. For a color version ...
Figure 9.5. Mass balance for the Blue Nile using SWAT Check. For a color version...
Chapter 10
Figure 10.1. Research site. For a color version of this figure, see www.iste.co....
Figure 10.2. Hydrology map in the La Vi catchment (source: DONRE, Binh Dinh prov...
Figure 10.3. Soil map in La Vi catchment (source: DONRE, Binh Dinh province). Fo...
Figure 10.4. Land use map in the La Vi catchment (source: DONRE, Binh Dinh provi...
Figure 10.5. Framework of the study
Figure 10.6. Automatic Hydrometeorology station 1 downstream the La Vi catchment
Figure 10.7. Automatic Hydro-meteorology station 2 downstream the La Vi catchmen...
Figure 10.8. Website for collecting observed data
Figure 10.9. Simulated water discharge at HM station 1 in the La Vi catchment. F...
Figure 10.10. Simulated water discharge at HM station 2 in the La Vi catchment. ...
Conclusion and Future Prospects
Figure C.1. The whiteboard at the time of cloud computing. D. Laffly and Q.H. Bu...
Guide
Cover
Table of Contents
Begin Reading
Pages
v
iii
iv
xi
xii
xiii
xiv
xv
xvi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
177
178
179
180
181
182
183
184
185
186
187
188
189
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
215
216
217
219
221
222
223
224
225
226
227
229
230
231
232
233
235
236
237
238
239
240
241
242
243
244
245
TORUS 3 – Toward an Open Resource Using Services
Cloud Computing for Environmental Data
Edited by
Dominique Laffly
First published 2020 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2020
The rights of Dominique Laffly to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2019955390
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-601-2
Preface: Why TORUS? Toward an Open Resource Using Services, or How to Bring Environmental Science Closer to Cloud Computing
Geography, Ecology, Urbanism, Geology and Climatology – in short, all environmental disciplines are inspired by the great paradigms of Science: they were first descriptive before evolving toward systemic and complexity. The methods followed the same evolution, from the inductive of the initial observations one approached the deductive of models of prediction based on learning. For example, the Bayesian is the preferred approach in this book (see Volume 1, Chapter 5), but random trees, neural networks, classifications and data reductions could all be developed. In the end, all the methods of artificial intelligence (IA) are ubiquitous today in the era of Big Data. We are not unaware, however, that, forged in Dartmouth in 1956 by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon, the term artificial intelligence is, after a long period of neglect at the heart of the future issues of the exploitation of massive data (just like the functional and logical languages that accompanied the theory: LISP, 1958, PROLOG, 1977 and SCALA, today – see Chapter 8).
All the environmental disciplines are confronted with this reality of massive data, with the rule of the 3+2Vs: Volume, Speed (from the French translation, “Vitesse”), Variety, Veracity, Value. Every five days – or even less – and only for the optical remote sensing data of the Sentinel 2a and 2b satellites, do we have a complete coverage of the Earth at a spatial resolution of 10 m for a dozen wavelengths. How do we integrate all this, how do we rethink the environmental disciplines where we must now consider at the pixel scale (10 m) an overall analysis of 510 million km2 or more than 5 billion pixels of which there are 1.53 billion for land only? And more important in fact, how do we validate automatic processes and accuracy of results?
Figure P.1.At the beginnig of AI, Dartmouth Summer Research Project, 1956. Source: http://www.oezratty.net/wordpress/2017/semantique-intelligence-artificielle/
Including social network data, Internet of Things (IoT) and archive data, for many topics such as Smart Cities, it is not surprising that environmental disciplines are interested in cloud computing.
Before understanding the technique (why this shape, why a cloud?), it would seem that to represent a node of connection of a network, we have, as of the last 50 years, drawn a potatoid freehand, which, drawn took the form of a cloud. Figure P.2 gives a perfect illustration on the left, while on the right we see that the cloud is now the norm (screenshot offered by a search engine in relation to the keywords: Internet and network).
What is cloud computing? Let us remember that, even before the term was dedicated to it, cloud computing was based on networks (see Chapter 4), the Internet and this is: “since the 50s when users accessed, from their terminals, applications running on central systems” (Wikipedia). The cloud, as we understand it today, has evolved considerably since the 2000s; it consists of the mutualization of remote computing resources to store data and use services dynamically – to understand software – dedicated via browser interfaces.
Figure P.2.From freehand potatoid to the cloud icon. The first figure is a schematic illustration of a distributed SFPS switch. For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
This answers the needs of the environmental sciences overwhelmed by the massive data flows: everything is stored in the cloud, everything is processed in the cloud, even the results expected by the end-users recover them according to their needs. It is no wonder that, one after the other, Google and NASA offered in December 2016 – mid-term of TORUS! – cloud-based solutions for the management and processing of satellite data: Google Earth Engine and NASA Earth Exchange.
But how do you do it? Why is it preferable – or not – for HPC (High Performance Computing) and GRIDS? How do we evaluate “Cloud & High Scalability Computing” versus “Grid & High-Performance Computing”? What are the costs? How do you transfer the applications commonly used by environmental science to the cloud? What is the added value for environmental sciences? In short, how does it work?
All these questions and more are at the heart of the TORUS program developed to learn from each other, understand each other and communicate with a common language mastered: geoscience, computer science and information science; and the geosciences between them; computer science and information sciences. TORUS is not a research program. It is an action that aims to bring together too (often) remote scientific communities, in order to bridge the gap that now separates contemporary computing from environmental disciplines for the most part. One evolving at speeds that cannot be followed by others, one that is greedy for data that others provide, one that can offer technical solutions to scientific questioning that is being developed by others and so on.
TORUS is also the result of multiple scientific collaborations initiated in 2008–2010: between the geographer and the computer scientist, between France and Vietnam with an increasing diversity of specialties involved (e.g. remote sensing and image processing, mathematics and statistics, optimization and modeling, erosion and geochemistry, temporal dynamics and social surveys) all within various scientific and university structures (universities, engineering schools, research institutes – IRD, SFRI and IAE Vietnam, central administrations: the Midi-Pyrénées region and Son La district, France–Vietnam partnership) and between research and higher education through national and international PhDs.
Naturally, I would like to say, the Erasmus+ capacity building program of the European Union appeared to be a solution adapted to our project:
“The objectives of the Capacity Building projects are: to support the modernization, accessibility and internationalization of higher education in partner countries; improve the quality, relevance and governance of higher education in partner countries; strengthen the capacity of higher education institutions in partner countries and in the EU, in terms of international cooperation and the process of permanent modernization in particular; and to help them open up to society at large and to the world of work in order to reinforce the interdisciplinary and transdisciplinary nature of higher education, to improve the employability of university graduates, to give the European higher education more visibility and attractiveness in the world, foster the reciprocal development of human resources, promote a better understanding between the peoples and cultures of the EU and partner countries.”1
In 2015, TORUS – funded to the tune of 1 million euros for three years – was part of the projects selected in a pool of more than 575 applications and only 120 retentions. The partnership brings together (Figure P.3) the University of Toulouse 2 Jean Jaurès (coordinator – FR), the International School of Information Processing Sciences (EISTI – FR), the University of Ferrara in Italy, the Vrije University of Brussels, the National University from Vietnam to Hanoi, Nong Lam University in Ho Chi Minh City and two Thai institutions: Pathumthani’s Asian Institute of Technology (AIT) and Walaikak University in Nakhon Si Thammarat.
Figure P.3.The heart of TORUS, partnership between Asia and Europe. For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
With an equal share between Europe and Asia, 30 researchers, teachers-researchers and engineers are involved in learning from each other during these three years, which will be punctuated by eight workshops between France, Vietnam, Italy, Thailand and Belgium. Finally, after the installation of the two servers in Asia (Asian Institute of Technology – Thailand; and Vietnam National University Hanoi – Vietnam), more than 400 cores will fight in unison with TORUS to bring cloud computing closer to environmental sciences. More than 400 computer hearts beat in unison for TORUS, as well as those of Nathalie, Astrid, Eleonora, Ann, Imeshi, Thanh, Sukhuma, Janitra, Kim, Daniel, Yannick, Florent, Peio, Alex, Lucca, Stefano, Hichem, Hung(s), Thuy, Huy, Le Quoc, Kim Loi, Agustian, Hong, Sothea, Tongchai, Stephane, Simone, Marco, Mario, Trinh, Thiet, Massimiliano, Nikolaos, Minh Tu, Vincent and Dominique.
To all of you, a big thank you.
Structure of the book
This book is divided into three volumes.
Volume 1 raises the problem of voluminous data in geosciences before presenting the main methods of analysis and computer solutions mobilized to meet them.
Volume 2 presents remote sensing, geographic information systems (GIS) and spatial data infrastructures (SDI) that are central to all disciplines that deal with geographic space.
Volume 3 is a collection of thematic application cases representative of the specificities of the teams involved in TORUS and which motivated their needs in terms of cloud computing.
Dominique LAFFLY
January 2020
1
http://www.agence-erasmus.fr/page/developpement-des-capacites
.
1Introduction to Environmental Management and Services
1.1. Introduction
Environmental management is an activity involved with social management that regulates human activities based on systematic access and information coordination skills for environmental issues. Environmental management is related to people, toward sustainable development and sustainable use of resources. In environmental management, some subjects should be integrated for optimal controlling and monitoring such as (1) environmental components, (2) environmental quality, (3) environmental pollution and (4) data services for the environment.
Environmental quality is a set of properties and characteristics of the environment, either generalized or local, as they impinge on human beings and other organisms. It is a measure of the condition of an environment relative to the requirements of one or more species and/or to any human need or purpose (Johnson et al. 1997). Environmental quality includes the natural environment as well as the built environment, such as air and water purity or pollution, noise and the potential effects which such characteristics may have on physical and mental health (EEA). In principle, environmental quality can be measured in terms of the value the people place on these non-waste receptor services or the willingness to pay.
Many parameters of the environment (air, water, soil and biome) should be collected to assess the environmental quality and propose standards for controlling the environmental quality. We can look at water quality parameters such as pH, T, color, turbidity, TSS, TDS, DO, COD, BOD, bacterial parameters; air quality parameters such as SO2, CO, NO2, O3, TSP, PM10, PM2.5, Pb; and soil quality parameters such as As, Cd, Pb, Cr, Cu, Zn, organic pollutants. The biome includes plants, animals and microorganisms; and can maintain and develop via ecological systems and nutrient cycles.
Human activities and natural occurrences, which release many kinds of pollutants, must be quantified and qualified through the environmental parameters. Over-release of pollutants into the environment causes the pollution which induces the environmental composition to break down and malfunction. However, nature has efficient functions to treat and overcome the stress of pollution by converting the pollutants into nutrients and energy. Therefore, understanding the functions of nature (the environment), people can monitor and control the released pollution against environmental quality parameters, and then issue the environmental standards for each activity and release.
Figure 1.1.Environmental components
1.2. Environmental components
Global environmental components can be generally divided into four main compositions: lithosphere, atmosphere, hydrosphere and biosphere. Each sphere interacts with the others to drive the dynamic change of the Earth. The description of each sphere and its interactions are summarized in the following subsections.
1.2.1. Lithosphere
Lithosphere refers to the rock and rigid layers of the Earth. The crust of the Earth covering the entire planet is around 5–50 km in depth (see also Figure 1.2(a)). In particular, in terms of the environment, the lithosphere is also known as soil layers (Bleam 2017). Soil composition is determined by its parent materials, as well as the weathering of rocks and sediments. Soil composition constrains the biological availability for living organisms. Common elements found in soil are mainly oxygen, silicon and aluminum (see Figure 1.2(b)). Most minerals are formed by two or more elements during the solidification of magma or lava and/or recrystallization by weathering.
Figure 1.2.The crust structure and its mineral composition (adapted from: Osman 2013). For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
1.2.2. Atmosphere
Atmosphere refers to the layer of air and gases surrounding the Earth. It consists of a mixture of the gases (by volume): nitrogen (78.084%), oxygen (20.9476%), water vapor (variable), carbon dioxide (0.0314%) inert gases such as argon (0.934%) and some other rare gases (Osman 2013). Atmospheric layers are divided into four layers depending on the temperature: troposphere, stratosphere, mesosphere and thermosphere. Among these layers, the troposphere is considered the most important and relevant to living organisms including humans. The layer has around 75% of the total mass of the atmosphere and most of the atmospheric elements are found here. Atmospheric composition plays an important role on the radiation budget to cool down or heat up the Earth’s surface (Schlager et al. 2012). The composition of gases in the atmosphere is slightly altered by chemical reactions.
1.2.3. Hydrosphere
The hydrosphere is the water portion of the Earth’s surface as it is distinguished from the solid part (in the lithosphere) and atmosphere (Glazovsky 2009). It includes the waters of oceans, seas, rivers, lakes, swamps and marshes, as well as soil moisture, underground water, water in the atmosphere and water in glaciers, ice and snow cover, as well as in all living organisms.
The distribution of water on the Earth’s surface is extremely uneven. Only 3% of water on the surface is fresh; the remaining 97% resides in the ocean. Of freshwater, 69% resides in glaciers, 30% underground and less than 1% is located in lakes, rivers and swamps. Another way of looking at this is that only 1% of the water on the Earth’s surface is usable by humans, and 99% of the usable quantity is situated underground (Figure 1.3).
Figure 1.3.The distribution of Earth’s water (adapted from: Gleick 1993). For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
Because Earth’s water is present in three states, it can get into a variety of environments. The movement of water around the Earth’s surface is the hydrologic (water) cycle (see Figure 1.4). Water changes from a liquid to a gas by evaporation by the sun to become water vapor. In surface water, only the water molecules evaporate; the salts remain in the ocean or freshwater reservoir. The water vapor remains in the atmosphere until it undergoes condensation to become droplets. The droplets gather in to clouds that are blown about the globe by wind. As the water droplets in the clouds collide and grow, they fall from the sky as precipitation. Precipitation can be rain, hail or snow. Sometimes precipitation falls back into the ocean and sometimes it falls onto the land surface.
Figure 1.4.The water cycle (source: USGS 2018). For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
1.2.4. Biosphere
The biosphere is the layer of the Earth including the hydrosphere, the lowermost part of the atmosphere, and a portion of the uppermost lithosphere (see Figure 1.5). Part of the Earth’s surface and atmosphere contains the entire terrestrial ecosystem and extends from the ocean. It contains all living organisms and the supporting media: soil, subsurface water, bodies of water and air. This sphere is also called the ecosphere.
Figure 1.5.Interactions of environmental components (adapted from: Osman 2013). For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
As a key component of earth systems, the biosphere interacts with and exchanges matter, including energy, with the other spheres. This helps to drive the global biogeochemical cycling of carbon, nitrogen, phosphorus, sulfur and other elements. The biosphere is the “global ecosystem”, comprising the totality of biodiversity on Earth and performing all manner of biological functions: photosynthesis and respiration, decomposition, nitrogen fixation and denitrification. Interaction of the biosphere is very dynamic, undergoing strong seasonal cycles in primary productivity and various biological processes driven by the energy captured by photosynthesis.
1.3. Environmental pollution
What is pollution? Pollution occurs when pollutants contaminate the natural surroundings, which brings about changes that adversely affect our normal lifestyles. Pollutants are the key elements or components of pollution, which can be any substance or form of energy. Pollution disturbs our ecosystem and the balance in the environment. With modernization and development in our lives, pollution has reached its peak, causing changes in the environment and human disease.
1.3.1. Air pollution
Air pollution affects our environment and human health. Pollution occurs in different forms: gas, liquid, solid, radioactive, noise, heat/thermal and light. Every form of pollution has two sources of occurrence: the point and the non-point sources. The point sources are easy to identify, monitor and control, whereas the non-point sources are hard to control.
Effects of pollution are (1) Environment Degradation: Environment is the first casualty for an increase in pollution, whether in air, water or land. It can lead to many other problems which may be local, regional or global problems such as global warming, ozone depletion, acid rain, eutrophication or the spread of toxic chemicals. Some examples are the emission of greenhouse gases, particularly CO2, that is leading to global warming, or chlorofluorocarbons (CFCs), which are a result of human activities, being released into the atmosphere, contributing to the depletion of the ozone layer. (2) Human Health: The decrease in air quality leads to several respiratory problems including asthma and lung cancer. Chest pain, congestion, throat inflammation, cardiovascular disease and respiratory disease are some of the diseases that can be caused by air pollution. Water pollution occurs due to the contamination of water and may pose skin-related problems including skin irritations and rashes. Similarly, solid waste or hazardous waste cause severe impacts on the living environments of animal species and humans.
1.3.2. Water pollution
In living activities, production and discharge create many different groups of pollutants. These substances can be collected and treated according to the regulations of each region and each country. However, the discharge of untreated waste into the environment is widespread, causing water pollution. Water contamination is caused by marine dumping, industrial waste, sewage (mainly from households) nuclear waste, oil pollution and underground storage leaks. Water pollution is also caused by natural factors such as water flowing through contaminated sites, which draws dissolved compounds into the water (iron, arsenic and some others).
Contaminated water is indicated by an increase in the concentration of inorganic and organic compounds in the water that exceeds the natural system’s ability to self-treat. Water pollution primarily affects the living organisms in water and other groups of organisms that are related to the aquatic environment, including humans. The ecosystem in the water environment is altered and reduces the self-cleaning function of the field environment. Water pollution can cause eutrophication, making ecological imbalances more serious.
Water pollution affects human activity, causing infectious diseases (E. coli, Salmonella), skin diseases, intestinal diseases and parasites and neurological diseases (Hg, Cr, As). The reduction of the quality and quantity of water used for production and consumption is also a major problem which is taking place in the world, especially in developing countries.
1.3.3. Soil pollution
Soil pollution occurs when the toxic chemicals, pollutants or contaminants in the soil are in high enough concentrations to be a risk to plants, wildlife, humans and, of course, the soil itself.
The main cause of soil pollution is the overuse of chemicals such as pesticides and fertilizers. These chemicals affect the activity of microorganisms in the soil environment, killing beneficial organisms, reducing soil fertility. In addition, environmental pollution of the soil can be caused by the leakage of radioactive compounds, waste tanks, water permeability through polluted soil, leakage from landfills, industrial waste disposal to the land environment, open toilets and underground burial sites which are near a river.
Soil pollution can have a number of harmful effects on ecosystems and human, plant and animal health. The harmful effects of soil pollution may come from direct contact with polluted soil or from contact with other resources such as water or food which has been grown on or come in direct contact with the polluted soil.
1.3.4. Biological pollution
Biological pollution comes from the invasion by and development of alien species in a certain space. Biological pollution affects the development of native species, causing serious ecological imbalance. Invasive species often adapt quickly and thrive under extreme conditions of the environment.
Biological pollutants can cause a decrease in the productivity of indigenous groups of organisms or the disappearance of a living organism in the short term.
Control of bio-pollution is still facing many difficulties, especially in the case of encroachment. At present, major biological pollution control is still preventive and minimizing, such as flushing the boat and cleaning the wheel before entering the regional or national boundary.
1.4. Environmental quality management
1.4.1. Air quality management: technical tools and data management
Pollutants released from various sources are present and mixed in the giant reactor of the atmosphere where multiple complex chemical and physical interactions occur. Pollutants can be directly emitted from the emission sources such as dust particles (primary pollutants) or formed in the atmosphere through chemical reactions such as sulfate and nitrate particles (secondary particulate matter) or ozone. WHO (2014) reported that globally, in 2012, around 7 million people died as a result of exposure to household (indoor) and ambient (outdoor) air pollution. Regionally, low- and middle-income countries in the Western Pacific and South East Asian regions bear most of the burden, with over 5 million deaths due to exposure to both household and ambient air pollution. Air pollution is now considered as the world’s largest single environmental health risk, and clean air is certainly a pressing need for sustainable development.
Air quality management (AQM) is the organized efforts to regulate the extent, duration and location of pollutant emissions to achieve ambient air quality standards, thereby minimizing the aesthetic, environmental and health risks. AQM is a dynamic process that can be illustrated as a cycle of inter-related elements to continuously improve air quality (see Figure 1.6), which comprises of the technical tools and policy actions. The technical tools of the AQM system include (1) air quality monitoring, (2) air pollution emission inventories and (3) air quality modeling. These tools generate and handle large air pollution databases which help us to understand the complex relationships between the source air pollution emissions and the multiple effects on human health, ecosystem and climate. The information provided by these technical tools can help to formulate policies to reduce the pollution emissions, thereby to (1) reduce ambient air pollutant concentrations to acceptable levels, (2) avoid adverse effects to human health and/or welfare and (3) avoid deleterious effects on animal and/or plant life and materials. The integrated AQM could also provide co-benefits in the reduction of climate radiative forcing through co-control measures of air pollution and greenhouse gases, as well as through the reduction of short-lived climate pollutants such as black carbon and tropospheric ozone (UNEP and WMO 2011).
Air pollution monitoring involves measurements of air quality by using traditional equipment (sampling and subsequent analysis) or using more advanced methods for in-situ monitoring with the aid of remote sensing (satellite) or on-line sensor systems. The monitoring results are used to compare against the ambient air quality standards (AAQSs) to assess the compliance. The monitoring equipment gives air pollution levels at a location during the measurement period; hence, a network is required to cover multiple sites to provide information on the status of air pollution in a geographical area. Monitoring should also be done over a long period to provide the pollution trend. The monitoring activity thus generates a large dataset: for example, in a small city with five monitoring stations, each measures six criteria pollutants (CO, PM2.5, PM10, O3, NO2, SO2) on an hourly basis; then, every year, a huge amount of data points are generated, which need good data management strategies for handling and processing.
Emission inventory (EI) is a systematic effort to obtain systematic information on the amount and types of air pollutants and greenhouse gases from emission sources in a geographic area during a specific period of time (USEPA 2007). EI is one of the fundamental components of the AQM process. EI provides input data for air quality model applications which helps to understand the roles of local and long-range transport pollutions in the ambient air quality in a city or a region. The information on spatial distributions of the emissions is useful for siting in air quality monitoring design. The EI database for modeling purposes should include the temporal (normally hourly) emissions of every interested species on every grid (normally 1x1 km for urban scale modeling) of the domain (a few hundred km in size); hence, it is a large dataset to be handled and analyzed. A much larger emission input dataset is required for regional or global scale air quality modeling.
Figure 1.6.Air quality management framework (adapted from Kim Oanh and Polprasert 2014). For a color version of this figure, see www.iste.co.uk/laffly/torus3.zip
Air quality dispersion models use a system of mathematical equations to provide the causal links between emission and ambient concentrations of pollutants. These equations describe the processes of pollutant diffusion/transport, transformation and deposition in relation to meteorology. This is an important tool for AQM which, together with EI and monitoring, provides information to develop and analyze AQM strategies. In particular, air quality modeling provides information on the causal link between emission (current, future scenarios) and ambient concentrations and the potential effects. Such information is required for the policy-making process in formulating and analyzing emission reduction strategies.
1.4.2. Water quality management
Water quality management is an essential action to control and monitor the water resources for human consumption and release. Plans can be conducted to ensure the water resources for industrial, agricultural and domestic activities are available. They include contingency plans, source water protection, the treatment process, the distribution system and continuous monitoring.
Executing the plans requires agreement from stakeholders. Plans for water management depend on both national and international sectors. Plans strictly follow the national strategy for reducing poverty, economic development and balancing the benefit of stakeholders.
Figure 1.7.Framework for water quality (Huang 2007)
1.4.3. Biosphere reservation and management
1.4.3.1. Forest in biosphere
Biosphere is the stratum of the Earth’s surface, covering a few kilometres, distance into the atmosphere to the furthest depths of the ocean (Ellatifi 2018). The biosphere is well known as a complex ecosystem composed of living organisms and the abiotic bodies that take energy and nutrients. Forests are the largest, and rich in ecosystems of the biosphere. Types of forest are classified differently depending on their species and environments. Using seasonality and latitudes, the forests are divided into three main different groups in the biosphere: tropical rainforests, boreal forests and temperate forests, including Mediterranean forests.
