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<p class="MsoNormal" style="margin-bottom:.0001pt;text-align:justify;line-height:normal;border:none;padding:0cm;"><span lang="en-gb" style="font-size:10pt;font-family:'Times New Roman', serif;" xml:lang="en-gb">Air pollution is one
of the fundamental environmental problems of the industrialized world due to
its adverse effects on all organisms. Several institutions warn that there
exist serious air pollution in many regions of the world. When all devastating
effects of air pollutants considered, it is crucial to create valid models to
predict air pollution levels in order to determine future concentrations or to
locate pollutant sources. These models may provide policy implications for
governments and central authorities in order to prevent the excessive pollution
levels. Though there are a number of attempts to model pollution levels in the
literature, recent advances in deep learning techniques are promising more
accurate prediction results along with integration of more data.  In this study, a detailed research about
modelling with deep learning architectures on real air pollution data is given.
With the help of this research we attempt to develop air pollution
architectures with deep learning in future and enhance the results further with
insights from recent advances of deep learning research such as Generative
Adversarial Networks (GANs), where two competing networks are working against
each other, one for creating a more realistic data and the other one to predict
the state.   </span></p><p></p>

</div>

Air Pollution Modelling with Deep Learning: A Review

555329

English

Deep learning

air pollution estimation

artificial neural networks

generative adversarial model

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<p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
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mso-border-bottom-alt:double windowtext 1.5pt;padding:0cm;mso-padding-alt:1.0pt 0cm 1.0pt 0cm"><span lang="EN-GB" style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;
mso-fareast-font-family:Calibri;mso-ansi-language:EN-GB">This study covers the
investigation of heavy metal pollution in Karadere Stream which is located in
Trabzon, East-Black Sea Basin. 10 heavy metal parameters (Pb, Zn, Cr, Fe, Cu,
Ni, Al, As, B and Ba) were analysed on water samples taken from two stations on
Karadere Stream every month, covering the period of February 2017 - July 2017.
The results of heavy metal analysis were evaluated according to maximum values
given in Water Quality Regulation. The results of the analysis showed that Fe,
Cu and Al values were exceeded the limit values for both station. The heavy
metal pollution index model was found useful to assess the overall pollution
level with respect to heavy metals and the values were found above the critical
pollution index value of 100 for both station.<b><o:p></o:p></b></span></p>

</div>

Investigation of Heavy Metal Pollution in Karadere Stream (Trabzon, Turkey) By Using Heavy Metal Pollution Index Model

555339

Heavy metal

Index Model

Karadere Stream

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<p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify;line-height:normal;border:none;mso-border-top-alt:double windowtext 1.5pt;
mso-border-bottom-alt:double windowtext 1.5pt;padding:0cm;mso-padding-alt:1.0pt 0cm 1.0pt 0cm"><span lang="EN-GB" style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;
mso-fareast-font-family:Calibri;mso-ansi-language:EN-GB">This study covers the
investigation of Polycyclic Aromatic Hydrocarbons (PAHs) pollution in Pazarsuyu
Stream which is located in East-Black Sea Basin. 14 PAHs analyzes were
performed on water samples taken from a station on the Pazarsuyu Stream every
month, covering the period of February 2017 - December 2017. The results of PAH
analysis were evaluated according to annual average and maximum values of PAH
parameters given in Water Quality Regulation. The results of the analysis
showed that PAHs values change periodically, especially the amount of PAH
increases in rainy periods. According to the results, it was seen that 5 PAH
parameters (benzo (b) floranten, benzo (k) floranten, benzo (a) piren, benzo
(g,h,i) perilen ve indeno piren) among 14 PAH parameters, exceeded the annual
mean values given in Water Quality Regulation.<b><o:p></o:p></b></span></p>

</div>

Investigation of Polycyclic Aromatic Hydrocarbons (PAH) Pollution in Pazarsuyu Stream (Giresun, Turkey)

555342

Chromatography

Pazarsuyu Stream

Water Quality

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<p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify;line-height:normal;border:none;mso-border-top-alt:double windowtext 1.5pt;
mso-border-bottom-alt:double windowtext 1.5pt;padding:0cm;mso-padding-alt:1.0pt 0cm 1.0pt 0cm"><span lang="EN-GB" style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;
mso-fareast-font-family:Calibri;mso-ansi-language:EN-GB">Nanotechnology is considered
the future of the world in most physics and chemical solutions that cannot be
applied in many scale level. This review aimed to highlight the different uses
of nanotechnology in industrial waste water treatment system because it is very
important issue to protect the environment from the different liquid industrial
pollutants. Nanoparticles is deﬁned by some as nanomaterials, and these
materials has unusual properties not present in ordinary materials. Nano,
typically employed as a preﬁx, is deﬁned as one billionth of a quantity or term
that is represented mathematically 10 <sup>9-</sup>. Generally, refers to the
processes that produces and use matter at the nanometre level. From the review
Nano-technology can be used to minimize the cost, accelerate the process and
improve the efficiency of industrial waste water treatment. Nanoparticles found
to be one of the best solution in the field of industrial waste water
treatment.&nbsp;<o:p></o:p></span></p>

</div>

Selected Nanotechnology Applications in Industrial Waste Water Treatment: A Review

555345

Nanotechnology

Nanoparticles

Industrial

Environment

Advanced Treatment

Waste

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<p class="MsoNormal" style="margin-bottom:0cm;margin-bottom:.0001pt;text-align:
justify;line-height:normal;border:none;mso-border-top-alt:double windowtext 1.5pt;
mso-border-bottom-alt:double windowtext 1.5pt;padding:0cm;mso-padding-alt:1.0pt 0cm 1.0pt 0cm"><span lang="EN-GB" style="font-size:10.0pt;font-family:&quot;Times New Roman&quot;,serif;
mso-ansi-language:EN-GB">With advancing and changing technology in the world,
many progress has been achieved in many areas, a progress which is known as
industry 4.0, in which the Internet and communication are involved. In addition
to the benefits of technology, it also affects the sustainability of many
reasons, such as the limitations of natural resources, population growth,
ecologically produced waste, cost and the inability of people to keep up with
technology. The sustainability of local products necessitated the promotion of
gastronomic sustainability. In our work, this fast change from the gastronomic
point of view is taking place from 1 to 4.<o:p></o:p></span></p>

</div>

Volume 1 Issue 3

Air Pollution Modelling with Deep Learning: A Review

Investigation of Heavy Metal Pollution in Karadere Stream (Trabzon, Turkey) By Using Heavy Metal Pollution Index Model

Investigation of Polycyclic Aromatic Hydrocarbons (PAH) Pollution in Pazarsuyu Stream (Giresun, Turkey)

Selected Nanotechnology Applications in Industrial Waste Water Treatment: A Review

Gastronomy Four Zero (4.0)

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