Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region

Okoro Goodluck; Umeuduji Joel Ekwutosi

doi:doi:10.11648/j.bio.20221004.13

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Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region

Okoro Goodluck, Umeuduji Joel Ekwutosi

Published in American Journal of Bioscience and Bioengineering (Volume 10, Issue 4)

Received: 10 June 2022 Accepted: 7 July 2022 Published: 24 August 2022

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Abstract

Air pollution is a very complex problem in the oil-bearing region of Niger Delta and poses multiple challenges in management and mitigation. This study is aimed at providing air quality monitoring plan and adaptation strategies for the residents of three oil bearing communities. Air quality data of the area was collected from NASA after a field study to determine the coordinates of the study area. After downloading the data, the latitude, longitude, and concentration data had to be extracted from the set of data using a computer high level language called python. The data formats gotten in this research were netcdf4, he5, and hdf. The techniques of analysis used were descriptive in the form of tables and trend analysis. Furthermore, geospatial analysis was carried out to note the spatio-temporal hot and cold spots in the area. The hot spot analysis tool calculates the Getis-Ord statistic for each geo-referenced household. This tool identified those households with either high or low attribute values that cluster spatially. In conclusion, wind and human activities greatly influence the pollutants distribution in the study area. Across these communities, Mgbede community is the cold spot region in both seasons while Ebocha is a hot spot region during the dry season and Okwuzi a hot spot region during the rainy season. Again, the concentration of HCHO increases across the three communities during the wet season, while that of PM_2.5 decreases in the three communities during the wet season. In a related development, the concentration of SO₂ across the three communities decreases during the dry season. Thus, this study proposed monitoring and adaptation plan to atmospheric pollution in the study area by the residents.

Published in	American Journal of Bioscience and Bioengineering (Volume 10, Issue 4)
DOI	10.11648/j.bio.20221004.13
Page(s)	89-104
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Air Pollution, Hot Spot, Cold Spot, Mitigation, Adaptation

References

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[4]	Adoki, A. (2012). Air Quality survey of some locations in the Niger Delta, Area. Journal of Appl. Sci. & Environ. Management, Vol. 16 (1) 125-134.
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[6]	Deguen, S., S´ egala, C., Ga¨ elle P´edrono, and Mesbah, M. (2012) A New Air Quality Perception Scale for Global Assessment of Air Pollution Health Effects. Risk Analysis, (32) (12) 14-27.
[7]	Ebitare, B. S and Okodudu,. S. (2021) ‘Forced Migration in the Niger Delta Region of Nigeria: Interrogating the role of oil induced Environmental Degradation’ International Journal of Innovative Psychology & Social Development 9 (3) pp 30-42.
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[12]	Nychka,. D. and Saltzman,. N. (1998) “Design of Air quality Monitoring Networks”. In: Nychaka, D., Piegorsch, W. W., Cox, L. H (eds) Case Studies in Environmental Statistics. Lecture notes in Statistics Vol. 132 Springer, New York.
[13]	Ogunba, A. (2016). An appraisal of the evolution of environmental legislation in Nigeria. Vermont Law Review (40) pp. 674-693.
[14]	Okoro, G. (2004) “The impact of gas flaring in soil pH”. Unpublished. B.Sc. Thesis submitted to Department of Geography & Envi. Mgt., University of Port Harcourt, Nigeria.
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APA Style

Okoro Goodluck, Umeuduji Joel Ekwutosi. (2022). Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. American Journal of Bioscience and Bioengineering, 10(4), 89-104. https://doi.org/10.11648/j.bio.20221004.13

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ACS Style

Okoro Goodluck; Umeuduji Joel Ekwutosi. Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. Am. J. BioSci. Bioeng. 2022, 10(4), 89-104. doi: 10.11648/j.bio.20221004.13

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AMA Style

Okoro Goodluck, Umeuduji Joel Ekwutosi. Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. Am J BioSci Bioeng. 2022;10(4):89-104. doi: 10.11648/j.bio.20221004.13

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@article{10.11648/j.bio.20221004.13,
  author = {Okoro Goodluck and Umeuduji Joel Ekwutosi},
  title = {Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region},
  journal = {American Journal of Bioscience and Bioengineering},
  volume = {10},
  number = {4},
  pages = {89-104},
  doi = {10.11648/j.bio.20221004.13},
  url = {https://doi.org/10.11648/j.bio.20221004.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20221004.13},
  abstract = {Air pollution is a very complex problem in the oil-bearing region of Niger Delta and poses multiple challenges in management and mitigation. This study is aimed at providing air quality monitoring plan and adaptation strategies for the residents of three oil bearing communities. Air quality data of the area was collected from NASA after a field study to determine the coordinates of the study area. After downloading the data, the latitude, longitude, and concentration data had to be extracted from the set of data using a computer high level language called python. The data formats gotten in this research were netcdf4, he5, and hdf. The techniques of analysis used were descriptive in the form of tables and trend analysis. Furthermore, geospatial analysis was carried out to note the spatio-temporal hot and cold spots in the area. The hot spot analysis tool calculates the Getis-Ord statistic for each geo-referenced household. This tool identified those households with either high or low attribute values that cluster spatially. In conclusion, wind and human activities greatly influence the pollutants distribution in the study area. Across these communities, Mgbede community is the cold spot region in both seasons while Ebocha is a hot spot region during the dry season and Okwuzi a hot spot region during the rainy season. Again, the concentration of HCHO increases across the three communities during the wet season, while that of PM2.5 decreases in the three communities during the wet season. In a related development, the concentration of SO2 across the three communities decreases during the dry season. Thus, this study proposed monitoring and adaptation plan to atmospheric pollution in the study area by the residents.},
 year = {2022}
}

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TY - JOUR
T1 - Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region
AU - Okoro Goodluck
AU - Umeuduji Joel Ekwutosi
Y1 - 2022/08/24
PY - 2022
N1 - https://doi.org/10.11648/j.bio.20221004.13
DO - 10.11648/j.bio.20221004.13
T2 - American Journal of Bioscience and Bioengineering
JF - American Journal of Bioscience and Bioengineering
JO - American Journal of Bioscience and Bioengineering
SP - 89
EP - 104
PB - Science Publishing Group
SN - 2328-5893
UR - https://doi.org/10.11648/j.bio.20221004.13
AB - Air pollution is a very complex problem in the oil-bearing region of Niger Delta and poses multiple challenges in management and mitigation. This study is aimed at providing air quality monitoring plan and adaptation strategies for the residents of three oil bearing communities. Air quality data of the area was collected from NASA after a field study to determine the coordinates of the study area. After downloading the data, the latitude, longitude, and concentration data had to be extracted from the set of data using a computer high level language called python. The data formats gotten in this research were netcdf4, he5, and hdf. The techniques of analysis used were descriptive in the form of tables and trend analysis. Furthermore, geospatial analysis was carried out to note the spatio-temporal hot and cold spots in the area. The hot spot analysis tool calculates the Getis-Ord statistic for each geo-referenced household. This tool identified those households with either high or low attribute values that cluster spatially. In conclusion, wind and human activities greatly influence the pollutants distribution in the study area. Across these communities, Mgbede community is the cold spot region in both seasons while Ebocha is a hot spot region during the dry season and Okwuzi a hot spot region during the rainy season. Again, the concentration of HCHO increases across the three communities during the wet season, while that of PM2.5 decreases in the three communities during the wet season. In a related development, the concentration of SO2 across the three communities decreases during the dry season. Thus, this study proposed monitoring and adaptation plan to atmospheric pollution in the study area by the residents.
VL - 10
IS - 4
ER -

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Author Information

Okoro Goodluck

Department of Environmental Management, Nnamdi Azikiwe University, Awka, Nigeria
Umeuduji Joel Ekwutosi

Department of Geography & Environmental Management, University of Port Harcourt, Port Harcourt, Nigeria

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APA Style

Okoro Goodluck, Umeuduji Joel Ekwutosi. (2022). Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. American Journal of Bioscience and Bioengineering, 10(4), 89-104. https://doi.org/10.11648/j.bio.20221004.13

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ACS Style

Okoro Goodluck; Umeuduji Joel Ekwutosi. Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. Am. J. BioSci. Bioeng. 2022, 10(4), 89-104. doi: 10.11648/j.bio.20221004.13

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AMA Style

Okoro Goodluck, Umeuduji Joel Ekwutosi. Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region. Am J BioSci Bioeng. 2022;10(4):89-104. doi: 10.11648/j.bio.20221004.13

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@article{10.11648/j.bio.20221004.13,
  author = {Okoro Goodluck and Umeuduji Joel Ekwutosi},
  title = {Monitoring and Adaptation to Air Pollution: The Case of Oil Producing Communities in the Niger Delta Region},
  journal = {American Journal of Bioscience and Bioengineering},
  volume = {10},
  number = {4},
  pages = {89-104},
  doi = {10.11648/j.bio.20221004.13},
  url = {https://doi.org/10.11648/j.bio.20221004.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20221004.13},
  abstract = {Air pollution is a very complex problem in the oil-bearing region of Niger Delta and poses multiple challenges in management and mitigation. This study is aimed at providing air quality monitoring plan and adaptation strategies for the residents of three oil bearing communities. Air quality data of the area was collected from NASA after a field study to determine the coordinates of the study area. After downloading the data, the latitude, longitude, and concentration data had to be extracted from the set of data using a computer high level language called python. The data formats gotten in this research were netcdf4, he5, and hdf. The techniques of analysis used were descriptive in the form of tables and trend analysis. Furthermore, geospatial analysis was carried out to note the spatio-temporal hot and cold spots in the area. The hot spot analysis tool calculates the Getis-Ord statistic for each geo-referenced household. This tool identified those households with either high or low attribute values that cluster spatially. In conclusion, wind and human activities greatly influence the pollutants distribution in the study area. Across these communities, Mgbede community is the cold spot region in both seasons while Ebocha is a hot spot region during the dry season and Okwuzi a hot spot region during the rainy season. Again, the concentration of HCHO increases across the three communities during the wet season, while that of PM2.5 decreases in the three communities during the wet season. In a related development, the concentration of SO2 across the three communities decreases during the dry season. Thus, this study proposed monitoring and adaptation plan to atmospheric pollution in the study area by the residents.},
 year = {2022}
}

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