Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment
<p dir="ltr">Armed conflicts pose severe environmental challenges, particularly in densely populated and infrastructure-limited regions. The Gaza Strip exemplifies such a context, where the intersection of warfare, urban density, and environmental vulnerability demands urgent scienti...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513541037359104 |
|---|---|
| author | Ammar Abulibdeh (15785928) |
| author_facet | Ammar Abulibdeh (15785928) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ammar Abulibdeh (15785928) |
| dc.date.none.fl_str_mv | 2025-08-13T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.gloenvcha.2025.103044 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Air_pollution_in_Gaza_during_the_post-october_7_era_a_satellite_and_machine_learning_assessment/30018715 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Earth sciences Atmospheric sciences Environmental sciences Environmental management Human society Development studies Information and computing sciences Machine learning Air pollution Machine learning forecasting Environmental monitoring Sentinel-5P Gaza Strip |
| dc.title.none.fl_str_mv | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Armed conflicts pose severe environmental challenges, particularly in densely populated and infrastructure-limited regions. The Gaza Strip exemplifies such a context, where the intersection of warfare, urban density, and environmental vulnerability demands urgent scientific inquiry. This study aims to assess the environmental impact of the 2023–2024 war on air quality in the Gaza Strip by examining temporal and spatial changes in key atmospheric pollutants. We use daily observations of five pollutants, nitrogen dioxide (NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), carbon monoxide (CO), methane (CH<sub>4</sub>), and the ultraviolet aerosol index (UVAI), obtained from the Sentinel-5P TROPOspheric monitoring instrument (TROPOMI) satellite and combine these with meteorological data (temperature, humidity, wind speed, and precipitation) to explore their behavior before and during the conflict. Our methodology integrates time-series analysis with statistical and machine learning models, including SARIMAX, Holt-Winters, Random Forest, and XGBoost, to forecast pollutant concentrations based on pre-war conditions and identify deviations post-October 2023. The findings reveal distinct responses to pollutants during the war. UVAI and CO showed sharp and sustained increases linked to widespread combustion and infrastructure damage, while CH<sub>4</sub> concentrations exhibited a steady rise associated with the collapse of waste management. SO<sub>2</sub> displayed episodic spikes, likely tied to fuel depot destruction and generator use, whereas NO<sub>2</sub> trends showed temporary suppression due to mobility restrictions and reduced industrial activity. Our findings demonstrate that traditional forecasting models may require adaptation to conflict-specific conditions, given altered emission sources and rapid pollutant dispersal in a small geographic area like Gaza. Policy implications include the urgent need for conflict-sensitive environmental monitoring systems, the integration of satellite data into humanitarian planning, and the development of adaptive forecasting models that incorporate war-related variables, such as infrastructure damage and displacement patterns.</p><h2>Other Information</h2><p dir="ltr">Published in: Global Environmental Change<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gloenvcha.2025.103044" target="_blank">https://dx.doi.org/10.1016/j.gloenvcha.2025.103044</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fcf44060c63ca0a6b8801bc835c22f2c |
| identifier_str_mv | 10.1016/j.gloenvcha.2025.103044 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30018715 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessmentAmmar Abulibdeh (15785928)Earth sciencesAtmospheric sciencesEnvironmental sciencesEnvironmental managementHuman societyDevelopment studiesInformation and computing sciencesMachine learningAir pollutionMachine learning forecastingEnvironmental monitoringSentinel-5PGaza Strip<p dir="ltr">Armed conflicts pose severe environmental challenges, particularly in densely populated and infrastructure-limited regions. The Gaza Strip exemplifies such a context, where the intersection of warfare, urban density, and environmental vulnerability demands urgent scientific inquiry. This study aims to assess the environmental impact of the 2023–2024 war on air quality in the Gaza Strip by examining temporal and spatial changes in key atmospheric pollutants. We use daily observations of five pollutants, nitrogen dioxide (NO<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), carbon monoxide (CO), methane (CH<sub>4</sub>), and the ultraviolet aerosol index (UVAI), obtained from the Sentinel-5P TROPOspheric monitoring instrument (TROPOMI) satellite and combine these with meteorological data (temperature, humidity, wind speed, and precipitation) to explore their behavior before and during the conflict. Our methodology integrates time-series analysis with statistical and machine learning models, including SARIMAX, Holt-Winters, Random Forest, and XGBoost, to forecast pollutant concentrations based on pre-war conditions and identify deviations post-October 2023. The findings reveal distinct responses to pollutants during the war. UVAI and CO showed sharp and sustained increases linked to widespread combustion and infrastructure damage, while CH<sub>4</sub> concentrations exhibited a steady rise associated with the collapse of waste management. SO<sub>2</sub> displayed episodic spikes, likely tied to fuel depot destruction and generator use, whereas NO<sub>2</sub> trends showed temporary suppression due to mobility restrictions and reduced industrial activity. Our findings demonstrate that traditional forecasting models may require adaptation to conflict-specific conditions, given altered emission sources and rapid pollutant dispersal in a small geographic area like Gaza. Policy implications include the urgent need for conflict-sensitive environmental monitoring systems, the integration of satellite data into humanitarian planning, and the development of adaptive forecasting models that incorporate war-related variables, such as infrastructure damage and displacement patterns.</p><h2>Other Information</h2><p dir="ltr">Published in: Global Environmental Change<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.gloenvcha.2025.103044" target="_blank">https://dx.doi.org/10.1016/j.gloenvcha.2025.103044</a></p>2025-08-13T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.gloenvcha.2025.103044https://figshare.com/articles/journal_contribution/Air_pollution_in_Gaza_during_the_post-october_7_era_a_satellite_and_machine_learning_assessment/30018715CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300187152025-08-13T15:00:00Z |
| spellingShingle | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment Ammar Abulibdeh (15785928) Earth sciences Atmospheric sciences Environmental sciences Environmental management Human society Development studies Information and computing sciences Machine learning Air pollution Machine learning forecasting Environmental monitoring Sentinel-5P Gaza Strip |
| status_str | publishedVersion |
| title | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| title_full | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| title_fullStr | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| title_full_unstemmed | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| title_short | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| title_sort | Air pollution in Gaza during the post-october 7 era: a satellite and machine learning assessment |
| topic | Earth sciences Atmospheric sciences Environmental sciences Environmental management Human society Development studies Information and computing sciences Machine learning Air pollution Machine learning forecasting Environmental monitoring Sentinel-5P Gaza Strip |