Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model
Particulate matter (PM2.5) concentrations are a serious concern for human health. In this study, a multinomial model was proposed to predict PM2.5 concentrations, and the relationship between PM2.5, PM10, and atmospheric parameters was studied. The study was conducted in northern Iraq, including Kir...
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/33/e3sconf_gases2025_05002.pdf |
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| author | Abdlwahd Jamel Alqdori Areej |
| author_facet | Abdlwahd Jamel Alqdori Areej |
| author_sort | Abdlwahd Jamel Alqdori Areej |
| collection | DOAJ |
| description | Particulate matter (PM2.5) concentrations are a serious concern for human health. In this study, a multinomial model was proposed to predict PM2.5 concentrations, and the relationship between PM2.5, PM10, and atmospheric parameters was studied. The study was conducted in northern Iraq, including Kirkuk Governorate. Data were collected from different sources and two datasets collected in February 2020 and July 2022 were used. The model was applied to the study area within Kirkuk Governorate: Based on the July 2022 dataset, the average value of the coefficient of determination R2 within Kirkuk Governorate was estimated to be 0.97; Based on the February 2020 dataset, the average value of R2 within Kirkuk Governorate was estimated to be 0.98, and the prediction accuracy was 82% for July and 96% for February. Moreover, the health impacts and air quality in the area were found to range from moderate to unhealthy. The aim of this study was to develop a multinomial model to predict PM2.5 concentrations using PM10, humidity, temperature and wind speed as independent variables. The results showed a high correlation coefficient (R2 = 0.98) between predicted and measured PM2.5 concentrations. The outputs of the (IDW) model indicate that the predicted PM2.5 concentration ranges between (35.92-47.65) μg/m3, which is an unhealthy air quality for sensitive groups in Kirkuk Governorate. The results of the study highlighted the impact of industrial areas and recommended monitoring and reducing exposure to particulate matter and pollutants emitted from factories. |
| format | Article |
| id | doaj-art-b01065b00efc47b0a8a876e305cc58b2 |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-b01065b00efc47b0a8a876e305cc58b22025-08-20T03:21:12ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016330500210.1051/e3sconf/202563305002e3sconf_gases2025_05002Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a modelAbdlwahd Jamel Alqdori Areej0Al-Mustansiriya University, College of Basic Education, Department of GeographyParticulate matter (PM2.5) concentrations are a serious concern for human health. In this study, a multinomial model was proposed to predict PM2.5 concentrations, and the relationship between PM2.5, PM10, and atmospheric parameters was studied. The study was conducted in northern Iraq, including Kirkuk Governorate. Data were collected from different sources and two datasets collected in February 2020 and July 2022 were used. The model was applied to the study area within Kirkuk Governorate: Based on the July 2022 dataset, the average value of the coefficient of determination R2 within Kirkuk Governorate was estimated to be 0.97; Based on the February 2020 dataset, the average value of R2 within Kirkuk Governorate was estimated to be 0.98, and the prediction accuracy was 82% for July and 96% for February. Moreover, the health impacts and air quality in the area were found to range from moderate to unhealthy. The aim of this study was to develop a multinomial model to predict PM2.5 concentrations using PM10, humidity, temperature and wind speed as independent variables. The results showed a high correlation coefficient (R2 = 0.98) between predicted and measured PM2.5 concentrations. The outputs of the (IDW) model indicate that the predicted PM2.5 concentration ranges between (35.92-47.65) μg/m3, which is an unhealthy air quality for sensitive groups in Kirkuk Governorate. The results of the study highlighted the impact of industrial areas and recommended monitoring and reducing exposure to particulate matter and pollutants emitted from factories.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/33/e3sconf_gases2025_05002.pdf |
| spellingShingle | Abdlwahd Jamel Alqdori Areej Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model E3S Web of Conferences |
| title | Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model |
| title_full | Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model |
| title_fullStr | Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model |
| title_full_unstemmed | Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model |
| title_short | Spatial modeling for predicting and identifying levels of climate-related disease hazards in Iraq using (GIS) Kirkuk as a model |
| title_sort | spatial modeling for predicting and identifying levels of climate related disease hazards in iraq using gis kirkuk as a model |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/33/e3sconf_gases2025_05002.pdf |
| work_keys_str_mv | AT abdlwahdjamelalqdoriareej spatialmodelingforpredictingandidentifyinglevelsofclimaterelateddiseasehazardsiniraqusinggiskirkukasamodel |