Schoobrary رجوع
العودة إلى البحث
رسائل ماجيستير الانجليزية 2019 0e0ef14f-62e3-489d-8361-8f0b7a436d51

Determining The Main Drivers Of Pm And Co Concentration Surfaces In The Greater Beirut Area Using A Landuse Regression Approach

إبراهيم علم الدين, جاد فارس زلزال

كلية مارون سمعان للهندسة والعمارة-الجامعة الأمريكية في بيروت · لبنان

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

The Eastern Mediterranean city of Beirut, Lebanon, suffers from poor air quality as compared to many European and North American cities. In this study, high resolution PM₂.₅ (fine particulate matter), PM₁₀ (coarse particulate matter), CO (carbon monoxide) and PM2.5-PM₁₀ annual and seasonal pollution maps are generated for the Greater Beirut Area (GBA) using Land Use Regression models (LUR). The LURs were calibrated and validated on monthly data collected from 58 predefined monitoring locations within the GBA between March 2017 and March 2018. The annual mean concentrations of PM₂.₅, PM₁₀ and CO across the monitoring stations were 68.1 ± 15.7 µg-m³, 83.5 ± 19.5 µg-m³ and 2.48 ± 1.12 ppm respectively. The observed spatio-temporal variability in the recorded PM concentrations was found to be larger than those typically reported in European cities. The performance of the developed LUR models was good, with R² ranging from 0.59 to 0.67 for the PM₂.₅ models, 0.49 to 0.63 for the PM₁₀ models, 0.50 to 0.60 for the CO models, and 0.32 to 0.51 for the PM₂.₅-PM₁₀ ratio models. Overall, the predicted pollution surfaces were able to conserve the inter-pollution correlations that were determined from the field monitoring campaign, with the exception of the cold season. Although the model structure of the generated LUR models differed, building area, distance to main roads, and industrial area were all found to be common predictors across the majority of the annual and seasonal models. The predicted PM surfaces suggested that the entire population of the GBA was exposed to annually-averaged concentrations that exceeded the 24-hour WHO (World Health Organization) air quality standards set for PM₂.₅ and PM₁₀. Finally from a methodological point of view, the results of this study show that LURs generated using low cost purpose-designed monitoring campai

التعريف والنوع

رقم الوثيقة
0e0ef14f-62e3-489d-8361-8f0b7a436d51
رقم العقد
0
نوع الوسائط
Crawler
نوع المحتوى
الرسائل العلمية
صيغة المصدر
رسائل ماجيستير
نوع الملف
pdf text
أسماء الملفات
1632357_1.pdf

بيانات النشر

ألقاب المؤلفين
[{"name_ar":"إبراهيم علم الدين","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"جاد فارس زلزال","title_ar":"اعداد","title_en":"Preparation"}]
اللغة
English

المصدر والدورية

اسم المصدر
Determining The Main Drivers Of Pm And Co Concentration Surfaces In The Greater Beirut Area Using A Landuse Regression Approach

المحتوى والصفحات

عدد الصفحات
0

إشراف وإعداد

الإشراف
إبراهيم علم الدين
الإعداد
جاد فارس زلزال

الاقتباسات الببليوغرافية

APA

إبراهيم علم الدين و جاد فارس زلزال. (2019). Determining The Main Drivers Of Pm And Co Concentration Surfaces In The Greater Beirut Area Using A Landuse Regression Approach. أطروحة(رسائل ماجيستير). كلية مارون سمعان للهندسة والعمارة-الجامعة الأمريكية في بيروت. لبنان.

MLA

إبراهيم علم الدين و جاد فارس زلزال. Determining The Main Drivers Of Pm And Co Concentration Surfaces In The Greater Beirut Area Using A Landuse Regression Approach. 2019. كلية مارون سمعان للهندسة والعمارة-الجامعة الأمريكية في بيروت، رسائل ماجيستير.