This document was produced by UNEP Regional Office for West Asia (UNEP-ROWA)

The overall work was done in the framework of UNEP ROWA, with the financial assistance of Greek Aid and the cooperation of the Ministry of Environment and UNDP in Lebanon in the framework of Environmental Resources Monitoring Project

The consultancy work is achieved by a team of consultants from the American University of Beirut led by Dr. Najat Saliba.

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Table of Contents A.1 Monitoring and Reporting Strategies for Criteria Pollutants in Several Countries 10 Table A.1: Models adopted by each country concerning the type of pollutant, measurement, frequency and the format of reporting 12 A.2 Monitoring and Reporting Strategy for Lebanon 14 Table A.2: Monitoring model applicable to Lebanon regarding type of pollutant, measurement, frequency and the format of reporting 14 A.3 Type of Equipment 14 Table A.3: Suggested specifications for the monitoring equipment of basic criteria air pollutants 15 Table A.4: Suggested specifications for the meteorological station 20 Table A.5: Suggested specifications for the open path gas measurements 20 Table A.6: Suggested specifications for the gas calibration standards in aluminum cylinders 22 A.4 Human Resources for Managing Equipment 22 Table A.7: Workforce needed for monitoring equipment and analyzing data based on full time equivalency (FTE) 22 Table A.8: Training and workshops prior to managing data and equipment 23 Table A.9: Maintenance schedule for real time air quality analyzers 23 Table A.10: Routine operation to ensure a monitoring network at optimal levels 24 B.1 Geographical Context 25 B.1.1 Physical Characteristics 25 Figure B.1: Lebanon geographical location 25 B.1.2 Topography and Geology 25 B.1.3 Climate of Lebanon 26 Figure B.2: Topography of Lebanon with emphasis over the different elevations in the country; Geography Department, Faculte des Lettreset Science Humaines (FLSH), Universite Saint Joseph (USJ), 2009 27 Figure B.3: Annual average precipitation in Lebanon; Geography Department, FLSH, USJ, 2009 28 Figure B.4: Annual average temperature distribution in Lebanon; Geography Department, FLSH, USJ, 2009 30 Figure B.5: Yearly average wind direction recorded at the Hariri International Airport in 2005 30 Figure B.6: Summer average wind direction (left) and winter average wind direction (right) in 2005 as recorded in the Hariri International Airport(Chelala 2008) 31 Figure B.7: Wind field at 12 AM (left) and at 5 PM (right) on November 19, 2010 as deduced from Weather Research and Forecast(WRF) modeling(WRF) (Geography Department, FLSH, USJ, 2012) 31 B.2 Human Characteristics 31 B.2.1 Population 31

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Figure B.8: Population percentage (top) and population density (bottom) by Mohafazat (Geography Department, FLSH, USJ, 2009) 33 Table B.1: Population distribution in Lebanon by Mohafazat (CAS 2006) 33 Table B.2: Population density per each Mohafaza (CAS 2006) 33 B.2.2 Land Use 34 Figure B.9: Land use in Lebanon as per the results reported in 2007 34 B.2.3 Urbanization 34 Figure B.10: Urbanization spread in Lebanon (top) and construction permit evolution per 1000 m2 (bottom) between 2002 and 2007 (Geography Department, FLSH, USJ, 2009) 35 Table B.3: Distribution of the population, urban density and increase of urban area in Lebanon (1996)(Dar el Handasah and IAURIF 2005) 36 B.2.4 Transportation 36 Figure B.11: Lebanese road network (Geography Department, FLSH, USJ, 2009) 37 Table B.4: Lebanese road categories (MOE/EU/NEAP 2005) 37 Table B.5: Estimation of the composition of Lebanese vehicle fleet(Chelala 2008) 38 Figure B.12: Total vehicle registration for the two periods: 1994-2000 and 2001-2006 (Unpublished data Motor vehicle department 2008) 38 Figure B. 13: Vehicle fleet age (Unpublished data Motor vehicle department 2008) 38 B.3 Economic Sector 39 B.3.1 Industries 39 Table B.6: Number of industries by mohafazat (LIA 2008) 39 Figure B.14: Spatial distribution of the number of registered industries (LIA 2008) 40 B.3.2 Power Generators 40 Table B.7: List of Electric Power Plants (MOE/ECODIT 2001) 40 Figure B.15: Distribution of electricity generation (ALMEE 2007) 41 B.3.3 Quarries 41 Figure B.16: Distribution of Quarries over Lebanon(Geography Department, FLSH, USJ, 2012) 42 B.4 Identification of air pollution hotspots in Lebanon 42 B.4.1 Methodology 42 B.4.2 Determining the monitoring sites in Lebanon 43 Table B.8: Urban Structure in Lebanon 43 Figure B.17: HYSPLIT trajectory dispersion simulation of the quarries on 12 May 2012 (Geography Department, FLSH, USJ, 2012) 44 B.4.3 Modeling Approach 45 Figure B.18: Schematic Diagram of the sequence of variables used in the modeling of air pollution 46 Figure B.19: The nested areas considered in modeling the weather data in Lebanon 47 Figure B.20: Boundary conditions deduced by the CHIMERE model 48 B.4.5 Results 48

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Figure B.20: CHIMERE model for the spatial distribution and the concentration of NO2 (left) for November 19, 2010 and O3 (right) for July 14, 2010 (Geography Department, FLSH, USJ, 2012) 49 Figure B.21: Locations of the air pollution hotspots in Lebanon (Geography Department, FLSH, USJ, 2012) 50 Table B.9: Location, Description and recommended air pollutant measurements in the identified hotspots in Lebanon, NOx, PM10, O3 and SO2 are major criteria pollutants, VOC still needs to be assessed before a monitoring equipment is installed 51 Table B.10: Priority Pollutants in identified hot spots 54 Table C.1: Priority of monitoring pollutants in the different sites of phases I, II and III 56 Table C.2: Cost of the monitoring equipment, housing, basic needs and data acquisition of each station based on three priority models; A, B and C. Prices are suggested based on one manufacturer’s quotation 57 Table C.3: Breakdown and suggested prices for the housing of the instruments 58 Table C.4: Suggested prices for one station basic accessories to install and operate the air monitoring instruments 59 Table C.5: Suggested prices for accessories and data acquisition system (DAS) 60 Table C.6: Estimated cost for operating and maintaining air quality monitoring stations 61 Table C.7: Estimated cost for yearly accessories 62 Table C.8: Suggested prices for a moving van equipped with monitoring equipment 63 Table C.9 Suggested prices for an Open Path Gas measurement equipment 63 Table C.10: Estimated cost for yearly accessories 64 D.1 Air Quality Index (AQI) Definition and Calculation 65 D.2 AQI Reporting 65 D.3 AQI color code 66 Conclusions and Recommendations 66 E. References 67

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LIST OF TABLES Table A.1: Models adopted by each country concerning the type of pollutant, measurement, frequency and the format of reporting 12 Table A.2: Monitoring model applicable to Lebanon regarding type of pollutant, measurement, frequency and the format of reporting 14 Table A.3: Suggested specifications for the monitoring equipment of basic criteria air Pollutants 15 Table A.4: Suggested specifications for the meteorological station 20 Table A.5: Suggested specifications for the open path gas measurements 20 Table A.6 Suggested specifications for the gas calibration standards in aluminum cylinders 22 Table A.7: Workforce needed for monitoring equipment and analyzing data based on full time equivalency (FTE) 22 Table A.8: Training and workshops prior to managing data and equipment 23 Table A.9: Maintenance schedule for real time air quality analyzers 23 Table A.10: Routine operation to ensure a monitoring network at optimal levels 24 Table B.1: Population distribution in Lebanon by Mohafazat (CAS 2006) 33 Table B.2: Population density per each Mohafaza (CAS 2006) 33 Table B.3: Distribution of the population, urban density and increase of urban area in Lebanon (1996)(Dar el Handasah and IAURIF 2005) 36 Table B.4: Lebanese road categories (MOE/EU/NEAP 2005) 37 Table B.5: Estimation of the composition of Lebanese vehicle fleet(Chelala 2008)- 38 Table B.6: Number of industries by mohafazat (LIA 2008) 39 Table B.7: List of Electric Power Plants (MOE/ECODIT 2001) 40 Table B.8: Urban Structure in Lebanon 43 Table B.9: Location, Description and recommended air pollutant measurements in the identified hotspots in Lebanon, NOx, PM10, O3 and SO2 are major criteria pollutants, VOC still needs to be assessed before a monitoring equipment is installed 51 Table B.10: Priority Pollutants in identified hot spots 54 Table C.1: Priority of monitoring pollutants in the different sites of phases I, II and III 56 Table C.2: Cost of the monitoring equipment, housing, basic needs and data acquisition of each station based on three priority models; A, B and C. Prices are suggested based on one manufacturer’s quotation 57 Table C.3: Breakdown and suggested prices for the housing of the instruments 58 Table C.4: Suggested prices for one station basic accessories to install and operate the air monitoring instruments 59

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Table C.5: Suggested prices for accessories and data acquisition system (DAS) 60 Table C.6: Estimated cost for operating and maintaining air quality monitoring stations 61 Table C.7: Estimated cost for yearly accessories 62 Table C.8: Suggested prices for a moving van equipped with monitoring equipment 63 Table C.9: Suggested prices for an Open Path Gas measurement equipment 63 Table C.10: Estimated cost for yearly accessories 64

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LIST OF FIGURES Figure B.1: Lebanon geographical location 25 Figure B.2: Topography of Lebanon with emphasis over the different elevations in the country; Geography Department, Faculte des Lettreset Science Humaines (FLSH), Universite Saint Joseph (USJ), 2009 27 Figure B.3: Annual average precipitation in Lebanon; Geography Department, FLSH, USJ, 2009 28 Figure B.4: Annual average temperature distribution in Lebanon; Geography Department, FLSH, USJ, 2009 30 Figure B.5: Yearly average wind direction recorded at the Hariri International Airport in 2005 30 Figure B.6: Summer average wind direction (left) and winter average wind direction (right) in 2005 as recorded in the Hariri International Airport(Chelala 2008) 31 Figure B.7: Wind field at 12 AM (left) and at 5 PM (right) on November 19, 2010 as deduced from Weather Research and Forecast(WRF) modeling(WRF)(Geography Department, FLSH, USJ, 2012) 31 Figure B.8: Population percentage (top) and population density (bottom) by Mohafazat (Geography Department, FLSH, USJ, 2009) 33 Figure B.9: Land use in Lebanon as per the results reported in 2007 34 Figure B.10: Urbanization spread in Lebanon (top) and construction permit evolution per 1000 m2 (bottom) between 2002 and 2007 (Geography Department, FLSH, USJ, 2009) 35 Figure B.11: Lebanese road network (Geography Department, FLSH, USJ, 2009) 37 Figure B.12: Total vehicle registration for the two periods: 1994-2000 and 2001-2006 (Unpublished data Motor vehicle department 2008) 38 Figure B. 13: Vehicle fleet age (Unpublished data Motor vehicle department 2008) 38 Figure B.14: Spatial distribution of the number of registered industries (LIA 2008) 40 Figure B.15: Distribution of electricity generation (ALMEE 2007) 41 Figure B.16: Distribution of Quarries over Lebanon(Geography Department, FLSH, USJ, 2012) 42 Figure B.17: HYSPLIT trajectory dispersion simulation of the quarries on 12 May 2012 (Geography Department, FLSH, USJ, 2012) 44 Figure B.18: Schematic Diagram of the sequence of variables used in the modeling of air pollution 46 Figure B.19: The nested areas considered in modeling the weather data in Lebanon 47 Figure B.20: Boundary conditions deduced by the CHIMERE model 48

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Figure B.20: CHIMERE model for the spatial distribution and the concentration of NO2 (left) for November 19, 2010 and O3 (right) for July 14, 2010 (Geography Department, FLSH, USJ, 2012) 49 Figure B.21: Locations of the air pollution hotspots in Lebanon (Geography Department, FLSH, USJ, 2012) 50

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LIST OF ACRONYMS ADEME l’Agence de l’Environnement et de la Maîtrise de l’Énergie AQI Air Pollution Index AQHI Air Quality Health Index AQI Air Quality Index AQRU Air Quality Research Unit CAA Clean Air Act CEPA Canadian Environmental Protection Act EDL Electricite du Liban EPA Environmental Protection Agency FAO Food and Agriculture Organization in the United Nations FLSH Faculte des Lettres et Science Humaines GFS Global Forest System GIS Global Information System IPCC Intergovernmental Panel on Climate Change LAQI Lebanese Air Quality Index MOE Ministry of Environment MOEW Ministry of Energy and Water MOPWT Ministry of Public Works and Transportation NAMS National Air Monitoring Stations QA Quality Assurance QC Quality Control NCEP National Centers for Environmental Prediction SLAMS State and Local Air Monitoring Stations SPMS Special Purpose Monitoring Stations SIP State Implementation Plans USG Unsafe for Sensitive Groups WRF Weather Research and Forecast

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Establishing a monitoring and reporting programme

A.1 Monitoring and Reporting Strategies for Criteria Pollutants in Several Countries

Before establishing any network of air monitoring stations for criteria air pollutants, enacting clean air laws is a necessary first step. This step will provide the relevant authorities the means and the guiding principles to successfully develop and manage a monitoring and reporting programme. Two well-known clean air laws that effectively demonstrate the process needed to develop a monitoring and reporting strategy are the Canadian Environmental Protection Act (CEPA 1999), and the 1990 Clean Air Act (CAA, as amended) implemented by the United States Environmental Protection Agency (EPA). Both acts share a common goal to protect the environment and human health.

The CEPA authorizes the Canadian government, the relevant authority, to enforce air quality standards and further promulgates rules for developing an environmental monitoring and reporting program. The CAA is a U.S. federal law that, among other responsibilities establishes an essential partnership between the federal government and the states, assigns tasks at a national level fulfilled by the federal government, and at a state, local or tribal level. In accordance with the CAA, the U.S. government ensures that every state meets the national air quality standards and maintains its State Implementation Plans (SIP). Furthermore, publishing and disseminating information to the public is greatly emphasized in these and many other countries’ clean air laws. Public participation and awareness are an integral part of the acts. The CEPA ensures public access to environmental information by establishing an online CEPA Environmental Registry, and a National Release Pollutant Inventory. Similarly, the CAA allows the public to easily access online sources of environmental information, reports, and collected monitoring data. Essentially, both clean air acts serve as an example that can help define the steps needed for Lebanon to follow through on its ultimate goals proposed in the draft law on air quality protection.

In terms of the draft law, it overlaps with the above acts in many points such as public awareness, the emphasis of close collaboration among all stakeholders, and the need for a National Ambient Air Quality Monitoring Network of monitoring stations. Nevertheless, Lebanon still needs to gain a better understanding of the ambient air monitoring process, as well as the tools and resources needed to sustain an air quality monitoring and reporting programme. In references to the above clean air acts along with the associated air management procedures, several conditions were identified for Lebanon to successfully implement an air quality monitoring and reporting programme:

1. Developing overall clear objectives for conducting air quality monitoring in order to facilitate passage of enabling legislation. The objectives will need to clearly define what to monitor and why, who will handle data retrieval, processing and analysis, and what will the response or plan be if monitoring data indicates unhealthy conditions.

2. Determine criteria air pollutants of interest to be monitored, which have already been identified in phase 1.

3. Planning and selecting the appropriate type of air quality monitoring network, including the network design, selection of monitor (continuous air quality analyzers), site selection of monitoring stations (possibly in identified hot spots), data acquisition, and management and reporting procedures.

4. Identifying the kind of technical staffing and training required for maintenance and data management. Technical personnel needed may include people with expertise in various fields such as network development, monitoring methods, gas and particle atmospheric

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behaviors, atmospheric and meteorological modeling, information technology and statistics. This group of people must understand what data needs to be collected and its purpose, in order to tailor the monitoring network accordingly.

5. Determine the availability or lack of resources and the budget constraints. 6. Optimize public transparency by disseminating documented monitoring data.

By meeting the above conditions, Lebanon can establish a comprehensive air quality monitoring and reporting programme that will minimize health and ecosystem threats.

By reviewing existing models adopted by different countries, particularly those of countries that have similar environmental and meteorological conditions and/or have a well-developed program, a potential monitoring and reporting strategy can be designed for Lebanon. Table A.1 demonstrates the countries’ adopted models that encompass key components such as the type of measurement, frequency and format of reporting.

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Table A.1: Models adopted by each country concerning the type of pollutant, measurement, frequency and the format of reporting Country Type of

pollutants Averaging Time Reporting

Frequency Format of reporting of Air Quality Index (AQI)

Germany PM10 PM2.5 NO2 SO2 O3 CO

24 h- mean 1 h –mean 1 h –mean 1 h- mean 1 h- mean 8 h- mean

Daily Each hour Each hour Each hour Each hour Each hour

Map with colors and spots indicating the measurement stations Blue: 0µg/m3 light blue: 10-20µg/m3, light yellow: 20-30µg/m3, yellow: 30-40µg/m3, orange: 40-50µg/m3 (http://www.envit.de/umweltbundesamt/luftdaten/map.fwd?comp=PM1&setLanguage=en)

United Kingdom (UK)

PM10 NO2 SO2 O3 CO

24 h- mean 1 h- mean Max 15h- mean Max 1h- mean 8 h- mean

Daily 1. By region - Tables 2. By monitoring site - Tables 3. As pollutant graphs 4. A map for latest summary 5. Specify index bands of colors indicating the levels if green:

low, orange: moderate, red: high, violet: very high (http://uk-air.defra.gov.uk/latest/)

Athens PM10 NO2 SO2 O3 CO

24 h- mean 1 h –mean 1h & 24h-mean 1 h- mean 8 h- mean

Daily Table for each station (http://www.minenv.gr/1/12/122/12204/e1220400.html)

Canada PM10 PM2.5 O3

6 h- mean 6 h- mean 6 h- mean

Daily 1. Animation Maps • Color coded maps indicating the areas with green:

good, yellow: moderate, orange: Unsafe for Sensitive Groups (USG), red: unhealthy, violet: very unhealthy, brown: hazardous levels.

• Table indicating the current Air Quality Health Index (AQHI)

2. Tables according to regions (Eastern Canada, Western Canada, North America) for ozone measurements (http://www.weatheroffice.gc.ca/airquality/pages/landing_e.html)

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Country Type of pollutants

Averaging Time Reporting Frequency

Format of reporting of Air Quality Index (AQI)

Finland Air quality index (AQI)

Daily 1. Tables using AQI; green: good, yellow: satisfactory, orange: fair, red: poor, violet: very poor.

2. Maps showing the AQI at different stations (http://www.ilmanlaatu.fi/ilmanyt/nyt/ilmanyt.php?as=Suomi&rs=Valitse+kunta&ss=Valitse+mittauspaikka&p=stationindex&pv=22.12.2011&h=04&et=map&ls=englanti)

Hong Kong

Air pollution index (API)

Each hour Tables including the API for each station along with the contributing pollutant. Low 0-25, Medium 26-50, High 51-100, Very High 101-200 , Severe 201-500 (http://www.epd-asg.gov.hk/eindex.html)

China Air pollution index (API)

Daily Table and map including the API and grade relative to standard (I: well within, II: generally within, III: may approach or exceed, IV: generally exceeded, V: significantly exceeded) for each city (http://www-app.gdepb.gov.cn/raqi3/RAQI_en.htm)

Singapore PM10, NO2, SO2, O3, CO

Daily Table including the responsible pollutant, and the air quality description at different areas (good, bad…). (http://www.nea.gov.sg/psi/)

Australia Air quality index

Each hour 1. Show map with color codes; blue: very good, green: good, yellow: fair, orange: poor, pink: very poor, Red: hazardous. (http://www.environment.nsw.gov.au/aqms/aqi.htm)

2. Table showing different pollutants AQI for each area (http://www.environment.nsw.gov.au/aqms/aqi.htm)

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A.2 Monitoring and Reporting Strategy for Lebanon

Upon reviewing and assessing the types of monitoring and reporting strategies pursued by the different countries, similarities can be found in the type of measurement, frequency or format of the reporting. These similarities suggest that there is a general approach used across the different countries, which can be tailored to Lebanon’s environmental situation. By taking feasibility and the overlaps in the countries’ strategies into consideration, a possible monitoring and reporting model has been developed that is applicable to Lebanon, shown in Table A.2. The type of measurement was adopted from the EPA. This suggested model can help achieve the objectives of the draft law established by the Ministry of Environment (MOE) by improving the process of continuous monitoring of air quality, and promoting public access to information. By color-coding the degree of air quality, Lebanese citizens are aware of the quality of the air they breathe within their communities, and as such, ameliorate the health risks they are subject to on a daily basis.

Table A.2: Monitoring model applicable to Lebanon regarding type of pollutant, measurement, frequency and the format of reporting

Country Type of pollutant

Averaging Time

Frequency of reporting

Format of reporting AQI

Lebanon PM10 PM2.5 NO2 SO2 O3 CO

24 h- mean 24 h –mean 1 h –mean 1 h- mean 8 h- mean 8 h- mean

Daily Daily Each hour Each hour Every 8 hours Every 8 hours

AQI color code: GIS Map with colors indicating the areas with green: good, yellow: moderate, orange: USG, red: unhealthy, violet: very unhealthy, brown: hazardous levels.

The air quality real-time analyzer measures ambient air quality concentrations every 30 seconds and accumulates the 1-minute averages in its internal data logging system. Depending on the data converter’s configuration, averages of a certain number of minutes are transferred to a central database where it can be retrieved for data handling, analysis and reporting. The average time and the type of report can be presented as 1-hour, 8-hours, daily, monthly, or annually. In this report it is suggested that the reporting system is able to display the daily and/or hourly averages of the different criteria pollutants as shown in Table A.2. A.3 Type of Equipment

Once the monitoring and reporting model is well-defined, the type of equipment to be used in the monitoring stations located in hot spots needs to be determined. Table A.3-A.6 defines important features of the monitoring equipment for each pollutant including the specification, calibration, accessories, and maintenance. These suggested features are partly based upon the commonly used equipment approved by EPA

(see http://www.epa.gov/ttn/amtic/files/ambient/criteria/reference-equivalent-methods-list.pdf), and provided by several well-known companies such as Teledyne, Anderson, Thermo Scientific, and Met-One Instruments. In addition, they were selected based on whether or not the instrument is able to collect real-time concentration values, an important feature required for proper air quality assessment and evaluation. Continuous air quality analyzers can generate continuous data stream that can be saved (data logger) and aggregated over various time intervals such as 1-minute, 5-minute, or 1-hour, and then transferred to other databases. The accessories needed for

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maintenance, frequency of maintenance and amount of technical support were factors in determining the specifications. Table A.3: Suggested specifications for the monitoring equipment of basic criteria air pollutants Gas Pollutant/ Equipment

Specification

O3/ Ozone Analyzer

Measurement Method: Uses non-dispersive ultraviolet (UV) absorption detection Range: 0-392640 μg/m3 (200 ppm) Zero Noise 0.5 g/m3 (60 second averaging time) Lower Detectable Limit: < 0.98 μg/m3 Zero Drift (24 hour) < 20 g/m3 Span Drift <1% full scale per month Response Time 20 seconds (10 second lag time) Operating Temperature 20°C - 30°C Precision: 0.98 μg/m3 at < 196.32 μg/m3 otherwise 0.2% of reading Linearity: 1% of Full Scale Sample Flow Rate 1-3 liters/min Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard). 0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) Approved and certified by an accredited monitoring agency Data logging: Internal memory stores data in instantaneous format 1, 3, 5, 10, 30, or 60 minute intervals

NO, NO2, NOx/ NOx analyzer

Measurement Method: Chemiluminescence detection Ranges: 0-1881.4 μg/m3 to 0-376280 μg/m3 full sale, user selectable Zero Noise: <6.2 g/m3 Lower Detectable Limit (LDL): <75.25 μg/m3 (RMS) Zero Drift: <15.5 g/m3/24 hrs<31 g/m3/7 days Span Drift: <0.5%/24 hours, <1%/7 days Operating temperature range of 20-30°C Precision: 0.5% of Reading Linearity: 1% of Full Scale Sample Flow Rate shall be less than or equal to 0.5 LPM. Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard).0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) B: RS232, Ethernet, and Status Output Analyzer shall use a high capacity ozone generator internal to the instrument Ozone shall be scrubbed from the instrument without the use of charcoal or other expendables Approved and certified by an accredited monitoring agency Data Logging: Internal data logging with 1 minute to 24 hour multiple averages (over 500,000 records), both instantaneous and averaged concentration values are included

CO/ CO Analyzer

Measurement Method: Compares infrared energy absorbed by a sample to that absorbed by a reference gas based on Beer-Lambert law Ranges: 0-1145.2 μg/m3 to 0-1145200 μg/m3 full sale, user selectable

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Zero Noise 230 g/m3 (30 second averaging time) Lower Detectable Limit (LDL): 340 μg/m3 Zero Drift (24 hour) < 1100 g/m3 Span Drift <1% full scale per month Response Time 60 seconds (30 second lag time) Operating Temperature Range: 20°C – 30 C Precision 0.2% of reading Linearity: 1% of Full Scale Sample Flow Rate: 0.5 liters/min + 10% Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard).0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) Approved and certified by an accredited monitoring agency Data logging Internal memory stores data in instantaneous format 1, 3, 5, 10, 30, or 60 minute intervals

SO2/ UV Fluorescence SO2 Analyzer

Measurement Method: Pulse fluorescence ultraviolet (UV) absorption detection Range: 0-2617600 μg/m3 (200 ppm) Zero Noise 26.2 g/m3 (10 second averaging time) Lower Detectable Limit: < 52.3 μg/m3 Zero Drift (24 hour) < 26 g/m3 Span Drift <0.5% full scale per month Response Time 80 seconds (10 second average time) Operating Temperature 20°C - 30°C Precision 1% of reading Linearity: 1% of Full Scale Sample Flow Rate 0.5 liters/min. Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard). 0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) Approved and certified by an accredited monitoring agency Data logging: Internal memory stores data in instantaneous format 1, 3, 5, 10, 30, or 60 minute intervals

Gas Calibrator

Dilution System Flow Measurement Accuracy +/- 2% set point or +/- 1% FS, whichever is less from 20 to 100% FS Repeatability of flow control +/- 0.2% FS (Porter flow controller spec - better than 146C) Linearity of mass flow measurements +/- 0.5% FS Flow range of dilution air 0-10 SLPM Optional ranges 0-5/0-20 SLPM Flow range of cylinder gases 0-100SCCM Optional ranges 0-50/0-200 SCCM Zero Air requirements 10 SLPM @ 30 PSI Optional ranges 20 SLPM @ 30 PSI Calibration gas input ports 3, optional 6 Dilutent gas input ports 1 Response time <60 sec. To 99% (146C spec) Ozone Generator Option Maximum output 1 ppm @ 6 SLPM (units cannot be converted to g/m3 as it depends on the type of gas that is analyzed. This applies to all the below

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numbers) Minimum output 10 ppb @ 6 SLPM Photometer System Full Scale Range 100 ppb to 5 ppm user selectable Linearity 1% of Full Scale Precision 1 ppb Response Time 180 Seconds to 95% of Target Minimum Detectable Limit 3 ppb Test channel analog 6 @ +/- 100 mv, 1,5,10 volts (user selectable) Digital control inputs 16 Temperature range 0-40 deg C Weight 51 lbs (58 lbs for 220-240VAC) Power 220-240VAC 50/60Hz, 275 watts (with all options) Flow rate 0-20 liters/min

Zero air supply

Dew point Membrane Dryer: -10°C Heatless Dryer: -40°C Operating Temperature -10°C to -40°C Physical Dimensions 24" (D) x 19" (W) x 12.24" (H). 85 lbs. fully equipped 400 mm (D) x 483 mm (W) x 315 mm (H). 38.6 kg full equipped Power Supply 100 to 240 VAC, 50/60Hz (+/-10%), Instrument: 30 Watts, Pump: 100 Watts Pollutant Concentration Data Other compounds available upon request SO2< 0.1 ppb (2 g/m3) NO < 0.1 ppb (1.2 g/m3) NO2< 0.1 ppb (2 g/m3) O3< 0.4 ppb (2 g/m3) H2S < 0.1 ppb (2.6 g/m3) NH3< 0.1 ppb (0.69 g/m3) CO < 0.02 ppm (1.1 g/m3)

NH3 Analyzer

Measurement Method: light producing reaction to convert to total (NO) with ozone (O3) as its basic principle. The instrument has three modes of operation, NO, NOx and Nt Range: 0-150000 μg/m3 Zero Noise 3.5 g/m3 (10 second averaging time) Lower Detectable Limit: < 7 μg/m3 Zero Drift (24 hour) < 7 g/m3 Span Drift <1% full scale per month Response Time 120 seconds (10 second average time) Operating Temperature 20°C - 30°C Precision 0.4% of reading Linearity: 1% of Full Scale Sample Flow Rate 0.6 liters/min. Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard). 0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) Approved and certified by an accredited monitoring agency Data logging: Internal memory stores data in instantaneous format 1, 3, 5, 10, 30, or 60 minute intervals

H2S/ UV Fluorescence

Measurement Method: Pulse fluorescence ultraviolet (UV) absorption detection; converting H2S into SO2 Range: 0-2617600 μg/m3 (100 ppm)

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SO2 Analyzer Zero Noise 26.2 g/m3 (10 second averaging time) Lower Detectable Limit: < 52.3 μg/m3 Zero Drift (24 hour) < 26 g/m3 Span Drift <1% full scale per month Response Time 80 seconds (10 second average time) Operating Temperature 20°C - 30°C Precision 1% of reading Linearity: 1% of Full Scale Sample Flow Rate 1 liters/min. Power Requirements 220-240 VAC +/-10% @ 150W Outputs Selectable Voltage, RS232/RS485, TCP/IP, 10 Status Relays, and Power Fail Indication (standard). 0-20 or 4-20 mA Isolated Current Output (optional) Inputs 16 Digital Inputs (standard), 8 0-10vdc Analog Inputs (optional) Approved and certified by an accredited monitoring agency Data logging: Internal memory stores data in instantaneous format 1, 3, 5, 10, 30, or 60 minute intervals

NMHC

Measurement Method: back-flushed gas chromatography system Range: 0-1310000 μg/m3 (200 ppm methane) Accuracy 2% span Lower Detectable Limit: < 131 μg/m3 Analysis Time 70 seconds or less Operating Temperature 20°C - 30°C Precision 2% of reading Oven Temperature 150-200°C , 65°C column oven Sample Flow Rate 500 ml/min. Power Requirements 220-240 VAC +/-10% @ 150W Output Methane, NMHC, Total hydrocarbon and FID signal, User-selectable concentration ranges 0-10V, 5V, 1V, or 0.1 (standard) 4-20mA (optional), RS-485 (Optional)

Particulate Matter/ Equipment

Specification

PM10, PM2.5, PM1 E-BAM Real time monitoring in mass mode

Beta Source C14, less than 75 microcurie, Half life of 5730 years Detector: Scintillation probe Analog Output 0-1V, 0-5V, 0-10V selectable, 12 bit accuracy Filter Tape Continuous glass fiber filter Inlet PM10 impactor type Flow Rate: 16.7 liters per minute, adjustable Flow accuracy +/- 3% of reading, volumetric flow controlled Sample Pump Dual diaphragm type, internally mounted Alarm Signals Filter, flow, power and operation failure Input Power 12 Volts DC @ 36 Watts, 25°C , 48 Watts Max Alarm Contact Closure 2 Amp @ 240 VAC Operating Temperature -20°C - 40°C Enclosure BX-807 PM 2.5 Sharp Cut Cyclone 390062 Battery, 12VDC 100AHR, 390052 Battery Charger, 12 VDC @ 4 A / .3A hold AC adaptor, 100-240 VAC in, 12VDC @ 6A, Wind speed and direction sensor Humidity sensor MMP MicroMet Plus Software

18

EX 121 AC Power Supply UX-961 Transfer Module

-Gauge PM particulate monitor PM10, PM-2.5 and total dust

Continuous and simultaneous particulate collection coupled with beta ray attenuation C-14 method, no measurable decrease in activity Concentration accuracy <2% of measuring range Ranges 0 to 5,000μg/m3 or 0 to 10,000μg/m3 Minimum detection limit <1 μg/m3 (24-hour average); <4 μg/m3 (1-hour average) Precision of two monitors ± 2 μg/m3 (24-hour) Resolution ± 1 μg/m3 (instantaneous) Correlation coefficient R > 0.98 Drift <1% of measuring range/month Operating temperature -30 to 60°C zero point drift automatic zero point correction Air flow rate 1 m3/h (16.67 lpm) measured across an internal subsonic orifice; user selectable from 0 to 20 lpm Pump: 220/240V, 50/60Hz, 100W Pre-calibrated, no site-specific calibration required Power Requirements Instrument: 100-240V, 50/60Hz, 330W max., 15W without pump or heater Data averages Each full 1/2, and 24 hour values automatically stored Output Serial interface RS 232 Analog output: 4-20mA or 0-10V output of concentration (μg/m3) Tube length standard 2 m 0.5–5 m possible Measurement cycle Single filter spot in position for 24 hours (default); user selectable 30-minutes to 24 hours Repeated collection on the same spot, collected particles available for heavy metal analysis

Ambient Particulate Monitor simultaneously measures PM-10, PM-2.5 and PM-Coarse mass concentration

Standard System Configuration Menu-driven software for user interaction via 1/4 VGA display with touch screen, connecting and Interface cables, and vacuum pump, ePort software for local or remote communication Measurement Range: 0 to 1,000,000 μg/m³ (1 g/m³), Resolution: 0.1 μg/m³, Precision: ±2.0 μg/m³ (1-hour avg), Accuracy for mass measurement: ±0.75% Data Averaging and Output Real-time mass conc average: 1 hour rolling average updated every 6 minutes, Long-term averaging: 1, 8, and 24 hr, data output rate: selectable from 10 sec to 24 hour Operating temperature Range -40° and 60 °C. The TEOM sensor and control units must be weather protected within the range of 8° to 25 °C. An optional complete outdoor enclosure provides complete weather protection. Main flow rate: Fine PM filter: 3.0 l/min; Coarse PM filter:1.67 l/min, bypass flow rate: 12.0 l/min Data Storage Internal data logging of user-specified variables; capacity of 500,000 records. Data Output and Input ePort software to view and change system operation from PC, touch screen user interface, Ethernet with embedded FTP server, USB, RS232, RS485, 8 user-defined analog outputs (0-1 or 0-5 Vdc), Power Requirements Instrument: 100-240 VAC, 440 VA, 47-63 Hz Pump: 120 VAC/60 Hz: 4.25 A; 240 VAC/50 Hz: 2.25 A Approved and certified by an accredited monitoring agency

19

Table A.4: Suggested specifications for the meteorological station Meteorological Station Description Specifications Wind Speed & Wind Direction Wind Speed Range 0 – 50 m/sec

Wind Speed Resolution 0.1 m/sec Wind Speed Accuracy ± 2% Wind Direction Range 0 – 360° Wind Direction Resolution 1° Wind Direction Accuracy ± 5° Threshold, both Speed & Direction 1 m/sec

Temperature and Humidity Temperature Range -40°C to +60°C Temperature Resolution 0.1°C Temperature Accuracy ± 0.5°C Relative Humidity Range 0-100% Relative Humidity Resolution 1% Relative Humidity Accuracy ± 4%

Both Temperature and Humidity need to be built into the temperature shield at the bottom of the sensor. The integral shield limits errors due to solar radiation Barometric Pressure Measurement Range 500 – 1100 mbars

Measurement Resolution 0.1 mbar Measurement Accuracy ± 2 mbars

A solid state pressure sensor for measurement of barometric pressure. Electronic temperature compensation if included it provides highest accuracy over the operating temperature of the sensor. RS-232 Configuration Pyranometer Solar energy studies for site evaluations and monitoring, passive system analysis, irrigation scheduling and other environmental studies

Range 400 to 1100 nm Absolute error ±5% maximum Sensitivity 10 μV per 1000 W/m2 Linearity Maximum deviation of 1% up to 3000 W/m2 Stability < ± 2% change over one-year period Response Time 10 μs Temperature Dependence ± 0.15% per °C maximum Cosine Correction Cosine up to 80° angle of incidence Azimuth < ±1% error over 360° at 45° elevation Tilt No error induced from orientation Operating Temperature -40°C to +65°C Relative Humidity 0% to 100% RH Detector High stability silicon photovoltaic Sensor Housing Weatherproof

Rain Gauge Standard Precision Instrument for Measurement of Rainfall Accumulation &/or Rate

Output 0.1 second switch closure Accuracy ±0.5% at 1.25 in./hr (3.175 cm/hr.) ±2% at 5 in./hr (12.7 cm/hr) Sensitivity 0.2-2.54

Table A.5: Suggested specifications for the open path gas measurements

Open path gas measurement equipment

FTIR Spectrometer ZnSe window Internal black body calibration Interferometer with beamsplitter mount Detector mounting plate with focusing optics AD-converter, automation electronics, embedded webserver

20

Standard MCT detector: liquid nitrogen cooled, 1x1mm², MIR KBrbeamsplitter 24V DC Power supply (110V-240 VAC input voltage) Performance (with standard MCT): Spectral range: 4,000-750cm-1 Resolution: up to 1cm-1 (0.5cm-1optional) Wave no. accuracy: Better than 0.03cm-1 NEDT (Noise Equivalent Delta Temperature): up to 0.02°C for one scan with a resolution of 4cm-1 and a mirror speed of 40kHz. NESR (Noise Equivalent Spectral Radiance): 0.033mW/(m² sr cm-1) (at Tb= 30°C, v= 1,000cm-1 for one scan) Optical Bench: compact, enclosed, desiccated, connection for purging Interferometer: Michelson type patented ROCKSOLID® interferometer. Its main features are: vibration insensitive, friction-free mechanical bearing; permanently aligned; symmetrical interferogram acquisition up to a resolution of 1cm-1; (optional 0.5cm-1); selectable scanning velocities up to 40kHz (optional up to 160kHz); Scan-Rate up to 280scans/min (double-sided interferograms) at Dv= 4cm-1 (1,000scans/min optional). Electronics Automation: Microprocessor controlled optical bench, digital speed control. A/D converter: 24bit, 96kHz, max. data rate: 80kHz Ethernet interface allows for a rapid transfer of measurement data onto the portable PC Included: manual Telescopic sight with mounts Tube with adapter Tool set Allen keys Accessories Ethernet cable (10m crossed, with IP67 plug) Tripod for the Spectrometer or the transmitter telescope, incl. head and spectrometer mount Transport and storage container for the Spectrometer (800x600x400mm) Transport bag for the storage of one tripod, (1000x500x270mm) Telescope, Receiver,3:1, 6inch diameter, FOV: 10mrad (for standard MCT) Detectors MCT detector mounted in cryo-cooler, narrow band In lieu of liquid nitrogen cooled MCT, no liquid N2 required Spectral range: 12,000-850cm-1 Fast Scan Option: D*:>4x10**10cm Hz½/W High Resolution Option: MTBF: 9,000h Ultrafast scanning option (160kHz, for high folding limits up to 3,900cm-1) High-Resolution option (0.5cm-1) Extension for operation in regions with high humidity Consisting of: T310/7 ZnSe beamsplitter, spectral range 6,000 to 500cm-1 in lieu of standard beamsplitter IR, FT-IR Spectroscopy Software Package

21

Table A.6 Suggested specifications for the gas calibration standards in aluminum cylinders Certified components Balance gas Applicable

concentration range Certification period (months)

Carbon monoxide Nitrogen or air >8 ppm 36 Hydrogen sulfide Nitrogen >4 ppm 12 Nitric oxide - Oxygen free nitrogen >4 ppm 24 nitrogen dioxide Air >80 ppm 24 Oxygen Nitrogen >0.8% 36 Sulfur dioxide Nitrogen or air >500 ppm 36 Propane Nitrogen or air >1 ppm 36

A.4 Human Resources for Managing Equipment

The success of the monitoring and reporting program largely depends on the personnel expertise and qualifications. Such qualifications associated with each job position and the assigned number of workers are listed in Table A.7. In addition, training and workshops listed in Table A.8 are required to ensure that every fieldworker acquires the needed skills to properly manage the data and equipment. In order to maintain the functionality of the equipment, the air pollutant analyzers and their accessories must be checked regularly according to the maintenance schedule shown in Table A.9. The routine operation schedule is summarized in Table A.10. Table A.7: Workforce needed for monitoring equipment and analyzing data based on full time equivalency (FTE) Specialty Job Description Number Procurement Purchasing capital equipment and consumables,

Developing contracts and maintenance agreements, Applying for other sources of funding

One person for the project

Field Engineer

Setting up a monitoring site, electricity, communications Developing standard operating procedures Selecting and installing monitoring equipment Calibrating equipment, performing quality control Shelter and equipment maintenance

One for every three stations

IT specialist and Data analyzer

Understanding population and measurement uncertainty Developing sampling designs Developing networks to achieve objectives Assessing/interpreting data (data quality assessments) Developing quality systems, Developing data quality objectives Implementing technical systems audits, performance evaluations Validating data, Quality Assurance (QA) reporting Selecting information technology (data loggers and local data base) Developing analyzer outputs to data loggers and data transfer to local data base Transferring data from local data base to external data repositories

One for every three stations

Supervisor PhD in air pollution; specialized in reviewing and evaluating results

One for more than three stations

22

Table A.8: Training and workshops prior to managing data and equipment Workshops Skills Training Record keeping. Using and Calibrating the equipment Data handling, retrieval, processing and

validation Uploading data to the website QA/QC for equipment, data and all related

electronics and accessories Monitoring and maintaining the equipment Courses (see the list below)

Suggested courses for Ambient Air Monitoring and QA Personnel:

1. Air Pollution Control Orientation Course 2. Principles and Practices of Air Pollution Control 3. Mathematics Review for Air Pollution Control 4. Orientation to Quality Assurance Management 5. Introduction to Ambient Air Monitoring 6. General Quality Assurance Considerations for Ambient Air Monitoring 7. Basic Air Pollution Meteorology 8. Quality Assurance for Air Pollution Measurement Systems 9. Atmospheric Sampling 10. Basic Electronics 11. Continuous Emission Monitoring 12. Network Design and Site Selection for Monitoring PM2.5 and PM10 in Ambient Air 13. Analytical Methods for Air Quality Standards 14. Data Quality Assessment 15. Introduction to Environmental Statistics

Table A.9: Maintenance schedule for real time air quality analyzers

Method Coverage (annual) Minimum frequency Quality control

One-Point QC: for SO2, NO2, O3, CO

Each analyzer

Once per 2 weeks

O3 Precision 7%, Bias 7%. SO2, NO2, CO Precision 10%, Bias 10%

Annual performance evaluation for SO2, NO2, O3, CO

Each analyzer Once per year < 15 % for each audit

concentration

Flow rate verification PM10, PM2.5, PM10-2.5, TSP

Each sampler

Once every month

< 4% of standard and 5% of design value

Semi-annual flow rate Audit PM10, PM2.5, PM10-2.5, TSP

Each sampler

Once every 6 months

< 4% of standard and 5% of design value

Collocated sampling PM2.5, PM10-2.5, TSP

15% within primary quality assurance

Every twelve days

PM2.5, 10% precision PM10-2.5, 15% precision TSP, 10% precision

PM Performance evaluation program

valid audits for primary QA, with < 5

over all 4 quarters

PM2.5, 10% bias PM10-2.5, 15% bias

23

Table A.10: Routine operation to ensure a monitoring network at optimal levels Item Each Visit Weekly/Monthly Minimum Review Data X Mark charts, where applicable X Check/Oil Exhaust Blower X Check Exterior X Check/Change Desiccant X Manifold Leak Test X Inspect tubing X Replace Tubing Annually Inspect manifold and cane X Clean manifold and cane Every 6 months or as

needed Check HVAC systems X Check electrical connections X Field site supply inventory X

PM2.5, PM10-2.5

sites 8 valid audits for primary QA, with > 5 sites All samplers in 6 years

Manual Methods Collocated sampling PM10, TSP, PM10-2.5, PM2.5

15% within primary quality assurance

Every 12 days or every 6 days

PM10, TSP, PM2.5, 10% precision PM10-2.5, 15% precision

Flow rate verification PM10 (low Vol), PM10-2.5, PM2.5,, TSP

Each sampler

Once every month

< 4% of standard and 5% of design value

Semi-annual flow rate audit PM10 (low Vol), PM10-2.5, PM2.5, TSP

Each sampler, all locations

Once every 6 months

< 4% of standard and 5% of design value

Performance evaluation program PM2.5, PM10-2.5

5 valid audits for primary QA, with < 5 sites 8 valid audits for primary QA, with > 5 sites All samplers in 6 years

Over all 4 quarters

PM2.5, 10% bias PM10-2.5, 15% bias

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Monitoring Stations in Key Hot Spots

Modeling of hotspots in Lebanon is based on the overlay of several variables including GIS analysis of the geographical characteristics, the topography, typology of areas, and demographic distribution of Lebanon. In addition, the meteorology and variations in boundary conditions as well as the major pollutants originating from major emission sources were considered. These variables are briefly introduced in the following paragraphs preceding the modeling section.

B.1 Geographical Context

B.1.1 Physical Characteristics

Lebanon is located on the eastern basin of the Mediterranean Sea (Figure B.1). Its surface is approximately 10 452 km2. It has a rectangular form with a length of 217 km and a width of 80 km in the northern section of the country and 48 km in the southern section. Its territorial boundary length is about 454 km. Lebanon is characterized by a mountainous area and a Mediterranean climate.

Figure B.1: Lebanon geographical location

B.1.2 Topography and Geology

Lebanon is essentially a mountainous country (Figure B.2). Topographically, it consists of the following areas aligned mainly from North-North-West (NNW) to South-South-East (SSE)(Walley et al. 2001).

From West to East:

• A narrow coastal plain. Its length is about 240 km composed of two Great Plains, one in the north (Aakar) and one in the south (Tyre). In the center, a succession of little narrow plains separated by rocky headlands is observed.

• The Mount Lebanon chain has an average elevation of about 2200 m with a peak at 3088 m (Qornet el Saouda). According to Walley (2001), Mount Lebanon can be divided into two parts; the north and the south. The north is a high region, (mostly over 1000 m) and cut by deep canyons. Its geology is composed essentially by Jurassic thick carbonate sediment. These layers are interrupted by a succession of basaltic volcanic formation, sands and clays from the superior Jurassic and inferior cretaceous. High plateau are recovered by the

25

Cenomanian calcareous formation. Topographically, the southern part of the Mount Lebanon chain is lower than the northern region; consequently, the elevation decreases towards the south. In the meridional part of the chain, the absence of any Mesozoic volcanism activity and the presence of inferior Nummulitic to middle Eocene calcareous formation is observed.

• The Beqaa valley is a flat basin with a length of about 120 km. It is an intra mountainous plain located between the Mount Lebanon and the anti Lebanon chains. Its elevation is primarily over 900 m and peaks at 1000 m at the center of the plain, covered in the central and southern part by thick Pliocene and quaternary lake deposits.

• The Anti Lebanon chain is subdivided into two mountain ranges: in the northern section, the highest summit is Talaat Moussa (2629 m) and in the southern part, Jabal el Sheikh or the Hermon Mount peaks at 2814 m. In the summit of the southern section, we can find Jurassic formation brought by an upthrust.

B.1.3 Climate of Lebanon

Lebanon is characterized by a Mediterranean Climate i.e. mild rainy winters and hot dry summers. However, Lebanon’s topography has an impact on the climatic variability of the country.

a) Precipitation and humidity: In Lebanon, precipitation is largely caused by rainy winds coming from the West or Southwest bringing large masses of humid air. The presence of mountains causes orographic precipitation: The western side of Mount Lebanon experiences significant rainfall because of the orographic lifting of the humid air mass (Figure B.3). Due to elevation, snowfall on mountains during the winter season can be observed. On the other hand, drier air caused by winds coming from the Caucasus contribute to slightly lower rainfall in the northern part of the littoral as compared to the central littoral (UNDP/GEF/MOPWT/DGU 2005). According to the “Atlas climatique du Liban” (Dar-IAURIF/CDR 2004), most regions in the northern littoral receive between 50 and 70 days of precipitation while the central region receives between 60 and 80 days.

26

Figure B.2: Topography of Lebanon with emphasis over the different elevations in the country; Geography Department, Faculte des Lettreset Science Humaines (FLSH), Universite Saint Joseph (USJ), 2009

27

Figure B.3: Annual average precipitation in Lebanon; Geography Department, FLSH, USJ, 2009

Therefore, the quantity of rain varies according to the topography and the wind regimes. The largest rainfall occurs in the center mountain region where slopes oriented W and SW are exposed to rainy wind coming from the south and southwest. Total measured rainfall reaches approximately 1200 mm/year (Figure B.3). Because of the orographic rainfall, the highest summit of Lebanon receives more than 1500 mm of rain/year. Littoral zones are the second most exposed zone to precipitation, and can reach around 1000 mm in the central littoral region. The northern and southern littorals receive around 900 and 700 mm/year, respectively. The Mount Lebanon Chain, which acts as a natural barrier, influences the rain variability of the inland region in such a way that

28

the northern portion of the Beqaa Valley receives approximately 200 mm. As the height of the mountain barrier decreases towards the south, the internal Litany region receives 500 mm to 1000 mm/year while the internal Hasbani region receives around 1000 mm/year.

The relative humidity near the littoral is the highest with an annual average over 70%. It decreases as elevation increases. The slopes of the western mountain range facing the sea up to an altitude of approximately 1800 m experience relative humidity ranging between 60 and 70%. Above this altitude, the relative humidity drops to less than 60%. In the inland region, a similar pattern is observed with the western side of the plateau exhibiting an elevated relative humidity (60-70%) and the eastern side showing relative humidity levels below 60%(UNDP/GEF/MOPWT/DGU 2005).

Temperature: Globally, spatial distribution of yearly average temperature depends on the presence of the mountains. Temperatures higher than 20°C are measured along the coastal plain (Figure. B.4). As the elevation increases, a decrease in temperature is attributed to the adiabatic effect, with a 15 ºC line observed at elevations ranging between 1100 and 1200 m. A yearly average of 5-10ºC is noted at elevations above 1800 m altitude (UNDP/GEF/MOPWT/DGU 2005).

b) Wind: Wind speed and direction typically depend on the general barometric situation. Because of its geographical location, Lebanon,experiences two types of wind: synoptic and local wind.

• Synoptic wind: In winter, continental air from Euro-Asia enters Lebanon through the Homs passage and the Internal Oronte valley. This air is mostly dry and cold and produces snow when mixed with warmer and humid maritime air. In summer, the air masses come only from the littoral after traveling around Cyprus. They are warmer and humid due to their passage over the sea (UNDP/GEF/MOPWT/DGU 2005).

• Local wind: Based on the meteorological station of the Hariri National Airport (Chelala 2008) the average prevailing wind observed in 2005 was predominated by a SW direction, as shown in Figure B.5. The average wind speed was recorded at about 2.9 m/s. The seasonal wind shows a prevailing SW wind in the summer and a NW-SW wind in the winter (Figure B.6).

Due to the topography of the country Lebanon experiences local breeze-like winds, sea breezes, earth breezes and valley wind. The speed of these winds is estimated to be less than 3 m/s (Figure B.7).

29

Figure B.4: Annual average temperature distribution in Lebanon; Geography Department, FLSH, USJ, 2009

Figure B.5: Yearly average wind direction recorded at the Hariri International Airport in 2005

30

Figure B.6: Summer average wind direction (left) and winter average wind direction (right) in 2005 as recorded in the Hariri International Airport(Chelala 2008)

Figure B.7: Wind field at 12 AM (left) and at 5 PM (right) on November 19, 2010 as deduced from Weather Research and Forecast(WRF) modeling(WRF)(Geography Department, FLSH, USJ, 2012)

B.2 Human Characteristics

B.2.1 Population

The latest population census conducted in Lebanon was in 1932. Since then, demographical data have been estimated, hence the observed variations among different sources. Furthermore, the massive immigration due to the war makes it more difficult to get an accurate representation of the Lebanese population. Between 1994 and 1996, a survey conducted by the United Nations

31

concluded that the total resident Lebanese population ranged between 4 and 4.2 million with a demographic growth of 2.1% (Abu Jawdeh et al. 2000). Later in 2005, another study concluded that the Lebanese population is 3,753,785(CAS 2006). This decrease over a decade could be attributed to several factors, one being the deviation between the different methodologies. The distribution of the population over the Mohafazat (Figure B.8) indicates that Beirut and Mount Lebanon include more than 50% of the population, North Lebanon holds 20% and the remainder is distributed over the Bekaa, South Lebanon and Nabatiyeh (Table B.1). The average population density was found to be 359 persons/km2 in 2004. Population density is highest in Beirut and Mount Lebanon at 19,195 and 762 persons/km2 respectively, and the lowest in the Beqaa with 110.8 persons/km2 (Table B.2).

32

Figure B.8: Population percentage (top) and population density (bottom) by Mohafazat (Geography Department, FLSH, USJ, 2009)

Table B.1: Population distribution in Lebanon by Mohafazat (CAS 2006) Mohafazat Population (2004) Percentage (2004) Beirut 389,661 10.4 Mount Lebanon 1,501282 40 North Lebanon 768,709 20.5 Beqaa 471,138 12.5 South Lebanon 401,075 10.7 Nabatiyeh 221.920 5.9 Total 3,753,785 100

Table B.2: Population density per each Mohafaza (CAS 2006) Mohafazat Population Density (km2) Beirut 19195 Mount Lebanon

762.5

North Lebanon

389.5

Beqaa 110.8 South Lebanon

434.3

Nabatiyeh 209.1

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B.2.2 Land Use

The most recent Land Cover map of Lebanon was completed in 2007 by the Ministry of Agriculture (MoA) and United Nations Food and Agriculture Organization (FAO). The artificialized territory, which according to this classification constitutes 6% of the Lebanese territory, is mainly concentrated along the Lebanese coast. Forest area and scrubland constitutes 25% (Figure B.9).

Figure B.9: Land use in Lebanon as per the results reported in 2007

B.2.3 Urbanization

In 1963, urban areas covered 254 km2. By 1998 urban areas had expanded to 599 km2, thereby occupying 6.3% of the Lebanese territory. The National Physical Master Plan of the Lebanese territory (Dar el Handasah and IAURIF 2005) estimates that the existing urban area covered roughly 600 km2 in the year 2000, and average urban growth had reached 136% since 1963 (a growth equivalent to 3.5% per year or to 8-10 km2 per year). In particular, an increase of 16% in the urban area of Beirut was reported in the 1990s (Faour et al. 2005). Mountains are also vulnerable to this urbanization (Figure B.10), which is growing along the ridge lines. One urban area inside the Beirut River watershed increased from 4 km2 in 1994 to 17 km2 in 1998 to 220 km2

in 2010. Construction is ongoing, with the number of construction permits (expressed in surface area per 1000 m2) having increased by about 33.8% in the last 7 years. This translates to a growth rate of 4.8% per year. The highest growth in urbanization is currently concentrated around Beirut and other agglomerations such as Nabatiyeh and Saida. Between 1963 and 1998, urbanization in the latter areas increased by 325% and 275%, respectively. Table B.3 shows the relative increase of major urban areas over the same time period. Urbanization estimates predict an expansion by an additional 250 to 300 km2 over the next 30 years (MOE/EU/NEAP 2005).

The uncontrolled urban growth in Lebanon is increasing, to the detriment of natural and agricultural areas. Soil is being removed and dumped into the sea for use in purported land reclamation operations (MOE/EU/NEAP 2005). This phenomenon, referred to as “mitage” in French or “scattered construction”, is best described as the uncontrolled and typically sporadic parceling out of (urban-type) buildings across the landscape.

34

Figure B.10:Urbanization spread in Lebanon (top) and construction permit evolution per 1000 m2 (bottom) between 2002 and 2007 (Geography Department, FLSH, USJ, 2009)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

2000 2001 2002 2003 2004 2005 2006year

Surfa

ce a

rea

(100

0 m

2 )

35

Table B.3: Distribution of the population, urban density and increase of urban area in Lebanon (1996)(Dar el Handasah and IAURIF 2005)

B.2.4 Transportation

The transport system in Lebanon is comprised of land, maritime, and air transport subsystems. The transport infrastructure consists of the road network, the Hariri International Airport and the main seaports of Beirut, Tripoli, Saida and Sour. The land transport infrastructure is practically characterized by the national road network, the vehicle fleet and the public transport system (MOE/EU/NEAP 2005).

a) Road transportation

Road network: In 2001, the mapping of the Lebanese road network amounted to 22,000 km (Figure B.11), out of which only 6,380 km (~30%) were classified as asphalted roads. These were subdivided into four categories: international, secondary, primary, and local (Table B.4). It is important to note that while classified roads are under the authority of the Ministry of Public Works and Transportation (MOPWT), the unclassified roads are managed by the municipalities.

Population Net density (hab/km2)

Increase of urban area from 1963 to

1998 (%) Central Urban Area (Beirut, Mount-Lebanon) 1,578,218 9,408 + 94

Agglomeration of Tripoli 286,309 18.527 + 100 Agglomeration of Saida 165,278 11,019 + 275 Agglomeration of Zahle 115,35 10,072 + 83 Agglomeration of Sour 104,655 11,628 + 200 Agglomeration of Baalbek 63,289 6,992 + 200 Agglomeration of Nabatiyeh 51,672 3,04 + 325 Total of 6 agglomerations 786,553 10,282 + 119 Total Lebanon 4,005,025 6,686 + 136 % Central Urban Area / Total Lebanon 39.40%

% 6 Agglomerations / Total Lebanon 19.60%

36

Figure B.11: Lebanese road network (Geography Department, FLSH, USJ, 2009)

Table B.4: Lebanese road categories (MOE/EU/NEAP 2005) Road category Length (Km)

International 583 secondary 1,717 Primary 1,495 Local 2,810

Vehicle fleet: The Lebanese vehicle fleet continues to expand (Table B.5). According to the MOPWT, 800,000 vehicles were recorded in 1998 and 962,800 in 2002 (Chelala 2008). The motor vehicle department reported a total number of 1,160,000 cars in 2005; an estimation of about 1:3 vehicle to person, or an average density of 52 cars/km. Comparing the total number of car registrations for the period 1994-2000 to the one of 2001-2006, a significant increase is observed

37

(Figure B.12). Furthermore, the age of these vehicles is relatively old with more than 61% of the cars being older than 12 years (Figure B.13).

Table B.5: Estimation of the composition of Lebanese vehicle fleet(Chelala 2008)

Type of vehicle Number Fleet (%) Number Fleet (%) % Increase Year 2002 2005

Private cars 820,000 85.17 1,000,000 86.21 21.95 Private trucks 90,500 9.40 100,000 8.62 10.50 Shared ride cars 32,000 3.32 33,000 2.84 3.13 Trucks > 5 tonnes 13,300 1.38 15,000 1.29 12.78 Public buses 4,000 0.42 7,000 0.60 75.00 private buses>12 passengers 3,000 0.31 5,000 0.43 66.67

Total 962,800 100 1,160,000 100 20.48

Figure B.12: Total vehicle registration for the two periods: 1994-2000 and 2001-2006 (Unpublished data Motor vehicle department 2008)

Figure B. 13: Vehicle fleet age (Unpublished data Motor vehicle department 2008)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

Before 1994 1995-2002 2003-2005 2006-2007

perc

enta

ge o

f car

s

38

B.3 Economic Sector

B.3.1 Industries

Most of the country’s industries are located in Mount Lebanon (446) and Beirut (76) (Figure B.14). Very few are located in South Lebanon (16) and Nabatiyeh (7). The most abundant types of industries are plastic, paints, cosmetics and food as detailed in Table B.6. These constitute almost 40% of the total number of industries, whereas textile and minerals constitute 11% and 10%, respectively. Only 18% of industrial units are located inside established industrial zones (LIA 2008). More than 80% of the industrial facilities are scattered among urban, rural or natural areas and cause great damage to both the built and natural environments. Furthermore, the industrial zones lack the necessary infrastructures, and need rehabilitation in order to come into compliance with updated regulations of safety and environmental requirements. Moreover, most of the areas which were created in the early 1970s in the periphery of the capital are now adjacent to dense residential areas with no buffer regions to separate the two zones. The classification of industrial activities is based on two decrees (MOE/EU/NEAP 2005):

1. Decree 4917/94 classifies industrial and non-industrial units into three categories without taking into consideration environmental concerns. This decree is based on the size of the establishment, the number of employees, available machinery and engine horsepower (MOE/ECODIT 2001).

2. Decree 5243/01 amended Decree 4917/94 to include environmental criteria such as the impact on water, air and soil. It introduces five industry categories ranging from class I (High risk for environment and health) to class V (no risk).

Table B.6: Number of industries by mohafazat (LIA 2008) Sector/Mohafazat Beirut Beqaa Mount

Lebanon North

Lebanon South

Lebanon Nabatiyeh Total

Metals 4 3 48 1 1 57 Furniture 2 1 24 4 1 2 34 Paper and Cardboard

13 3 42 1 1 60

Chemical products 6 3 102 7 3 121 Different Products 2 1 8 1 12 Food Products 18 22 63 15 5 3 126 Industrial and Electrical Machines

6 3 42 2 4 57

Leather Products 13 13 Minerals 12 3 42 4 1 62 Textile 13 54 1 1 1 69 Transport 5 5 Water and Wastewater Treatment

3 3

Total 76 39 446 36 16 7 620

39

Figure B.14: Spatial distribution of the number of registered industries (LIA 2008)

B.3.2 Power Generators

Electricity is supplied through Electricite du Liban (EDL), an autonomous utility under the jurisdiction of the Ministry of Energy and Water (MOEW). EDL operates 5 hydropower plants and 7 thermal plants (Table B.7, Figure B.15). With the exception of a small percentage of hydropower (2.3%), Lebanon depends entirely on imports of fuel for energy. While an increase in electricity production was observed between 2000 and 2003, the electricity generation seemed stable after this period. The high demand for electricity was compensated by private power generators. This study did not account for private power generators, which have mushroomed in the past decade due to intermittent power outages. Even though they are growing in large numbers, their distribution in the Lebanese territory is not compiled given that this sector is not regulated. Table B.7: List of Electric Power Plants (MOE/ECODIT 2001)

Hydropower Thermal Region Power

Capacity (MW) Region Power Capacity

(MW) Fuel Type

Kadisha 7.4 Zouk 628 Fuel/Diesel oil El Safa 13 Jiyeh 262 Fuel oil Nahr Ibrahim

33 HreysheQadisha 65 Fuel oil

El Bared 17 Baalbeck 70 Diesel oil Litani No data Sour 70 Diesel oil Zahrani 435 Diesel oil/Natural

gas Beddawi 435 Diesel oil/Natural

gas

40

Figure B.15: Distribution of electricity generation (ALMEE 2007)

B.3.3 Quarries

Lebanon‘s quarry sector is very poorly organized (MOE/ECODIT/UNDP 2011). According to studies, the number of quarries varies from 885 to about 1,278 quarries covering 5,267 ha (MOE/ECODIT/UNDP 2011) scattered across the country. (Figure B.16). These quarries scar the Lebanese landscape and the vast majority are unlicensed .

41

Figure B.16: Distribution of Quarries over Lebanon (Geography Department, FLSH, USJ, 2012)

B.4 Identification of air pollution hotspots in Lebanon

B.4.1 Methodology

The preferred method for the identification of hotspots in Lebanon is based on the inventory of the different emission sources in Lebanon coupled with the model developed by l’Agence de l’Environnement et de la Maîtrise de l’Énergie (ADEME). Relying on this successful model is essential since information presented in this report indicates a lack of continuous data in most emission sectors. ADEME has established clear guidelines for the classification of the permanent air quality monitoring stations according to their locations and measuring objectives (ADEME 2002). Seven monitoring classes were defined based on meteorological, topographical, source distribution and source emission. They are: urban, suburban, rural, regional, national, industrial, and traffic. On the other hand, the European Union under the EUROAIRNET identifies the need for two main monitoring classes: the background and the proximity sites. The background classification includes the urban, near city, regional and remote sites whereas the proximity classification is composed of industrial and traffic monitoring sites. The U.S. EPA ambient air

42

quality monitoring program consists of three major categories of monitoring stations: State and Local Air Monitoring Stations (SLAMS), National Air Monitoring Stations (NAMS), and Special Purpose Monitoring Stations (SPMS) (EPA 2011).

B.4.2 Determining the monitoring sites in Lebanon

GIS approach: In Lebanon, the classification method adopted by ADEME helps define six main categories of monitoring stations distributed over four different background sites of urban, suburban, regional rural and national rural and two proximity sites of industrial and traffic. This classification is based on several criteria including the urban structure, residential density, agglomerations (Dar-IAURIF/CDR 2004) (Table B.8), population estimated by the central administration of the Lebanese statistics (1996), the geographical information of the road network and the land use (2007), the satellite imagery of CNES/SPOT (Google Earth 2011) as well as the location of quarries (Unpublished data MOE).

Table B.8: Urban Structure in Lebanon

Urban Space Principal urban agglomeration (Central and periphery)

> 100,000 habitants

Secondary urban agglomeration (Central and periphery)

Between 40,000 and 100,000 habitants

Suburban zones - Isolated cities > 10,000 habitants - Small cities Between 5,000 and 10,000 habitants

Rural Space Bourgs important towns Between 2,500 and 5000 habitants

Urban stations are determined based on a location 100 m away from the main road and the urban density of residential buildings and population. High consideration is given to the ground occupation and the center of agglomerations as defined by ADEME. Some industrial sites, such as the electric power station in Zouk, were still classified as an urban site due to their location at the periphery of the suburban agglomeration of Beirut.

Suburban stations are based on the delineation of the agglomeration of the suburban zones in addition to the criteria for urban background sites.

Regional rural stations are a function of their geographic location in rural zones. They must enable the monitoring of the plumes of nearby urban areas and atmospheric pollution on a regional scale. Territories cover large surface-areas with a light total population.

National rural stations are representative of the different air mass flows across the region as defined by ADEME. Sites need to be well exposed on a slope with an altitude greater than the highest value of the nocturnal inversion layer. In addition to the local dominant wind direction, the main sectors of precipitation systems and areas of air-mass stagnation must be considered in the choice of the site.

Proximity traffic stations are in close proximity to the road network located at the agglomeration center with high residential density and traffic.

Proximity industrial non-urban stations

Quarries: sites are defined based on three criteria (Environment Canada 2009)

i) particle emission per quarry estimated as follows:

Particle emission = particle emission factor (kg/tons) x annual production (t/year)x 1ton/1000kg. 43

ii) Dispersion simulation using dispersion models like HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) to determine the air parcel trajectories to complex dispersion and deposition simulations. Figure B.17 shows an example of the output of the HYSPLIT model for a particular day and time.

iii) GIS analysis based on a combination of emissions and ground occupation. Results allowed identifying highest zone emission densities, highest population and most vulnerable population to air pollution.

Figure B.17: HYSPLIT trajectory dispersion simulation of the quarries on 12 May 2012 (Geography Department, FLSH, USJ, 2012)

Industrial: Industrial sites located in the vicinity of residential areas exert high pressure on the environment and the well-being of inhabitants in the region. One example is the Selaata industry of phosphorous and sulfur products which emits mainly SO2 and PMs. Being surrounded by heavy industry and having specific topographical and climatic conditions add to the severity of its pollution effects on the region (Antoun 2012)

44

B.4.3 Modeling Approach

General Method: analysis of ambient concentration of primary pollutants such as NO2 over Lebanon can be assessed solely on GIS analysis of the geographical characteristics. However, for secondary pollutants like O3, physico-chemical models such as “CHIMERE”1 need to be adopted in order to account for all precursors leading to its formation, decomposition and dispersion over several kilometers into the atmosphere (CHIMERE).

Model variables:

Time and simulation period depends on the meteorology, boundary conditions and emission sources .

Variables independent of time such as topography, surface content (city, forest, river, etc.), soil type and landscape.

Figure B.18 summarizes the main inputs used to run the model. Below is a short description of the parameters:

• Meteorology: Meteorological data was downloaded from WRF (Weather Research and Forecasting Model) and were projected on the grid defined by CHIMERE. WRF is a fully compressible, nonhydrostatic model with an Eulerian mass dynamical core. The model solver integrates the atmospheric equations and interfaces with the physics parameterizations to generate forecasts of meteorological variables. "Downscaling" WRF outputs is based on the view that regional climate is the result of interplay of the overall atmospheric or oceanic circulation and of regional specifics, such as topography, land-sea distribution and land use.

• Boundary conditions: A set of boundary conditions from the MOZART, LMDz-INCA or GOCART global simulations models is proposed as a default solution. Additional nested domains were defined for the local boundary layers. Both conditions are important for understanding the dispersion of short and long-lived pollutants.

• Emissions: o Anthropogenic, such as traffic and industrial emissions, are consequences of human

activities o Biogenic emissions are naturally occurring emissions, calculated using the model MEGAN o Mineral aerosol emissions are characterized based on soil, surface and wind properties

1The CHIMERE model uses meteorological model fields and emissions fluxes and calculates deterministically their behavior in the troposphere. The results are three-dimensional fields of chemical concentrations.

45

Figure B.18: Schematic Diagram of the sequence of variables used in the modeling of air pollution

Model application to Lebanon:

The results of the CHIMERE model application for three days are representative of Lebanese weather types (November 19, March 25, and July 14, 2010). The model outputs were validated using the NO2 measurements conducted by the Air Quality Research Unit (AQRU) for NO2 over the whole year, 2010. Please note that due to the limited capacity of our computer system, it was impossible to run the model for the year.

1. Meteorological fields obtained by WRF modeling.: The large scale forcing for the WRF run is adopted by the Global Forest System (GFS) provided by the National Centers for Environmental Prediction (NCEP) and WRF. Hourly weather fields are provided with a triple interlocking (27 km parent domain and two nests at 9 and 3 km) (Figure B.19).

46

Figure B.19: The nested areas considered in modeling the weather data in Lebanon

2. Emissions: • Anthropogenic: A register of hourly emissions for seven days of the week was done on all of

Lebanon for point sources, linear and planar, for different sectors, and ozone precursor pollutants, namely NOx and VOC.

Road Transport Sector (line source): an expert approach was used to determine flow, speed and features of cars on different road types (highway, main road, secondary road, path and other) in each of the 25 cazas in Lebanon. This data was coupled with the slope of the road and the type of fuel used, and then introduced in the module COPERT4 that is used for calculating emissions from road transport (AUTH 2007). The land registry schedule for 7 days of the week was deduced from the daily traffic profile. The sum of emissions of all roads in Lebanon thus calculated was compared to global emissions based on Intergovernmental Panel on Climate Change (IPCC) Tier 1 methodology (Abboud et al. 2009) that increases the amount of fuel used nationally by a predefined emission factor. The results of both methods are very close (27 Ggv/s30 Gg), confirming thereby the method of spatial emission.

47

Industrial sector (point sources on larger industrial sites, including power plants and cement plants): calculated emissions based on IPCC (IPCC 2006)and EMEP / CORINAIR(EMEP/Corinair 1999) methodology.

Residential/commercial/institutional (surface sources): calculated emissions nationally based on the IPCC methodologies EMEP/CORINAIR. It is then disaggregated by taking into account land use (Ministry of Agriculture 2007) and population across the division (CAS 1996). The result is a spatial resolution on a grid of 3 km.

• Biogenic emissions of aerosols and minerals: calculated automatically by CHIMERE from land use and soil type.

Physico-Chemical model: Run the CHIMERE model for three days representing the three seasons (November 19, March 25, and July 14, 2010) using the nested three domains. The largest area with a resolution of 27 km will be considered as boundary conditions based on a global model. Chemical concentrations schedules deliver field concentrations at 3 km resolution, and at the same time give inputs for the 9 km resolution field at its edge. The simulation of the central domain will therefore, at a time, provide a calculation end in its field and import / export data as realistic as possible to its limits (Figure B.20).

Figure B.20: Boundary conditions deduced by the CHIMERE model

B.4.5 Results

As an example, present in Figure B.21 are the modeling results for July 14, 2010 for NO2 and O3. While the spatial distribution was found to be similar for the three representative dates, the concentration of the pollutants changed with seasonal variations. Both the CHIMERE and Global Information System (GIS) models confirm the distribution of the hot spots proposed as shown in Figure B.22 and Table B.9.

48

Figure B.21: CHIMERE model for the spatial distribution and the concentration of NO2 (left) for November 19, 2010 and O3 (right) for July 14, 2010 (Geography Department, FLSH, USJ, 2012)

Locations of air pollution hot spots: Taking into account all preceding information, 29 hotspots were identified. They are distributed among background urban station, near-city background station, urban traffic station, industrial station, (quarries) , national station and regional rural station. Details on the location and the characteristics of air pollutants to be measured at each site are shown in Table B.9. Gas pollutants listed by priority are shown in Table B.10.

49

Figure B.22: Locations of the air pollution hotspots in Lebanon (Geography Department, FLSH, USJ, 2012)

50

Table B.9: Location, Description and recommended air pollutant measurements in the identified hotspots in Lebanon, NOx, PM10, O3 and SO2 are major criteria pollutants, VOC still needs to be assessed before a monitoring equipment is installed

Name of the monitoring

station

Station number

Station type

Recommended air pollutants

Type of area Coordinates/Altitude/Population Site Description

Beirut 1

Background urban station

NOx, PM10, O3, SO2 and VOCs under

relevant level

Center of principal

agglomeration

33°53'27.39"N 35°28'34.45"E /77 /403337

School surrounded by

buildings Tripoli 2 34°26'9.95"N 35°49'26.57"E /4 /

211134 Plain-green

space Saida 3 33°33'43.80"N 35°22'20.20"E /23 /

78808 Opened area

near the archeological

site Sour 4 33°16'4.66"N 35°12'40.39"E /6 /

47478 Opened area

near the archeological

site Zahleh 5 33°50'37.24"N 35°54'17.39"E /967

/51610 Beqaa plain,

cultivated area with tress

Golf of Beirut 6

Background urban station

NOx, PM10, O3, SO2 and VOCs under

relevant level

Periphery of principal

agglomeration

33°50'59.45"N 35°29'26.58"E /23 /201214

Green space, club of golf, near the sea

Hadath 7 33°49'38.19"N 35°31'16.38"E /43 / 24156

Lebanese university campus (Hadath)

JdeidehMetn 8 33°53'12.92"N 35°33'57.97"E /28 / 16254

Pine area

Zouk 9 33°58'4.88"N 35°36'49.64"E /70 / 16707

Pine area

QraiyetSaida 10 33°33'1.54"N 35°24'54.55"E /253 / 2259

Opened and orchard area

Jbail 11 Background urban station

NOx, PM10, O3, SO2 and VOCs under

relevant level

Center of secondary

agglomeration

34° 7'21.36"N 35°39'24.00"E /76 /13248

Opened and orchard area

Nabatiyeh 12 33°22'34.58"N 35°28'49.68"E /402 Opened area

51

Name of the monitoring

station

Station number

Station type

Recommended air pollutants

Type of area Coordinates/Altitude/Population Site Description

/17503 Baalbeck 13 34° 0'32.00"N 36°12'20.36"E /1146

/ Orchard area

facing archeological

site Chim 14 Background

urban station

NOx, PM10, O3, SO2 and VOCs under

relevant level

Periphery of secondary

agglomeration

33°37'22.33"N 35°28'51.90"E /528 /13566

Green space

Aaley 15

Near-city Background

station

NOx, O3, and photochemical

precursors Optional pollutants under relevant level

conditions: SO2, PM10, other pollutants

Near-city

33°48'28.42"N 35°36'19.98"E /890 /17732

Pine area

Broummana 16 33°52'43.53"N 35°35'53.95"E /511 /7873

Pine area

Zgharta 17 34°23'40.67"N 35°53'39.20"E /111 /12633

Opened area

Al Bazouria (Sour)

18 33°15'33.38"N 35°15'39.71"E /107 /5406

Opened and orchard area

Majdlaya (Tripoli)

19 Background urban station

NOx, PM10, O3, SO2 and VOCs under

relevant level

Periphery of principal

agglomeration

34°25'14.18"N 35°51'56.28"E /90 /undetermined

Opened and grove area

Beirut 20

Urban traffic station

Regulated pollutants of an “automobile” source

such as CO, NOx, particles and toxic

organic compounds. Optional pollutants on

the condition of relevant levels SO2(8),

Pb(9), metals and PAHs. Measuring ozone(10) is not

relevant

Road side

33°53'52.56"N 35°30'20.28"E / 20/ 403337

Build area

Tripoli Center 21 34°26'14.29"N 35°50'0.43"E /20 /211134

Build area

Saida Center 22 33°33'47.75"N 35°22'27.08"E /18 /78808

Build area

Sour Center 23 33°16'18.60"N 35°11'55.21"E /6 /47478

Build area

Zahleh Center

24 33°50'53.72"N 35°53'57.68"E /973 /51610

Build area

Selaata 25 Industrial station

Regulated pollutants of specific industrial

origin from the industrial

Rural or urban

34°16'42.50"N 35°40'11.80"E /184 /389

Orchard area facing the industry

52

Name of the monitoring

station

Station number

Station type

Recommended air pollutants

Type of area Coordinates/Altitude/Population Site Description

activity under consideration: SO2, VOC, PAH, heavy metals, NOx

in relevant level conditions, dioxins, HF...

Pollutant not recommended: O3, CO, except in special cases.

Meidoun (West Bekaa)

26

Industrial station

(Quarries)

Regulated pollutants of specific industrial

origin from the industrial activity under

consideration Pollutant not

recommended: O3, CO, except in special

cases.

Rural

33°27'56.11"N 35°38'22.76"E /1108 /undetermined

Opened area

Boustan El Assi

(Batroun)

27 34°14'21.76"N 35°49'26.69"E /587 /undetermined

Orchard area

Arz 28 National station

NOx, O3, photochemical

precursors, pollutants covered by the

Geneva Convention on transboundary

pollution

Rural 34°15'11.55"N 36° 4'54.65"E /2580 /uninhabited

Mountain area

Faraiya 29 Regional rural station

NOx, O3 and photochemical

precursors, analysis, the monitoring of

phenomena of local pollution (e.g.

ammonia).

Rural 34° 0'45.56"N 35°49'36.32"E /1375/ 2435

Mountain open and orchard

area

VOC: volatile organic compounds, PAH: polycyclic aromatic hydrocarbons, HF: hydrogen fluoride.

53

Table B.10: Priority Pollutants in identified hot spots Name of

the monitoring

station

Station number

Station type

Type of area Recommended air pollutants

Priorit

y 1 Priorit

y 2 Priorit

y 3 Priorit

y 4 Priorit

y 5 Beirut 1

Background urban station

Center of principal

agglomeration

NOx PM10 PM2.5 O3 SO2 Tripoli 2 PM10 NOx PM2.5 O3 SO2 Saida 3 NOx PM10 PM2.5 O3 SO2 Sour 4 NOx PM10 PM2.5 O3 SO2 Zahleh 5 NOx PM10 PM2.5 O3 SO2 Golf of Beirut 6

Background urban station

Periphery of principal

agglomeration

NOx PM2.5 PM10 O3 SO2

Hadath 7 NOx PM2.5 PM10 O3 SO2 JdeidehMetn 8 NOx PM2.5 PM10 O3 SO2

Zouk 9 NOx PM2.5 PM10 SO2 O3 QraiyetSaida 10 NOx PM2.5 PM10 O3 SO2

Jbail 11 Background urban station

Center of secondary

agglomeration

PM10 NOx PM2.5 O3 SO2 Nabatiyeh 12 NOx PM10 PM2.5 O3 SO2 Baalbeck 13 PM10 NOx PM2.5 O3 SO2

Chim 14 Backgroun

d urban station

Periphery of secondary

agglomeration

NOx PM10 PM2.5 SO2 VOCs

Aaley 15 Near-city

Background station

Near-city

NOx PM2.5 O3 PM10 SO2 Broummana 16 NOx PM2.5 O3 PM10 SO2 Zgharta 17 NOx PM2.5 O3 PM10 SO2 Al Bazouria (Sour) 18 NOx PM2.5 O3 PM10 SO2

Majdlaya (Tripoli) 19

Background urban station

Periphery of principal

agglomeration

PM10 NOx PM2.5 O3 SO2

Beirut 20

Urban traffic station

Road side

NOx PM2.5 Heavy Metals PM10 SO2

Tripoli Center 21 NOx PM2.5 Heavy

Metals PM10 SO2

Saida Center 22 NOx PM2.5 Heavy

Metals PM10 SO2

Sour Center 23 NOx PM2.5 Heavy Metals PM10 SO2

Zahleh Center 24 NOx PM2.5 Heavy

Metals PM10 SO2

Selaata 25 Industrial station

Rural or urban VOCs PM2.5 NOx O3 SO2

Meidoun (West Bekaa)

26 Industrial

station (Quarries)

Rural NOx PM10 PM2.5 O3 SO2

54

Name of the

monitoring station

Station number

Station type

Type of area Recommended air pollutants

Priorit

y 1 Priorit

y 2 Priorit

y 3 Priorit

y 4 Priorit

y 5 Boustan El Assi (Batroun)

27 NOx PM10 PM2.5 SO2 O3

Arz 28 National station Rural NOx O3 PM2.5 SO2 PM10

Faraiya 29 Regional rural station Rural NOx O3 PM2.5 SO2 PM10

C. Financial Resources to sustain Long-term Monitoring

Twenty-nine monitoring sites were identified based on population, agglomeration, emission sources and industrial sites as preceded. While this is an ideal case scenario, the application of such a monitoring system would incur a huge financial burden on the government as well as the sponsors. It is recommended that the proposed plan is implemented in several steps, starting with the first five monitoring sites. Sites 1-5 are classified as background urban stations for centers of principal agglomeration. They are identified based on highest population density and represent sites most indicative of the population exposure of more than 60% of the Lebanese population. Background urban sites are characterized by emission sources such as vehicle fleet, dust re-suspension and small scale industry, hence, NOx, PM10, and PM2.5 are considered the most relevant pollutants to be monitored in real time. Measurements of volatile organic carbon would be conducted at separate times in order to assess the need for real-time monitoring equipment. The financial reports of equipment, accessories, housing and labor needed to install five monitoring stations equipped with priority pollutant monitoring instruments are summarized in Tables C.1-C.9 according to different scenarios. In the second step, it is absolutely necessary that a network of another five monitoring sites (sites 6-10) is installed, as they are still classified as background urban station but for periphery of principal agglomerations. Similar monitoring stations can be installed.

55

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Table C.1: Priority of monitoring pollutants in the different sites of phases I, II and III

Phases

Name of the

monitoring station

Station

number

Station type

Type of area Recommended pollutants

Station

Type

Equipment specificatio

ns

Priorit

y 1 Priorit

y 2 Priorit

y 3 Priorit

y 4 Priorit

y 5

I

Beirut 1

Background urban station

Center of principal

agglomeration

NOx PM10 PM2.5 O3 SO2

Fixed Table A.3 Tripoli 2 PM10 NOx PM2.5 O3 SO2 Saida 3 NOx PM10 PM2.5 O3 SO2 Sour 4 NOx PM10 PM2.5 O3 SO2 Zahleh 5 NOx PM10 PM2.5 O3 SO2

II

Golf of Beirut 6

Background urban station

Periphery of principal

agglomeration

NOx PM2.5 PM10 O3 SO2

Fixed Table A.3

Hadath 7 NOx PM2.5 PM10 O3 SO2 JdeidehMetn 8 NOx PM2.5 PM10 O3 SO2

Zouk 9 NOx PM2.5 SO2 PM10 O3 QraiyetSaida 10 NOx PM2.5 PM10 O3 SO2

III Jbail 11 Backgroun

d urban station

Center of secondary

agglomeration

PM10 NOx PM2.5 O3 SO2 Fixed Table A.3 Nabatiyeh 12 NOx PM10 PM2.5 O3 SO2

Baalbeck 13 PM10 NOx PM2.5 O3 SO2

56

Scenario I

Objective I: Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration Table C.2: Cost of the monitoring equipment, housing, basic needs and data acquisition of each station based on three priority models; A, B and C. Prices are suggested based on one manufacturer’s quotation

Name of the

monitoring station

Station number Specific Instrument for

Recommended pollutants Cost (US dollars)

Priority NOx PM10 PM2.5 O3 SO2

Total Price for

Specific Instrument

Housing (Table C.3)

Basic equipment (Table C.4)

Data acquisition (Table C.5)

Total

Beirut 1 A 15000 25000 25000 65,000 50,220 42,250 13,800 171,270 B 15000 25000 25000 15000 80,000 50,220 42,250 13,800 186,270 C 15000 25000 25000 15000 15000 95,000 50,220 42,250 13,800 201,270 Tripoli 2 A 15000 25000 25000 65,000 50,220 42,250 13,800 171,270 B 15000 25000 25000 15000 80,000 50,220 42,250 13,800 186,270 C 15000 25000 25000 15000 15000 95,000 50,220 42,250 13,800 201,270 Saida 3 A 15000 25000 25000 65,000 50,220 42,250 13,800 171,270 B 15000 25000 25000 15000 80,000 50,220 42,250 13,800 186,270 C 15000 25000 25000 15000 15000 95,000 50,220 42,250 13,800 201,270 Sour 4 A 15000 25000 25000 65,000 50,220 42,250 13,800 171,270 B 15000 25000 25000 15000 80,000 50,220 42,250 13,800 186,270 C 15000 25000 25000 15000 15000 95,000 50,220 42,250 13,800 201,270 Zahleh 5 A 15000 25000 25000 65,000 50,220 42,250 13,800 171,270 B 15000 25000 25000 15000 80,000 50,220 42,250 13,800 186,270 C 15000 25000 25000 15000 15000 95,000 50,220 42,250 13,800 201,270

57

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration Housing Station unit Table C.3: Breakdown and suggested prices for the housing of the instruments

Item Description Unit Quantity Unit Cost Total Currency Note 1 Housing Steel Frame 1 $3,500.00 $3,500.00 USD $ Al Sheating 1 $3,500.00 $3,500.00 USD $ Door (Lockseal) 1 $850.00 $850.00 USD $ Insulation 1 $20,000.00 $20,000.00 USD $ 2 Air

conditioning 2 $2,000.00 $4,000.00 USD $ One (1) is required but REDUNDANCY is recommended

3 Electrical Cable m 100 $3.00 $300.00 USD $

Cable Trays /conduits m 20 $6.00 $120.00 USD $

Outlets 20 $50.00 $1,000.00 USD $ Tags 100 $3.00 $300.00 USD $ Tie wraps 200 $0.25 $50.00 USD $ Switches 20 $50.00 $1,000.00 USD $ Circuit breakers 20 $25.00 $500.00 USD $ Lights 6 $30.00 $180.00 USD $ Junction box 2 $85.00 $170.00 USD $ Terminal Blocks 100 $3.50 $350.00 USD $

Temperature Sensor 2 $650.00 $1,300.00 USD $

Alarm 1 $400.00 $400.00 USD $ Horn 1 $700.00 $700.00 USD $ Labor 1 $12,000.00 $12,000.00 USD $ 4 Foundation 0 $0.00 $0.00 USD $ Required by civil engineer

Grand Total $50,220.00 USD $

58

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration Basic Equipment Station unit Table C.4: Suggested prices for one station basic accessories to install and operate the air monitoring instruments

Item Description Unit Quantity Unit Cost Total Currency Note

1 Zero air supply

Specifications in Table xx 1 $15,000.00 $15,000.00 USD $

2 Gas Calibrator Specifications in Table xx 1 $15,000.00 $15,000.00 USD $

3 Calibration gases NO, NO2, CO, SO2 4 $1,000.00 $4,000.00 USD $

4 Regulators 6 $700.00 $4,200.00 USD $ 5 Tubings m 100 $5.00 $500.00 USD $

6 Tube fittings couplings, valves, stoppers, etc… 100 $7.00 $700.00 USD $

7 in relation to meteorological station

compressor 1 $2,000.00 $2,000.00 USD $

poll 1 $50.00 $50.00 USD $ tubings m 1 $50.00 $50.00 USD $ valves 5 $30.00 $150.00 USD $

8 UPS 2 $300.00 $600.00 USD $ Redundancy is required

Grand Total $42,250.00 USD $

59

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration Data Acquisition Station unit Table C.5: Suggested prices for accessories and data acquisition system (DAS)

Item Description Unit Quantity Unit Cost Total Currency

1 Ethernet hardware (box, cables, ports) 1 $500.00 $500.00 USD $

connection fee 1 $300.00 $300.00 USD $ operation fee month 12 $150.00 $1,800.00 USD $

2 data acquisition system

1 $10,000.00 $10,000.00 USD $

3 Computer 1 $1,000.00 $1,000.00 USD $ 4 Printer 1 $200.00 $200.00 USD $

Grand Total $13,800.00 USD $ Data Acquisition Systems (DAS): DAS should have modem and Internet capabilities; allow remote access and control; allow for digital input; and be able to initiate automated calibrations and polling. It is also recommended that DAS have software compatible with AQI reporting and editing.

60

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration O&M cost analysis Station unit Table C.6: Estimated cost for operating and maintaining air quality monitoring stations

Monitoring station

Station number Workforce Cost (US dollars)

Priority Field Engineer IT specialist /Data analyst Supervisor

Field Engineer @

$20/hour

IT specialist

@ $20/hour

Supervisor @

$100/hour Total

Work* Travel* Work Travel Work Travel Beirut 1 A 2 3 2 1 2 1 100 60 300 460 B 3 3 3 1 3 1 120 80 400 600 C 4 3 4 1 4 1 140 100 500 740 Tripoli 2 A 2 3 2 1 2 1 100 60 300 460 B 3 3 3 1 3 1 120 80 400 600 C 4 3 4 1 4 1 140 100 500 740 Saida 3 A 2 3 2 1 2 1 100 60 300 460 B 3 3 3 1 3 1 120 80 400 600 C 4 3 4 1 4 1 140 100 500 740 Sour 4 A 2 3 2 1 2 1 100 60 300 460 B 3 3 3 1 3 1 120 80 400 600 C 4 3 4 1 4 1 140 100 500 740 Zahleh 5 A 2 3 2 1 2 1 100 60 300 460 B 3 3 3 1 3 1 120 80 400 600 C 4 3 4 1 4 1 140 100 500 740 10 15 10 5 10 5 Considering 5 stations type A 25 15 15 Equivalent to 3 field engineers, 1 IT specialist and 1

Supervisor based on a maximum of 8 working hours/day

*Hours for Work at stations and travel between stations 61

Scenario I

Objective I Reporting Air Quality Index in principal and secondary agglomeration sites Phase I Background urban station, Center of principal agglomeration O&M cost analysis Station unit Table C.7: Estimated cost for yearly accessories

Item Description Unit Quantity Unit Cost Total Currency

1 Pump repair kit 6 $200.00 $1,200.00 USD $

2 Fuse 2A 12 $100.00 $1,200.00 USD $ 3 Capillary 12 $200.00 $2,400.00 USD $ 4 Light source 6 $200.00 $1,200.00 USD $ 5 purifiers Charcoal kg 1 $1,000.00 $1,000.00 USD $ purafill kg 1 $250.00 $250.00

6 Ozonator 2 $100.00 $200.00

7 Particulate filters 2 $1,000.00 $2,000.00 USD $

Grand Total $9,450.00 USD $

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Scenario II

Objective II Reporting Air Quality in industrial zones Cost analysis Moving unit (Table C.8) and open path air monitoring system (Table C.9) Table C.8: Suggested prices for a moving van equipped with monitoring equipment

Station type

Type area Recommended pollutants Cost (US dollars)

NOx PM10 PM2.5 O3 SO2

Total Price for

Instruments Moving Vehicle Housing Basic

equipment Data

acquisition Total

Moving Van

Industrial sites 15000 25000 25000 15000 15000 95,000 50,000 50,220 42,250 13,800 251,270

Table C.9 Suggested prices for an Open Path Gas measurement equipment

Description Quantity

Unit Price/ USD

Total Price/ USD

FTIR Spectrometer 1 150,000 150,000 Accessories Ethernet cable (10m crossed, with IP67 plug) 2 7,000 14,000 Tripod for the Spectrometer or the transmitter telescope, incl. head and spectrometer mount

1 3,600 3,600

Transport and storage container for the Spectrometer (800x600x400mm) 1 1,500 1,500 Transport bag for the storage of one tripod, (1000x500x270mm) 1 20,000 20,000 Telescope, Receiver,3:1, 6inch diameter, FOV: 10mrad (for standard MCT) 1 18,000 18,000 Detectors MCT detector mounted in cryo-cooler, narrow band In lieu of liquid nitrogen cooled MCT, no liquid N2 required, Spectral range: 12,000-850cm-1

Fast Scan Option: D*: >4x10**10cm Hz½/W 1 8,000 8,000 High Resolution Option: MTBF: 9,000h 1 4,480 4,480 Ultrafast scanning option (160kHz, for high folding limits up to 3,900cm-1) 1 7,600 7,600

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Scenario II

Objective II Reporting Air Quality in industrial zones O&M cost analysis Open path gas measurement equipments Table C.10: Estimated cost for yearly accessories

Item Description Unit Quantity Unit Cost Total Currency

1 Light source 1 $3,000.00 $3,000.00 USD $ 2 Cryo-cooler 1 $1,000.00 $1,000.00 USD $ 3 Lenses 1 $2,000.00 $2,000.00 USD $

4 Connectors and Tubing 1 $1,00.00 $1,000.00 USD $

Grand Total $7,000.00 USD $

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D. Display of Public Information

D.1 Air Quality Index (AQI) Definition and Calculation

Information will be displayed to the public through a website, which includes a map showing the location of stations and the level of air pollution as per the Lebanese Air Quality Index (LAQI) based on color codes (green: good, yellow: moderate, orange: unsafe for sensitive groups, red: unhealthy, violet: very unhealthy, brown: hazardous levels). In addition, trends will be incorporated and presented in the same way, and will predict air pollutant levels for upcoming days. The final website design has to be simple, attractive, easy to explore and regularly updated on an hourly or daily basis.

Moreover, color codes were chosen as a reporting format to allow the maximum dissemination of information to all Lebanese groups of different socio-economic backgrounds and educational levels. Such a format will make it easier for the public to relate the information to their well-being and everyday life. Colors indicate the level of pollution in relation to health outcomes, safety, and degree of dangerous risk. They are based on the AQI ranges. AQI reports the degree of air pollution, which ranges from 0 to 500. However the upper range limit can be changed according to the highest expected level of pollution in Lebanon. The higher the numerical value, the greater the level of air pollution and the greater the health risk. Establishing AQIs depends on a proper air monitoring and reporting system, as well as a comparison to the set national air quality standards. The equation used to determine the AQI is the following:

IP= the index for pollutant P

CP= the rounded concentration of pollutant P

BPHi= the breakpoint that is greater than or equal to CP

BPLo= the breakpoint that is less than or equal to CP

IHi= the AQI value corresponding to BPHi

ILo= the AQI value corresponding to BPLo

D.2 AQI Reporting

AQI can be designed to adopt the following frequency of reporting.

• NO2 1 hour mean (every one hour) • PM10 24 hour mean (from midnight to midnight) • PM2.5 24 hour mean (from midnight to midnight) • O3 1 or 8 hour mean (every hour or every 8 hours, averaging the 1 hour concentration

means over a period of 8 hours) • SO2 1 or 24 hour mean (every hour or from midnight to midnight)

As suggested by the EPA, at the end of each hour, the concentration of each pollutant, measured at each site, is converted into a number ranging from zero upwards using a common index. The calculated number for each pollutant is referred to as a sub-index. The highest index has to be reported all the time, regardless of the type of pollutant. Therefore the pollutant of the highest sub-index would be the dominant pollutant of that day. The number of exceedances is reported annually. They are estimated by calculating the number of daily maximum 1-hour values that exceed the level of the 1-hour standard for each pollutant.

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D.3 AQI color code

If AQI is represented between 0 and 500, 0 will represent good air and 500 represents hazardous air. The AQI can then be broken down into six categories, each color coded with the number scale. For example:

• Good (green) is for numbers 0 through 50, and means satisfactory air quality • Moderate (yellow) is 51-100, and is for acceptable air quality • Unhealthy for Sensitive Groups (Orange) is 101-150, and means sensitive individuals with

sensitive skin may be affected • Unhealthy (red) is 151-200, and almost everyone may experience problems • Very unhealthy (violet) 210-300 is a health alert, where everyone may have health problems • Hazardous (brown) 310-500.

Conclusions and Recommendations

Based on this study, the setup of a monitoring network of air quality in Lebanon (Objective I) requires the installation of at least five monitoring stations. As detailed in Tables C.1-C.7, these stations require a long-term governmental commitment with an initial capital of 172,000 USD for the installation for each station, and approximately 10,000 USD for accessories per station every year, and one technician per three stations. Objective II, on the other hand, focuses on the ability to measure (i) emission from point sources (industrial, hazardous wastes or incinerators and others) and (ii) ambient air in areas affected by these emissions. Opting for the installation of these mobile/portable monitoring stations (Objective II) would reduce the initial budget and give officials at MOE the ability to monitor air pollutants in various locations.

Hence, the recommendation to start a long-term commitment by acquiring a mobile lab fully equipped with monitoring analyzers for the criteria air pollutants, and an open path spectrophotometer for hydrocarbon and other hazardous gases. The mobile lab will be able to move in the different hotspots and stationary sources for initial screening and validation of the proposed model, while the open path will be used to measure gases other than the criteria pollutants in areas where enhanced activities that result in the emission of major hydrocarbons is determined. As a second option, we recommend that Objective I is implemented in phases. Phase I would include the purchase and installation of two stationary stations situated along the Northern coastal line between Beirut and Tripoli. Joining efforts with the American University of Beirut, Universite Saint Joseph and the Faihaa municipality would allow MOE to report, based on at least four monitoring stations, the AQI of Northern Lebanon.

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