urban assessment of air quality

Rapid Urban Assessment of Air Quality for Kathmandu, Nepal

Full Report

Published byInternational Centre for Integrated Mountain Development GPO Box 3226, Kathmandu, Nepal

Copyright © 2012

International Centre for Integrated Mountain Development (ICIMOD)All rights reserved. Published 2012

ISBN 978 92 9115 267 4 (printed)

978 92 9115 268 1 (electronic)

LCCN 2012-323210

Printed and bound in Nepal by

Hill Side Press (P) Ltd., Kathmandu, Nepal

Production team

A Beatrice Murray (Consultant editor)

Isabella C. Bassignana Khadka (Consultant editor)

Andrea Perlis (Senior editor)

Amy Sellmyer (Proofreader)

Punam Pradhan (Layout and design)

Asha Kaji Thaku (Editorial assistant)

Note

This publication may be reproduced in whole or in part and in any form for educational or non-profit purposes without special permission from the copyright holder, provided acknowledgement of the source is made. ICIMOD would appreciate receiving a copy of any publication that uses this publication as a source. No use of this publication may be made for resale or for any other commercial purpose whatsoever without prior permission in writing from ICIMOD.

The views and interpretations in this publication are those of the author(s). They are not attributable to ICIMOD and do not imply the expression of any opinion concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries, or the endorsement of any product.

This publication is available in electronic form at www.icimod.org/publications

Citation: Pradhan, BB; Dangol, PM; Bhaunju, RM; Pradhan, S (2012) Rapid urban assessment of air quality for Kathmandu, Nepal: Summary. Kathmandu: ICIMOD

1. Introduction

1

Bidya Banmali PradhanPradeep Man DangolRajina MaskeySuyesh Pradhan


Full Report

International Centre for Integrated Mountain Development, Kathmandu, Nepal, 2012

The following institutions have provided inputs to this report: National Focal PointMinistry of Environment, Science, and Technology (MOEST)Government of Nepal

National Implementation AgencyInternational Centre for Integrated Mountain Development (ICIMOD) Secretariat, Malé Declaration Regional Resource Centre for Asia and the Pacific (RRC.AP)A UNEP Collaborating CentreKlongluang, Pathumthani, Thailand

Technical SupportIVL Swedish Environmental Research InstituteGöteborg, Sweden

FundingThe Swedish International Development Cooperation Agency (Sida), has funded this study under the Malé Declaration Implementation as part of the Regional Air Pollution in Developing Countries (RAPIDC) programme.

Foreword ii

Acknowledgements iii

Acronyms and Symbols iv

1. Introduction 1 Background 1 Kathmandu Valley 2 Sources of Airborne Pollutants 4 Air Pollution as a Health Risk 5

2. Rapid Urban Assessment 6 Overview 6 Rapid Urban Assessment Method 6 The Kathmandu Study 6 Building the Macro-Scale Emission Inventory 8 Establishing an Emissions Database for Grid-Wise Maps 9 Passive Monitoring 14

3. Results and Discussion 18 Emission Inventory 18 Observations from the Passive Monitoring Campaigns 23 Validating the Emission Inventory 31 Using Population Density Maps to Estimate the Exposure Hazard 33 Limitations 33

4. Conclusion 38

References 39

Bibliography 41

Annexes 42 Annex 1: Details of Study Area and Population 42

Annex 2: Data Sources for Emission Inventory of Kathmandu (2005) 44

Annex 3: The Road Network – Total Traffic and Estimated Emissions Intensity 45

Annex 4: LPG Use in Hotels 45

Annex 5: Calculation of Fuel Consumption 45

Annex 6: Attribute Tables for Area, Line, and Point Sources (screen capture) 46

Annex 7: Passive Monitoring – Particle Deposition and Gaseous Concentrations in the Seasonal Campaigns 49

Annex 8: Passive Monitoring, Results of Year-long Continuous Monitoring at Nine Sites 51

Annex 9: Summarized Results of the Emissions Inventory for the Kathmandu Valley 55

Contents

Foreword

Today, the world’s unprecedented rate of growth and the consequent urbanization, industrialization, and expanding consumerism are creating many environmental problems. The energy required to meet the growing demand, especially in the transport and industry sectors, has come mainly from combustion of fossil fuels, which releases greenhouse gases and air pollutants such as carbon dioxide, methane, and nitrous oxide into the atmosphere. While greenhouse gas emissions are resulting in global climate change and melting of Himalayan glaciers, air pollutants are degrading air quality in urban areas. Air pollution is not only a local problem; it can regional and global implications. Because pollutants respect no boundaries, anyone anywhere in the world can be affected by air pollution, and the lives of thousands of millions of people are at risk because of the associated health impacts. Air pollution also has adverse impacts on crops, biodiversity, infrastructure, cultural heritage, and the natural climate system.

Air pollution is an increasing concern in South Asia, as it has a quarter of the world’s population and some of its fastest urbanization and economic growth. The air quality in many of the region’s major cities is deteriorating at alarming rates, and the transport of air pollutants is exposing the whole region to this risk. According to the World Health Organization (WHO), in urban areas of Asia alone more than half a million premature deaths are linked with degrading air quality every year. Health and environmental impacts of air pollution result in significant economic costs. Estimates of actual damage range from 1 to 3% of the gross domestic product (GDP). The knowledge base on health and environmental impacts of air pollution in Asia has improved, but reliable methodologies are needed to quantify the economic impacts in Asian cities.

Air quality needs to be considered within the larger sustainable development context. At the United Nations Conference on Sustainable Development (Rio+20) in June 2012, governments noted that transportation and mobility are central to sustainable development and that sustainable transportation can enhance economic growth as well as improve accessibility and respect the environment.

In Kathmandu, Nepal, air pollution has emerged as one of the biggest threats to residents. Various studies clearly indicate that Kathmandu’s air quality fails to meet national and international standards owing to the high level of particulate matter in the air.

This publication provides a detailed account of the pollution hotspot areas in Kathmandu. This is the first study done using quantitative data to get an overall picture of the major pollutants. Population density and pollution concentration data are overlaid to provide easily understood maps that will be of particular relevance to policy makers. This study provides an example that can be replicated for other cities.

The publication was prepared by ICIMOD in partnership with the Regional Resource Centre for Asia and the Pacific – A United Nations Environment Programme (UNEP) Collaborating Centre – and the Ministry of Science, Technology and Environment of the Government of Nepal. It was prepared under the Malé Declaration on Control and Prevention of Air Pollution and Its Likely Transboundary Effects for South Asia (Malé Declaration), which calls for regional cooperation to address shared local air quality problems and the increasing threats of transboundary air pollution and its possible impacts. The Declaration also calls for the formulation and implementation of national and regional action plans and protocols based on a fuller understanding of transboundary air pollution issues. Nepal is a member country to the Malé Declaration.

We are particularly grateful to the Stockholm Environment Institute (SEI) and IVL Swedish Environmental Research Institute for providing technical guidance.

This publication underlines the importance of science, assessment, and policy towards attaining a clean environment in Nepal. With it we hope at both policy and practical level to contribute to healthier and cleaner air.

Keshav Bhattarai Ministry of Science, Technology and Environment

Government of Nepal

Dr Jonathan ShawDeputy Director

Regional Resource Centre for Asia and the Pacific – A UNEP Collaborating Centre

Dr David MoldenDirector General

ICIMOD

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Acknowledgements

The team would like to thank the institutions and many individuals who contributed to the preparation of this publication. We especially thank everyone who has enabled us to place passive samplers on their office or home premises. We appreciate the help received from the Department of Archaeology, Government of Nepal, for permitting us to place passive samplers at World Cultural Heritage sites in Kathmandu. Similarly, we are thankful to Kathmandu Metropolitan City, Lalitpur Sub-Metropolitan City, and the Survey Department of the Government of Nepal for providing household-level data and maps of the Kathmandu Valley.

Our sincere thanks go to IVL Swedish Environmental Research Institute – especially Marcus Liljeberg, Milla Malander, and Karin Sjöberg – for providing not only guidance throughout the process of preparing this report, but also training.

We sincerely acknowledge the contribution of our reviewers, Prof. Nguyen Thi Kim Oanh of the Asian Institute of Technology and Martin Ferm, Marie Hager-Eugensson, and Karin Sjöberg from IVL.

This study could not have been prepared without the continuous support and encouragement of the Regional Resource Centre for Asia and the Pacific. Our special gratitude goes to Mylvakanam Iyngararasan and Wah Wah Htoo. Equally we express our appreciation to Dr Johan Kuylenstierna, Director of the Swedish Environment Institute (SEI), who with his team of Dr Harry Vallack and Dr Kevin Hicks provided inputs to the emission database used for the publication.

Extensive editing work by the consultant editors A. Beatrice Murray and Dr Isabella C. Bassignana Khadka was crucial in preparing the publication. The input from ICIMOD’s Integrated Knowledge Management team, especially Andrea Perlis, Senior Editor, is deeply acknowledged. We also thank Punam Pradhan for layout and graphic preparation, Amy Sellmyer for proofreading, and Asha Kaji Thaku for assistance in printing the document.

The overall guidance and support from Basanta Shrestha, Head of the Mountain Environment and Natural Resources Information System (MENRIS) Division, ICIMOD, is deeply appreciated. This report heavily relied on input from MENRIS staff, in particular Gauri Dangol, Govinda Joshi, and Kabir Uddin.

We would also like to thank our former ICIMOD colleague Lokap Rajbhandhari for providing technical guidance.

We highly acknowledge the Ministry of Science, Technology and Environment of the Government of Nepal for providing the overall facilitation needed to bring out this publication.

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Acronyms and Symbols

Cl– chlorine

CO carbon monoxide

Ca2+ calcium

GIS Geographic Information System

ICIMOD International Centre for Integrated Mountain Development

LPG liquefied petroleum gas

K+ potassium

Mg2+ magnesium

Na+ sodium

NH3 ammonia

NH4+ ammonium

NMVOCs non-methane volatile organic compounds

NO nitric oxide

NOX nitrogen oxides

NO3– nitrate

PM10 particulate matter with a diameter of 10µm or less

PM2.5 particulate matter with a diameter of 2.5µm or less

RUA rapid urban assessment

RAPIDC Regional Air Pollution in Developing Countries

RRC.AP Regional Resource Centre for Asia and the Pacific

SO2 sulphur dioxide

SO42– sulphate

TAPM The Air Pollution Model

VOCs volatile organic compounds

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1. Introduction

1

1. Introduction

Background

Nepal is a small country situated between India and China. In the 1970s it was lauded as a pristine Shangri-La between these two industrial giants and a priority destination for tourists. In recent years, migration and urbanization, together with an increase in the population and in the number of vehicles and industry, have contributed to a deterioration in air quality. The air pollution in urban centres is now so serious that it has become an environmental problem which poses a threat to the health and wellbeing of the country’s citizens and a potential deterrent for tourists. The Kathmandu Valley, Nepal’s major urban centre, now has air pollution that is comparable to that of other industrialized cities in Asia such as Bombay, Calcutta, Delhi, and Shanghai (Tuladhar 2008).

The rapid increase in urbanization in the Kathmandu Valley and the concomitant rise in the number of vehicles are responsible for an increased level of polluting gases and solid particulate matter in the ambient air. These pollutants can cause irritation and difficulty in breathing followed by chronic effects such as emphysema, bronchitis, chronic cough, and asthma, and they can also lead to skin problems. Several studies have documented the rise of respiratory diseases in Kathmandu (ICIMOD 2007).

Widespread and effective air quality monitoring programmes are essential for the development of air quality management plans. In developed countries, air quality management plans have been in place for decades, whereas in Nepal the Ministry of Population and Environment (MOPE) only started to investigate air quality in the 1990s; very few studies had been performed before then. In 2002, MOPE set up the first six ambient air quality monitoring stations at different locations in the Kathmandu Valley under the Danish International Development Assistance (DANIDA)-supported project Environment Sector Programme Support. Both this and more recent studies show that the level of ambient total suspended particles and other pollutants at roadside stations in Kathmandu can be higher than the National Ambient Air Quality Standards recommended maxima (GON 2007), except during the monsoon season. The measurements also raise the question of what Nepal’s national threshold limits for these common pollutants should be. This is now under discussion since limits are essential in order to make full use of the monitoring data in air quality management planning for the country.

Nepal needs to be able to assess the air quality in its urban areas. However, while monitoring does now take place on a more regular basis, power failures and other technical constraints still hamper routine monitoring efforts. A complementary approach to monitoring is to use an emission inventory as an assessment tool. The present study describes how air quality can be assessed using a method known as rapid urban assessment (RUA). RUA is a procedure developed for the study of air pollution in urban areas under the Malé Declaration’s Regional Air Pollution in Developing Countries (RAPIDC) programme. In the RUA method, the emission inventory is validated by using a dispersion model to calculate the air concentration contributions from the total calculated emissions and comparing the results to those obtained by passive monitoring. The method was developed to be used anywhere but is being applied in the first instance in Asian countries. It was initially tested in Hyderabad, India. The present case study describes how RUA was used to quantify seven major pollutants in the urban Kathmandu area and to identify the location of the major pollution hotspots.

The results of the study highlighted both the potential usefulness of the method; the limitations for areas like the Kathmandu Valley with diverse topography and climate, lack of separation of commercial, industrial, and residential areas, and limited local data; and modifications needed for application in such areas. It provides a useful test of the method and example for other towns and cities planning to apply it.


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Kathmandu Valley

Geographic setting

The Kathmandu Valley is situated in Nepal’s Central Development Region (Figure 1) within the Bagmati zone, one of the country’s 14 administrative zones. It lies at 1,300 metres above sea level between latitudes 27°32’13” and 27°49’10” North and longitudes 85°11’31” and 85°31’38” East. The valley itself forms a bowl-shaped basin approximately 600 km2 in area and surrounded by the Mahabharat mountain range on all sides. Four hills act as ‘forts’ of the valley: Pulchowki in the southeast, Chandragiri/Champa-Devi in the southwest, Shivapuri in the northwest, and Nagarkot in the northeast. Kathmandu’s geographic features and its unique topography contribute to a marked climatic variation. The temperature ranges from below 0°C in winter to above 30°C in summer (ICIMOD 2007) and westerly and southwesterly winds dominate throughout the year (Sapkota 2004). During the dry winter season, low wind speeds create poor dispersion conditions and make the valley susceptible to air pollution. Over the past ten years, both population growth and an increase in the number of motor vehicles have contributed to a substantial rise in the concentration of airborne pollutants.

Population and urbanization

At the time of the 2001 census, the Kathmandu Valley housed nearly 1.6 million of Nepal’s 20.2 million people (CBS 2001). Nepal’s urban population is increasing at a rate of approximately 6.65% per annum where the annual national growth rate is only 2.3%. It is estimated that by 2050, 46% of the population will be living in cities, compared to less than 9% in 1990. The urban population growth rate in Kathmandu Valley is approximately 4% (CBS 2005). The trend in urbanization is mostly attributed to people migrating people from villages to the cities in search of better opportunities.

Figure 1: Location of the Kathmandu Valley within Nepal

RUA study area

Kathmandu Valley

km

1. Introduction

3

Volume of traffic and the road network

The number of vehicles in the Kathmandu Valley is increasing with industrial and economic development. In the 1990s, the number of vehicles increased 4.2 fold (Pokhrel et al. 1999). According to the Department of Transport Management, the number of vehicles in the Bagmati zone increased to 171,678 by the end of fiscal year 2001/02; the latest registration records available at the time of this assessment (mid-July 2007/08) gave the total number of registered vehicles for all categories in Nepal as 703,044, with 421,826 inside the valley, almost 60% of the total and more than twice as many as in 2001. Figure 2 shows the total number of vehicles registered in the Bagmati zone from 2000/01 to 2009/10, of which almost all were plying the streets of Kathmandu (Magar 2012). The exact number of vehicles in operation is not known since there is no mechanism to track when a vehicle is or isn’t operating (Adhikari 1999).

The large number of vehicles has adversely impacted the air quality of the valley. A study conducted by the Society for Legal and Environmental Analysis and Development Research (LEADERS Nepal) concluded that motor vehicles are mainly responsible for the increased concentration at different traffic intersections of particulate matter of dimensions that can be inhaled (Pokhrel et al. 1999). A part of this is the result of resuspension rather than emission, but more than one-third of the vehicles on the streets of the Kathmandu Valley fail to comply with the emission standards set by the Government of Nepal (Bastola 1998).

Despite the increasing number of vehicles, the total length of road network in the Kathmandu Valley in 2004 was only 1,445 km of which only 834 km was black topped (Table 1).

The roads in urban Kathmandu are not regularly maintained, and most motorable roads Kathmandu are too narrow for the ever-increasing number of vehicles. A 2002 study showed that on average there was less than 4 m of road (of any kind) per registered vehicle (Pokhrel 2002); in 2000 there was less than 3 m of black topped road per vehicle (LEADERS Nepal 2000), which dropped to less than 2 m in 2004, with less than 3.5 m for any type of road. The sub-standard condition of the road, with much of it not paved, also contributes to air pollution through resuspension and the effects of traffic jams (Adhikari 1999).

Recent industrial growth

Over the past two decades, the economy has diversified and Kathmandu now has a much larger industrial base. In 2001, the valley housed about 25% of all the industries in Nepal (UNEP 2001); this number is now closer to 38% (ICIMOD 2007). According to the Department of Cottage and Small Industries, the Kathmandu Valley at present houses 14,791 industrial units of which 10,527 are in Kathmandu; 2,933 in Lalitpur; and 1,331 in Bhaktapur.

0

100

200

300

400

500

600

2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10

Num

ber

('000

)

Year

Figure 2: Vehicle registration data for the Bagmati zone

Source: Magar 2012

Table 1: Roads in the three major urban centres of the Kathmandu Valley

Major urban centre Type of road

Total (km)

Black topped (km)

Gravelled (km)

Earthen (km)

Kathmandu 567 172 176 915

Patan (Lalitpur) 163 90 95 348

Bhaktapur 104 40 38 182

Total 834 302 309 1,445

Source: GON 2007


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These industries are responsible for generating stack and fugitive emissions which are among the primary sources of air pollution. Out of the 125 industries identified as point sources for pollution in Nepal, 105 are serious polluters (NECG 1990). According to one study, 3,156 industries were classified as air polluting, 47.5% of them located in the Kathmandu Valley. Of these, brick making and cement industries were found to be the main industrial polluters (Devkota and Neupane 1994; Shah and Naqpal 1997; Sapkota 2005). In response to this, cement industries and moving bull trench kilns were banned inside the valley and have been replaced either by fixed chimney or vertical shaft brick kilns which are less polluting.

Sources of Airborne Pollutants

The most common airborne pollutants are sulphur dioxide (SO2), nitrogen oxides (NOX), carbon monoxide (CO), particulate matter (PM10 with a diameter of 10 µm or less and PM2.5 with a diameter of 2.5 µm or less), non-methane volatile organic compounds (NMVOC), ammonia (NH3), ozone, and heavy metals. The sources of these pollutants in the urban and industrialized c entres of Nepal are the same as those found in such centres all over the world.

Sulphur dioxide

The most significant man-made sources of SO2 are from the combustion of sulphur-containing fossil fuels. Both the short and the long-term effects of SO2 on human health are well documented and include a higher incidence of respiratory diseases, especially when the exposure is high and when this is accompanied by a concomitant high concentration of fine particles (PM2.5). The major source of SO2 emissions in Kathmandu is fuel combustion. The industries that produce SO2 emissions include hotels and brick kilns.

Nitrogen oxides

Oxides of nitrogen are formed when atmospheric nitrogen is exposed to high temperatures – this includes all types of combustion processes. Oxides of nitrogen, particularly nitrogen dioxide, are toxic. NO2 is known to have both short and long-term detrimental effects since it impedes pulmonary function. Nitric oxide (NO) is produced as a by-product of combustion and is further oxidized to nitrogen dioxide (NO2) when it reacts with oxidants (predominantly ozone) present in ambient air. The main sources of nitrogen oxides are motor vehicles and other high-temperature combustion processes used in industrial work.

Carbon monoxide

CO is a colourless, odourless, and tasteless gas with relatively poor solubility in water. Carbon monoxide binds strongly with the haemoglobin in blood and reduces the blood’s oxygen carrying capacity. It is so toxic that even short-term exposure is of concern. CO is produced by the incomplete combustion of fossil fuels and the greatest emissions are in the exhaust of internal combustion engines, especially from vehicles with petrol engines.

Non-methane volatile organic compounds

Organic chemical compounds (excluding methane) are collectively known as non-methane volatile organic compounds. NMVOCs include compounds such as benzene, xylene, propane, and butane. Significant sources of emissions are wetlands, landfill sites, and industry; wood combustion may also play a role. NMVOCs are released into the atmosphere as a by-product of transportation; as a result of industrial processes wherever organic solvents are used and where paints, varnishes, and chemicals evaporate; whenever oil tankers load and offload; and when tobacco is smoked.

NMVOCs are significant greenhouse gases via their role in creating ozone and in prolonging the life of methane in the atmosphere, although the effect varies depending on the local air quality. NMVOCs such as the aromatics benzene, toluene, and xylene are suspected carcinogens that may induce leukaemia through prolonged exposure; 1,3-butadiene is another dangerous compound which is often associated with industrial uses. Some NMVOCs

1. Introduction

5

react with nitrogen oxides in the air in the presence of sunlight to form ozone. Ozone is beneficial in the upper atmosphere because it absorbs harmful ultraviolet radiation from the sun; however, in the lower atmosphere it can pose a health threat because it causes respiratory problems and at high concentrations can damage crops and buildings.

Ammonia

Ammonia is present even in unpolluted air because it is the product of natural biochemical and chemical processes. The sources of atmospheric ammonia include emissions from microorganisms involved in the decay of animal matter and in sewage treatment, emissions from coke and ammonia factories, and leakage from ammonia-based refrigeration systems. Ammonia in polluted atmospheres is key to the formation and neutralization of nitrate and sulphate aerosols. Ammonia reacts with these acidic aerosols to form ammonium salts which are some of the more corrosive atmospheric aerosols.

Particulate matter

PM10 roughly corresponds to the thoracic fraction of suspended particles, that is, those particles that can penetrate beyond the upper nose and throat. Particulate matter originates from a wide variety of processes, ranging from simple grinding of bulk matter to complicated chemical and biochemical processes. PM10 can have a wide range of impacts from effects on the climate (either alone or in combination with gaseous pollutants), to direct detrimental effects on human health. Particulate matter may cause damage to materials, reduce visibility, and cause undesirable aesthetic effects.

PM2.5 corresponds to the fraction of inhalable particles that can lodge deep down in the respiratory bronchioles of human lungs; they are particularly harmful to high-risk populations such as children and adults with pulmonary diseases. In the Kathmandu area, PM2.5 constitutes about 60-65% of the PM10 emissions.

Air Pollution as a Health Risk

Air pollution is a major environmental health risk, it affects both developed and developing countries around the world, and Nepal is no exception. Air pollution is associated with an increase in respiratory and cardiovascular diseases. On a global scale, 4–8% of premature deaths, about 537,000 daily, are attributed to exposure to ambient particulate matter (Haq et al. 2002; WHO 2002). Approximately 20–30% of all respiratory diseases appear to be caused by air pollution (WHO 2000).

High concentrations of pollutants in the lower atmosphere are a health risk to the residents of the Kathmandu Valley, especially during the dry winter months (Pokhrel and Sharma 1998; Pokhrel et al. 1999). According to previous air pollution inventories, the main pollutants in the Kathmandu Valley are particulate matter, carbon monoxide, and sulphur dioxide (Gautam 2006). In spite of the potential health risks to the residents of the Kathmandu Valley, there have been very few studies on the impacts of air quality. Long-term epidemiological studies are almost non-existent (ICIMOD 2007); however, a citizen’s report published by LEADERS Nepal indicates a rise in the incidence of respiratory disorders; in the reporting of eye, throat, and skin problems; and in the incidence of cardiovascular diseases in Kathmandu (Pokhrel et al.1999). This study indicated that the number of respiratory-related cases treated in hospitals is highest among Kathmandu’s urban residents and suggested that this can be attributed to the poor ambient air quality.

Acute respiratory infection is one of the top five diseases reported in Nepal (UNEP 2001). Up to 16.5% (156,483 patients) of all hospital visits during 1996/97 reported for the three major municipalities of the Kathmandu Valley were for respiratory-related problems (Pokhrel and Sharma 1998). The Ministry of Environment, Science, and Technology estimated that in 2005 ambient air pollution was probably responsible for up to 1,600 premature deaths in the Kathmandu Valley. Clean Energy Nepal/Environment and Public Health Organization (CEN/ENPHO), estimates that about NPR 30 million (approximately USD 400,000) in hospital costs could be saved annually by reducing Kathmandu’s PM10 level to meet WHO guideline values (CANN 2008).


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2. Rapid Urban Assessment

Overview

The poor air quality in many South Asian cities is a consequence of industrialization, urban growth, and increased population as a result of migration. Air quality must be improved to enable sustainable growth since poor air quality impacts both human health and the environment, entailing high financial costs. Many countries that have developed an effective air quality management approach have discovered that the benefits far outweigh the costs (WHO 2000).

In order to implement mitigation measures, it is first necessary to establish a monitoring system. Monitoring helps planners to decide on cost-effective measures aimed at decreasing exposure and reducing damage in defined geographic areas. Several monitoring approaches are available; however there are a number of constraints to air quality monitoring in South Asia including the cost, the lack of specialized equipment, and the lack of skilled personnel. Practical considerations also limit routine monitoring: for example, power supplies are often disrupted preventing long-term continuous monitoring, and instruments are often not sufficiently robust or adaptable for the conditions.

Rapid Urban Assessment Method

A rapid urban assessment (RUA) method has been developed to study air pollution in urban areas of developing countries, resourced through the RAPIDC programme prepared and implemented by the Stockholm Environment Institute. This is an inexpensive method with a fast turnaround time which can be used to locate hotspot areas and identify the major pollutants and their concentrations. The RUA gives results that are comparable to those obtained with traditional approaches, but has the advantage of being both quicker and less costly. The RUA was initially tested in Hyderabad, India.

The RUA was chosen for Kathmandu as an inexpensive method to be used as an initial tool for starting up the air quality management process. However, it was not possible to apply the method used for Hyderabad in its entirety for Kathmandu since the two cities have quite different structures. Hyderabad is a relatively clearly delineated city with well-defined low-income areas, mid-income areas, high-income areas, commercial building areas, banking areas, and so on; in contrast, in Kathmandu commercial, industrial, and residential areas are highly integrated. For example, a typical building in Kathmandu can have business activities on the ground floor, administrative or business activities on the second floor, and a residential flat on the top floor. Areas have a similar mix, for example, with small factories directly adjoining clusters of houses and shops. Thus a modified and revised version of the approach used for Hyderabad was developed for Kathmandu. The method was developed in close collaboration with IVL Swedish Environmental Research Institute, and was implemented in close collaboration with the Nepal Ministry of Environment and United Nations Environment Programme Regional Resource Centre for Asia and the Pacific (UNEP/RRC.AP), and with the support of the Swedish International Development Cooperation Agency (Sida) under the framework of the Malé Declaration on Control and Prevention of Air Pollution and Its Likely Transboundary Effects for South Asia.

The Kathmandu Study

Seven major air pollutants were tracked in the study: SO2, NOX, CO, NMVOC, NH3, PM10, and PM2.5. The main components of the RUA are an emission inventory (i.e., identification of sources and their geographical distribution using satellite images and GIS software, and a calculation of likely emissions based on estimated emission factors), dispersion modelling based on the emission inventory database, cross-validation with low-cost monitoring data, exposure analysis, and health risk assessment. The major output of this method is a series of maps which help in identifying the major hotspot areas in terms of types of major pollutant and levels of concentration. Figure 3 shows the flow diagram of the RUA method for Kathmandu.


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Figure 3: Flow diagram for RUA Kathmandu

Emission inventory SO2, NOx, CO, PM2.5, PM10, NMVOC, NH3

Passive monitoring SO2, NO2, PM, O3, HNO3, Cl–, SO4

2–, HN4

+, Ca2+, Mg2+, Mg2+, Na+, K+

Line sources Road network

Estimate the household number/unit area

Find the total length of each category of road per grid

Calculate total emissions for road sources in each grid

Calculate total emissions for all area sources in each grid

Find the total area of each type of area source per grid

Point sources Hotels, brick kilns

Compare dispersion modelling maps with passive monitoring maps

Conduct monitoring campaigns (expose samplers)

• Rainy and dry season• One year continuous

Analyse samplers (IVL)

Calculate the emission factors for different sources

Use satellite images to identify land cover classes (Quick Bird 06 + Arc GIS 9.2)

Overlay the grids for point, line, and area sources and generate grid-wise pollutant concentration maps by summing the

emissions from all sources in each grid

Area sources Identify built up areas in 13 land cover classes

Use ward-level data to estimate population density per grid

Calculate concentration of each pollutant per m2 area

Calculate macro-level emissions from statistical data

Generate 100 x100 m2 RUA-Kathmandu grid

Calculate total emissions for all hotels in each grid

Rapid urban assessment for Kathmandu

Grid-wise pollution maps

Use dispersion modelling to introduce meteorology

Generate GIS pollutant concentration maps


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RUA-Kathmandu study area

The Kathmandu Valley comprises five municipalities: Kathmandu Metropolitan City, Lalitpur Sub-Metropolitan City, and Bhaktapur, Madhyapur, and Kirtipur Municipalities. The RUA-Kathmandu study area covered two of the largest municipalities: Kathmandu Metropolitan City and Lalitpur Sub-Metropolitan City (Figure 1). Kathmandu Metropolitan City covers an area of 51 km2 and in 2001 had 35 wards with 152,155 households and a population of 669,846. Lalitpur Sub-Metropolitan City, known locally as Patan, is situated on an elevated tract of land south of the Bagmati River. Patan is the fourth largest city in the country, it covers an area of 15 km2 and in 2001 had 22 wards with 35,000 households and a population of 162,997. Detailed information on ward areas and number of households is given in Annex 1.

Building the Macro-Scale Emission Inventory

Top-down and bottom-up approaches were combined to build the macro-scale emission inventory. In the top-down statistical approach available inventories for a greater emission area were taken and disaggregated to sub-units using actual data for the source strength of emission generating activities. The bottom-up approach used data from baseline statistics, for example, traffic counts, distance travelled by individual vehicles, and type of fuel, were used to define total emissions from the transport sector.

The emission inventory for Kathmandu was based on the common methodology developed by IVL Swedish Environmental Research Institute for countries in South Asia. This uses the Malé Emission Inventory Work Book Version 2.4, which mainly uses emission factors prescribed by the Intergovernmental Panel on Climate Change (IPCC). The process and results are described in the 2005 Report on Emission Inventory – Kathmandu (ICIMOD 2005).

In this method, the sources of emission are attributed to six different activities:

• energy (combustion activities, non-combustion fugitive emissions from fuels, and transport-related activities), • industrial processes,• solvent and other product use,• agriculture,• vegetation fires and forestry, and • waste.

The emission values used to quantify total pollutant emissions were mainly obtained from government sources. The sources are listed in Annex 2. The present assessment mainly used a top-down approach, with primary data where needed.

Calculating emissions and emission factors

The general equation for emission estimation is

emissions = (emission factor) * (activity rate)

The emission of pollutants was quantified in terms of kilotonnes per year. The emission factors were taken from the Malé Emissions Inventory Workbook Version 2.4. The activity level was derived from the fuel consumption data and the net calorific value of the fuel.

For example, the emission of SO2 depends on the activity rate (i.e., how much fuel is used), the emission factor, and the overall emission reduction efficiency. The equation for SO2 emissions is

E = A * EF (1 – ER),

where

E = emission of SO2

A = activity rate (fuel consumption) EF = emission factor for SO2 ER = overall emission reduction efficiency (expressed as a number 0–1)


9

EF, the emission factor for SO2, depends on the sulphur content of the fuel, sulphur retention in the waste ash, and emission control efficiency of the fuel combustion, along with net calorific values of the fuel. The emission factor for SO2 emissions is calculated as follows:

EF = 1 B100)((1 C

100)106A

E2B100{ }* * *

where

EF = emission factor for SO2

A = activity rate B = sulphur content C = sulphur retention in ash (%) E = emission of SO2

The sulphur content for fuel combustion is specific to the sector; estimates for each sector are given in the Malé Emissions Inventory Workbook Version 2.4 (see sample page in Figure 4).

The emission loads from the macro-scale inventory of emissions for each pollutant were used to produce the pollution grid maps for the RUA-Kathmandu as discussed below.

Establishing an Emissions Database for Grid-Wise Maps

Three different types of sources of emission were identified: area sources like buildings and water bodies, line sources like roads, and point sources like brick kiln flue stacks. The position of these sources was determined through analysis of satellite images. The land cover mapping, spatial data preparation, and analysis were performed using Arc GIS 9.2 software. The complete process undertaken for the assessment is described in the following sections:

Figure 4: Snapshot of a page from the Malé Emissions Inventory Workbook Version 2.4

Template prepared by: Stockholm Environment Institute at York (SEI-Y), UNEP RRC-AP and SACEP Date last modified: 1/28/2008

Malé Emissions Inventory Workbook Template - Version 2.4 Prepared within the Sida-funded Regional Air Pollution In Developing Countries (RAPIDC) programme as a contribution towards the Implementation of the Malé Declaration on Control and Prevention of Air Pollution and its Likely Transboundary Effects

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Date last modified: 1/28/2008

User must enter inventory details here:Inventory year: 2000

Region: South Asia

Country: Nepal

Province: Nepal

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MENU OVERVIEW

Menu1 Sectors 1. to 4. Fuel combustion activities

Menu2 Sector 5. Fugitive emissions (non-combustion) for fuels

Menu3 Sector 3. Fuel combustion activities. Sector: Transport (Detailed method)

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Menu4 Sector 6. Industrial processes (non-combustion) emissions

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Sheet 9 Summary sheet - Annual emissions of each pollutant by source sector

References

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If the green 'GO' buttons do not work, it means your security level is set too high. Click on Tools, Macro, Security and seleof security.

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Image interpretation using remote sensing data

Image interpretation was performed using a mosaic of high resolution satellite images (Quick Bird, Google Earth Pro) for January 2006. On-screen digitization was performed for area, line, and point types of emission source.

Area sources

An area source is a two-dimensional source of diffuse air pollutant emissions, for example, the emissions from a forest fire, a landfill, or the evaporated vapour from a large spill of volatile liquid. Thirteen classes of land use were classified and digitized as area sources of emissions for the RUA-Kathmandu:

Class 1: Built-up area, coverage 0–25%Class 2: Built-up area, coverage 25–50%Class 3: Built-up area, coverage 50–75% Class 4: Built-up area, coverage 75–100%Class 5: Parking areaClass 6: Water bodyClass 7: ForestClass 8: Open fieldClass 9: Agricultural fieldClass 10: TempleClass 11: Industrial areaClass 12: Green area other than forest (grassland, shrubs, etc.)Class 13: Airport

Figure 5 shows the distribution of these classes in the study area.

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Figure 5: Map of the RUA-Kathmandu area showing area sources (classes 1–13) line sources (classes 1–5) and point sources

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Line sources (traffic intensity)

Point sources

Built-up, coverage 0–25%Built-up, coverage 25–50%Built-up, coverage 50–75%Built-up, coverage 75–100%Parking areaWater bodyForestOpen fieldAgricultural fieldTempleIndustrial areaGrassland, shrubs, etc.Airport

High

Low

4–5 Star hotel2–3 Star hotel1 Star hotelSmall hotelBrick kiln


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Line sources

A line source is a one-dimensional source of air pollutant emissions, for example, the emissions from vehicular traffic on a roadway or from aeroplanes. Traffic is the main line source in the Kathmandu Valley, thus the road network was used to estimate emissions due to line sources. The road network was digitized and categorized into five classes in terms of traffic intensity with classes 1, 2 and 3 representing major roads and classes 4 and 5 minor roads. Traffic counts were conducted for each of these classes (see Annex 3). Figure 6 shows the road network in the RUA-Kathmandu area

Point sources

A point source is a single, identifiable source of air pollutant emissions, for example a combustion furnace flue gas stack. Point sources are further characterized as elevated or at ground-level. A point source has no geometric dimensions. Brick kilns and hotels were the main point sources considered for the RUA-Kathmandu.

Out of the total 1,000 hotels, 143 were digitized and sorted into four classes as follows:

Class 1: 5 star and 4 star hotelsClass 2: 3 star and 2 star hotelsClass 3: 1 star hotelsClass 4: Other hotels, lodges, and restaurants

The classification is based on energy use, specifically, the number of cooking gas cylinders used per hotel per month. A primary survey of 150 hotels was used to help establish the classification; see Annex 4 for details.

Brick kilns were also digitized, but these point sources lay beyond the studied grid area, thus their emissions were not taken into account in the assessment. Figure 5 shows the position of point sources in the RUA-Kathmandu area (and brick kilns in the surrounding area).

Figure 6: Road network in the RUA-Kathmandu area

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Balkhu

Balaju

ICIMOD

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BouddhaLazimpat

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Gaushala

ChabahilBaluwatar

Maharajgunj

Putalisadak

Tribhuvan University, Kirtipur

Singh DurbarNew Baneswor

Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune

Kathmandu Durbar Square

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Legend!( Sampling points

Road typeFeeder roadHighwayMain foot trailsMajor roadMinor foot trails


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Preparing the data

The emission inventory was based on a geographic disaggregation of the total emissions for the Kathmandu Valley. It is important to have high quality total emission data since these totals are the ones that will be distributed geographically. The total emissions for SO2, NOx, CO, NH3, NMVOC, PM10, and PM2.5 were calculated from fuel consumption statistics (Annex 5). In some cases, activity data such as the number of vehicles and the number of flights were also used. Thus, the emission inventory for Kathmandu was both top down and bottom up.

Top-down emission data: Using the Inventory Workbook

The emission inventory provided the total estimated emission of selected pollutants from different emission sources for the study area; these were then distributed in a defined grid area as prescribed in the emission inventory guidelines, which were based on the Malé Emissions Inventory Workbook Version 2.4 adapted for South Asia.

Bottom-up data collection: Surveying the area

A survey, or bottom-up approach, was used to fill in gaps where the emission data was not adequate as follows:

� Field visits and surveys were conducted to obtain information on fuel use and other activities.

� Field visits to urban, suburban, and other built-up areas were conducted in order to help differentiate buildings as private residences, commercial buildings, institutional buildings, and so on. This information was used to help estimate other similar areas.

� Surveys were conducted for point sources to identify what type of fuel they use and how much they consume.

� Surveys of roadside fast food stalls were conducted to assess how many there are, what type of cooking fuel they use (gas/kerosene/charcoal) and how much fuel they consume. These surveys included fast food stalls, which are now seen more and more frequently by the roadside in busy market areas and near hospitals, since they also contribute to ambient air pollution.

� Traffic counts were conducted at different traffic intersections to estimate the volume of traffic; this information was used to classify the roads into five classes as discussed above. Counts were conducted at 24-hour intervals on both working days and holidays. The volume of taxi traffic in and out of the airport area was also surveyed.

Once the emission inventory data set was established, the data were further processed as follows.

Spatial disaggregation of emissions

Emission data were disaggregated by land use type to relate the emissions to the areas where they took place.

Area sources – built-up areas

The households in each ward in the RUA-Kathmandu study area (Annex 1) were identified as using a particular type of fuel. Households mainly use liquefied petroleum gas (LPG), kerosene, wood, charcoal, and occasionally rice husks for cooking; restaurants and institutional buildings mostly use only LPG and kerosene. Electricity is rarely used for cooking since it is expensive compared to LPG and kerosene and frequent power cuts make it an unreliable source.

A household index was introduced in order to differentiate between residential, commercial, and institutional dwellings since it was assumed that there is a correlation between the amount of fuel used and the type of dwelling. The household index was applied to the entire ward regardless of the exact location of the buildings – it is a figure which estimates the average household in each digitized polygon.

number of households Household index = ward area


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Fuel consumption and emissions were estimated using this index. For example, it was estimated that a restaurant uses ten times more fuel than a residential building, and that an institutional building uses on average half as much fuel as a residential building,

The emission factors were calculated for each pollutant from the emission amounts given in the emission inventory for the residence sector using the method from the Malé Emission Inventory Work Book Version 2.4. The total emitted amount in kilotonnes per year from the Malé Work Book was converted to tonnes per year and divided by the total number of households in Kathmandu (152,155) and Patan (35,000) to yield the pollutant emitted per household per year in the RUA-Kathmandu study area.

Line sources

The total number of vehicles registered for the Kathmandu Valley was obtained from the Department of Traffic Management in Kathmandu.

Point sources

Point sources are specific industries, hotels, and brick kilns.

Grid construction using area, line, and point sources

The area covered by the Kathmandu study was disaggregated into 100 m x 100 m grids. The emissions from area, line, and point sources were quantified in terms of this grid as described below.

Area sources

For each polygon that included built-up areas, the percentage of area covered by buildings was estimated and the polygon was classified as belonging to land cover class 1, 2, 3, or 4. The values were entered into the attribute table (this is the distribution factor). The buildings contained in each polygon were classified as residential, commercial, or institutional. For polygons that did not consist of built-up areas (land cover classes 5–13) the entire polygon was classified as a single type. Emissions estimation was carried out as follows.

Built-up area sources (land cover classes 1–4). The number of dwellings in each polygon was estimated using the coverage factor and the type (residential, commercial, and institutional) was estimated using the household index described above. The digitized polygons were overlaid with the ward layer. The resulting output layer contained different land use classes in each ward. The area covered by dwellings was multiplied by an emission factor (according to the average type) to calculate the area emissions from each grid cell, as shown in the flow diagram in Figure 3.

Other land cover classes (5–13). The emissions per unit were area calculated by dividing the total emissions for a particular pollutant by the total area (in each land use class). Emissions from different land use types (other than built-up areas) were calculated by multiplying the area of each by the appropriate emission factor.

Total area source emission estimation. A new polygon layer with a 100 x 100 m2 grid was created and overlaid with the layer containing the emissions database for all land use types. The total emissions for one grid were calculated by summing the emissions for all land use classes in that grid. Annex 6(a) shows a screen capture of the attribute table for the area source emissions calculations.

Line sources

The line sources were roads. These were classified into five categories; the total length of each type was calculated by adding the road ID to the road length.

The traffic density for each category of road was estimated from the vehicle count survey. The emission factor was calculated by using the traffic density for each category of road (Annex 5). The emissions for each type of road were calculated by multiplying the road length by the emission factor for each pollutant. Annex 6(b) shows a screen capture of the attribute table for line sources.


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The [INTERSECT] operation in ArcGIS 9.2 was used to overlay the grid layer and the road layer to produce an output layer with features with attribute data from both layers. The result was a table with all road segments where the number of records in the table equals the number of road segments. Grid ID, road ID, and road segment length are all included in the table for each record. The estimation of pollutants in each grid was calculated by summing the pollutants using the grid ID.

Point sources

The emission factors for hotels were calculated according to the amount of energy they use in comparison to standard households. The estimate of how much each category emits was based on a primary survey (Annex 4). The emission from each of the four categories was calculated as follows:

• Category 1 emits 100 times more than a household; • Category 2 emits 50 times more than a household;• Category 3 emits 20 times more than a household; and • Category 4 emits 10 times more than a household.

The point layer was overlaid with the grid layer using the [INTERSECT] operation in ArcGIS 9.2. The pollutants in each grid were estimated by summing the amount of pollutants using the grid ID and this information was saved in a database file. Annex 6(c) shows a screen capture of the attribute table for emissions from hotels.

Total emission calculation for pollutant concentration maps

The emissions from point, line, and area sources were joined together on the grid layer using the [JOIN] operation in Arc GIS 9.2. The total emissions in each grid were calculated by summing up the pollutants emitted from each source.

Passive Monitoring

The results of the RUA were compared with actual measurements of selected pollutants obtained using simple passive samplers over a limited period. The basic approach is described in the following sections. The general methodology is described in more detail in UNEP/WHO 1994.

An introduction to passive samplers

Passive or diffusion samplers are single-use pieces of equipment designed to absorb or attach to the pollutant under study over a given length of time. The quantity of pollutant absorbed or attached is later analysed in a laboratory. For gases, the basic principle on which they operate is molecular diffusion, with molecules of a gas diffusing from the open end of the sampler to the absorber end of the sampler. In particulate samplers, particles in the air simply adhere to a surface, and the total weight of particles and their chemical composition is analysed.

The development and use of passive samplers originated in the field of occupational exposure monitoring. Later, diffusion sampling techniques were developed and used for ambient air quality monitoring of gaseous pollutants such as SO2, NO2, and O3. Passive monitoring techniques provide high-resolution spatial monitoring and are also lightweight, inexpensive, robust, and easy to operate; the samplers can be transported to other laboratories, stored for several weeks, and left unattended during sampling. These samplers are the method of choice for developing countries like Nepal since they do not require an uninterrupted power supply, the monitoring is passive, and there is no need for on-site power and pumping of air. The samplers are also relatively low-cost and do not require expensive instrumentation or skilled personnel.

Diffusion samplers are generally designed either in a tube-type configuration with one end open (Palmes tubes), or in a shorter badge-type configuration in which the open end is protected by a membrane filter or other wind screen. In either case, the closed end contains an absorber for the gaseous species to be monitored. Several different types of commercial diffusion tubes are available; some examples are shown in Figure 7.


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Choice of samplers

Passive samplers have been validated in previous studies (Ferm and Rodhe 1997; Ferm and Svanberg 1998; Ferm et al. 2005). They have many advantages including that the measuring range is high and averages are obtained without interruption. In the current study, badge type samplers from IVL were used to collect samples for analysis of SO2 and NO2 (Figure 8). The measuring range for SO2 was about 0.1 to 50 µg/m3 and for NO2 about 0.05 to 50 µg/m3. Passive samplers were used to collect samples of particulate matter (Ferm et al. 2006). These samplers are used to measure the total deposition of particulate matter to a surrogate surface, but do not distinguish between particulate fractions of different diameters. The samplers were attached to an aluminium arm and mounted under a metal disc which acted as a rain shield, as shown in Figure 9.

Figure 7: Different types of commercial passive monitors showing tube type and badge type diffusion tubes

ddouble-ended

c

cover

ba

AbsorbentDiffusion membrane/baffle

cross sectione

a. Open-ended diffusion tubeb. Shorter diffusion tube with diffusion membrane at opening

Source: Based on Cox 2003

c. Badge-type with diffusion membrane at openingd. Double-ended badge with baffles at openinge. Cylindrical badge with tubular diffusion membrane

Figure 8: Badge type passive samplers from IVL, Sweden

PM

HNO3 O3

SO2

NO2

Figure 9: Exposure of IVL passive samplers mounted with metal discs as rain shields


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Figure 10: Sites in the Kathmandu Valley where samplers were exposed

Traffic intensity (road)

Corrosion monitoring siteSampling pointBrick kiln

High

Low

4–5 Star hotel2–3 Star hotel1 Star hotelSmall hotel

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Figure 11: Typical sampling locations in Kathmandu

Institution (Tribhuvan University Hospital) Roadside (Dilli Bazaar)

Temple (Indrachowk) Pond (Rani Pokhari)


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Deployment of samplers

Two intensive seasonal campaigns

Sixty-five sampling sites were selected to cover the inventory area in the Kathmandu Valley. The locations of the sites are shown in Figure 10 and some photos of typical sampling locations in Figure 11. Seasonal variations have been observed in previous monitoring studies (MOEST 2006). Thus in order to test for seasonal variations, sampling was performed in two campaigns, one during the rainy season and one during the pre-monsoon (dry) season. All passive samplers were analysed at IVL in Sweden.

The first campaign was carried out from July to September 2007 in the rainy season and exposed 52 diffusion samplers for gases (17 sites for SO2; 35 for NO2) and 58 deposition samplers for particulates. The second campaign was carried out from February to April 2008 in the pre-monsoon dry season and exposed 58 diffusion samplers for gases (21 sites for SO2, of which 17 were the same as in the first campaign; 37 for NO2, of which 31 sites were the same as in the first campaign) and 65 deposition samplers for particulates (of which 7 were lost due to high winds, leaving a total of 55 for analysis). See Annex 7 for detailed information on the monitoring sites and results.

Year-long continuous monitoring

In addition to the two seasonal monitoring campaigns described above, monitoring was also performed at nine sites continuously for one year (November 2006 to November 2007). In this continuous monitoring programme, passive diffusion samplers were used to calculate the bi-monthly average concentrations of SO2, NO2, O3, and HNO3. Deposition of particulate matter was measured simultaneously at the same sites using a surrogate surface. These continuous measurements were part of a RAPIDC project on corrosion, and the measurements were conducted near cultural heritage sites. See Annex 8 for detailed information on the monitoring sites and monitoring data.


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3. Results and Discussion

Emission Inventory

Macro-scale emissions

Table 2 shows the estimated amount of the studied pollutants emitted from the different sectors in the RUA study area and Figure 12 shows the proportional contribution by different sectors to the total. Combustion in manufacturing industries was the main contributor to SO2 emissions, and transportation was the main source for NOx emissions. Transportation and combustion in other sectors were the main sources of CO and NMVOC, while transportation, including resuspension, was by far the most important contributor to particulate emissions. Agriculture and waste management activities accounted for most of the NH3 emissions.

RUA grid-wise emission maps

The grid-wise RUA-Kathmandu emission maps for SO2, NOx, CO, NMVOC, NH3, PM10, and PM2.5 are shown in Figures 13 to 19. The range of emissions for each pollutant for different grids is summarized in Table 3. The grid-wise maps show the major hotspots for the emissions of each pollutant within the study area. Pollution hotspots are generally located along main roads, at the city core, and near industrial centres. Hotspots for specific pollutants are discussed in the following sections.

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Figure 12: Sector-wise total of anthropogenic emissions from fuel use in Kathmandu

Table 2: Estimated emissions of pollutants for the Kathmandu Valley (based on the emission inventory)

Sector Sub-sector Total emissions (kilotonnes of pollutant per year))


Combustion in the energy industries

Public electricity 0.021 0.114 0.003 0.001 – 0.001 0.001

Combustion in the manufacturing industries

Brick kiln, other petroleum products

3.014 0.838 0.389 0.053 – – –

Transport Road and air 0.388 11.371 20.051 5.086 0.092 85.199 13.472

Combustion in other sectors

Commercial, residential, forestry, etc.

0.655 0.818 40.121 3.945 0.523 1.058 0.852

Industrial processes Food, drinks, and mineral products

– – – 0.787 – 0.318 0.541

Agriculture Agriculture residues burning, fertilizer, and manure management

0.008 0.089 1.057 0.086 1.705 0.087 0.087

Waste Human waste and incineration

0.011 0.066 0.928 0.074 1.298 0.420 0.384

Total anthropogenic emissions

4.097 13.297 62.548 10.032 3.618 87.083 15.338


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Sulphur dioxide

Emissions of SO2 ranged from 0.01 to 19.00 tonnes per year per grid. The major hotspots of emissions are the city core areas where the population density is high and where many of the city’s hotels are located (Figure 13). The effect of brick kilns was not included since these lay beyond the study area.

Nitrogen oxides

Transportation was the main source of NOX emissions accounting for more than 87% of the total NOX emissions. Other sources were domestic combustion and combustion associated with air traffic. The hotspots of NOX emissions were high traffic areas, the city core, and the airport (Figure 14).

Figure 12: Sector-wise total of anthropogenic emissions from fuel use in Kathmandu

Table 3: Range of emissions for different pollutants in the RUA-Kathmandu study

Pollutant

Emission range (tonnes/year/grid)

Minimum Maximum

SO2 0.01 19.00

NOx 0.01 25.00

CO 0.80 995.75

NMVOC 0.10 157.00

NH3 0.01 20.00

PM10 0.02 44.16

PM2.5 0.04 33.00

Figure 13: Map of SO2 emissions in the RUA-Kathmandu study area (tonnes/year)

km

HotelBrick kiln

SO2 tonnes/year

0.01

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Traffic intensity


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Carbon monoxide

Approximately 60% of all CO emissions were produced by residential combustion and 37% by the transportation sector. The CO emission hotspots were densely populated city areas and the environs of hotels (Figure 15).

Non-methane volatile organic compounds

Approximately 50% of the NMVOCs emitted in Kathmandu were from residential combustion, 20% from fugitive emissions and industries, and 10% from agriculture and waste. The main NMVOC hotspots areas were the Balaju industrial area (northwest of the city centre), the main road networks, and city core areas (Figure 16).

Ammonia

Agriculture (>50%) and waste (>33%) were the main contributors to atmospheric ammonia emissions. The ammonia pollution hotspots were at the periphery of the study area (Figure 17).

Particulate matter of aerodynamic diameter less than 10 µm

The transportation sector was the major emitter of particles. The calculated values included resuspension. Other contributing sectors were industries, residences, and waste incineration. The PM10 pollution hotspots were along the main roads and in the environs of industrial areas (Figure 18).

Particulate matter of aerodynamic diameter less than 2.5 µm

PM2.5 is included in the PM10 estimates but was also considered separately as it has specific health implications. As with total PM10, the main source of PM2.5 was transportation; other sources included residential combustion, industrial combustion, and waste incineration. The PM2.5 hotspots included the road network, the city core area, and industrial areas (Figure 19).

Figure 14: Map of NOX emissions in RUA-Kathmandu study area (tonnes/year/grid)

km

0.01

25

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Low

Traffic intensity

HotelBrick kiln

NOx tonnes/year


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Figure 15: Map of CO emissions in the RUA-Kathmandu study area (tonnes/year/grid)

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HotelBrick kiln

CO tonnes/year

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Figure 16: Map of NMVOC emissions in the RUA-Kathmandu study area (tonnes/year/grid)

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HotelBrick kiln

NMVOC tonnes/year

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157

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Low

Traffic intensity


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Figure 17: Map of NH3 emissions in the the RUA-Kathmandu study area (tonnes/year/grid)

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HotelBrick kiln

NH3 tonnes/year

0.01

20

High

Low

Traffic intensity

Figure 18: Map of PM10 emissions in the RUA-Kathmandu study area (tonnes/year/grid)

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HotelBrick kiln

PM10 tonnes/year

0.02

44.16

High

Low

Traffic intensity


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Observations from Passive Monitoring Campaigns

Observations from seasonal campaigns

Figures 20–22 show the results of the seasonal passive monitoring campaigns. The results from the individual monitoring stations were interpolated to yield GIS area maps. Detailed information from the seasonal exposure monitoring is given in Annex 7. The main results from the two seasons is summarized below.

Rainy season (July to September 2007)

� The concentrations of pollutants were high at roadside stations.

� The range observed for deposited particulate matter was 3–608 µg/m2 . The lowest value was recorded at the rural site Godavari (located southeast of the city centre) and the highest values at roadside points (the western outlet point from the valley, Putalisadak in the centre, and others). These values represent the relative concentration of all particles as passive monitoring does not distinguish between particulate matter of different diameters.

� The measured concentrations of NO2 ranged from 5.6–33.6 µg/m3.

� The measured concentrations of SO2 ranged from 0.6–3.7 µg/m3.

� The concentration of pollutants during the rainy season was lower than the limits set in the National Ambient Air Quality Standard. Other surveys have also reported similar results.

Pre-monsoon (dry) season (February to April 2008)

� The concentration of all pollutants was higher overall during the dry season than during the rainy season.

� The range for deposited particulate matter was 26–450 µg/m2. Particulate matter concentrations were higher at roadside stations.

Figure 19: Map of PM2.5 emissions in the RUA-Kathmandu study area (tonnes per year per grid)

km

High

Low

HotelBrick kiln

Traffic intensity

0.04

33

PM2.5 tonnes/year


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Figure 20: Results of passive monitoring of particulates in the dry and wet season; example of PM10

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Thamel

Gwarko

Balkhu

Balaju

Kalanki

BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune


Wet season 2007 (July - September)

0 1 2 30.5 km

±

Legend

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Kalimati

Gaushala

Chabahil

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor- Tinkune


0 1 2 30.5 km

±

Legend


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Kalimati

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Chabahil

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor- Tinkune


0 1 2 30.5 km

±

Legend



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Jamal

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Balkhu

Balaju

Kalanki

BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune


Dry season 2008 (February - April)

0 1 2 30.5 km

±


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Kalimati

Gaushala

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Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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±



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BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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±



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Thamel

Gwarko

Balkhu

Balaju

Kalanki

BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



0 1 2 30.5 km

±

Legend



100 - 200200 - 300300 - 400above 400


25

Figure 21: Results of passive monitoring of particulates in the dry and wet season; example of NO2

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Balaju

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BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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±



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BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor- Tinkune


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Kalimati

Gaushala

Chabahil

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor- Tinkune


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±

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Balaju

Kalanki

BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



0 1 2 30.5 km

±



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26

Figure 22: Results of passive monitoring of particulates in the dry and wet season; example of SO2

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Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

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Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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±



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Gaushala

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Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



0 1 2 30.5 km

±

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Gaushala

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Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



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±



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Thamel

Gwarko

Balkhu

Balaju

Kalanki

BouddhaLazimpat

Kalimati

Gaushala

Chabahil

Baluwatar

Maharajgunj

Putalisadak

TU, Kirtipur


Dilli Bazaar

Patan Hospital

Tinthana-Naikap

Koteswor-Tinkune



0 1 2 30.5 km

±



below - 1010 - 20

20 - 3030 - 40above 40


27

� NO2 concentrations ranged from 10.8 to 52.6 µg/m3. Overall concentrations were higher than in the rainy season but with the same trend of higher concentrations at roadside stations. On average, the reported concentrations were less than the National Ambient Air Quality Standard (40 µg/m3 maximum annual average) but there were a few roadside stations which recorded higher concentrations.

� SO2 concentrations ranged from 3.3–23.4 µg/m3 with the highest concentrations downwind. The higher concentrations were attributed both to different wind patterns during the dry season and to emissions from brick kilns (which operate mainly during the dry season and are mostly located in the southern part of the valley).

Meteorology and seasonal variation

There was a clear seasonal variation in the pollutant concentrations in the Kathmandu Valley which can be attributed to the combined effects of meteorological factors, slight changes in the local environment, and the presence of other pollutant generating sources. Wind patterns play a significant role in pollutant transfer. The dominant winds above the valley throughout the year are westerly and southwesterly (Sapkota 2004) but each season is characterized by its own unique meteorological parameters. The meteorological parameters for temperature, rainfall, and humidity during the two campaigns are summarized in Figure 23.

Higher concentrations of all atmospheric pollutants were observed in the dry season than in the rainy season. During the rainy season, high wind speeds, rain, and unstable conditions help to clean pollutants from the lower atmosphere. Similar seasonal trends were also observed in previous monitoring studies conducted by the Department of Hydrology and Meteorology (DHM 2000). Seasonal changes in the concentration of particulates and other pollutants have also been discussed by Sapkota (2004). Figures 24 – 26 show wet and dry season comparisons for each of the three monitored pollutants.

1-Jul-07 15-Jul-07 9-Jul-07 12-Aug-07 6-Aug-07 9-Sep-07 3-Sep-07 7-Oct-07 1-Oct-07

1-Feb-08 15-Feb-08 9-Feb-08 14-Mar-08 8-Mar-08 11-Apr-08 5-Apr-08 9-May-08 23-May-08

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Figure 23: Meteorological parameters for the two monitoring campaigns


28

The similarities and differences can be summarized as follows.

� For most sites, the particle deposition recorded during the rainy season and the pre-monsoon dry season was similar, with overall slightly lower values in the rainy season (Figure 24). (One outlier value was removed from the dataset as it was thought to have been contaminated.)

� The relative distribution of NO2 concentrations was similar in the two campaigns but the values were systematically lower during the rainy season (Figure 25).

� Measured SO2 concentrations were much lower in the rainy season than in the dry season and there was no direct correlation between the dry and rainy season values (Figure 26). This was possibly because SO2 emissions are mainly due to brick kilns which do not operate in the rainy season.

Meteorological studies show that southwesterly winds prevail in Kathmandu throughout the year (Figure 27) although there is also a weaker northeasterly component in the dry season. During almost all of the dry season monitoring period, the wind was from the southwest with an average wind speed of 2.5 kph (range 0.2 to 4.6 kph). During the rainy season, the winds prevailed from southwest, and from mid-September from the southeast. The average wind speed for the rainy season monitoring period was 1.5 kph (range 0.1 to 3.3 kph). Overall, the wind speeds were relatively low throughout the year; in general pollutants concentrations are higher when wind speeds are lower. Usually rain also plays a role in removing pollutants, especially particulates, from ambient air. However, even during the rainy season there are long periods without rain, and during the dry season there are rainy days. During the dry season monitoring campaign, there was no rain during February, but a total of 64.8 mm of rain fell during March and April, compared with a total of 357 mm during the rainy season campaign (Figure 23). Emissions during dry times in the rainy season, and resuspension of previously emitted particulates deposited by rain, lead to high levels of particulates even during the rainy season. This was reflected in the relatively small difference between rainy and dry season values (Figure 24).

Figure 24: Comparison of particle deposition recorded at the same sites in the dry and rainy seasons.

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son

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Figure 25: Comparison of NO2 concentrations recorded at the same sites in the dry and rainy seasons

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3)

Wet season (µg/m3)

Figure 26: Comparison of SO2 concentrations recorded at the same sites in the dry and rainy seasons

0

5

10

15

20

25

0 5 10 15 20 25

Dry

sea

son

(µg/m

3)

Wet season (µg/m3 * 5)


29

Observations from year-long continuous monitoring

The results of year-long monitoring of pollutant concentrations at nine selected sites are given in Annex 8. Samplers were replaced at two-month intervals, so each measurement provides an average over the two months. Figure 28 shows the pollutant concentrations observed at Bouddha, a typical site, as an example.

The main results over the nine sites were as follows:

� The maximum concentrations of all monitored pollutants were observed during the pre-monsoon dry season (mid March to mid May) and the minimum concentrations during the rainy season (mid-August to mid-October).

� The concentrations of SO2 ranged from 0.1 to 44 µg/m3 (average 5.7 µg/m3).

� The concentrations of NO2 ranged from 2 to 36 µg/m3 (average 16 µg/m3).

0

3

6

9

12

15

18N

NNE

NE

ENE

E

ESE

SE

SSE

S

SSW

SW

WSW

W

WNW

NW

NNW

3.0 - 4.51.5 - 3.00.1 - 1.5

Wind speed (m/s)

%

Calm wind: 2 %

0

3

6

9

12

15

18N

NNE

NE

ENE

E

ESE

SE

SSES

SSW

SW

WSW

W

WNW

NW

NNW

3.0 - 4.51.5 - 3.00.1 - 1.5

Wind speed (m/s)

%

Calm wind: 4 %

Figure 27: Wind rose for Kathmandu

Dry Season

Wet Season

Wind speed (m/s)

Wind speed (m/s)

Calm wind: 4%

Calm wind: 2%


30

� The concentrations for O3 ranged from 25 to 100 µg/m3 (average 47 µg/m3).

� The concentrations of HNO3 ranged from 0.10 to 0.60 µg/m/3 (average 0.26 µg/m3).

� The rural site had the lowest concentrations of all pollutants except ozone, and the second highest concentration of ozone (Figure 29).

� Deposition of particulate matter o There was a good correlation between the blackness of the filters and the mass of deposited particles

(Figure 30). The particulate deposition to the surrogate surface averaged 40 µg/cm2 per month.

o Deposition was lowest at the rural site.

o On average, the analysed ions represented 7% of the total mass. Calcium had the highest concentration followed by sulphate. Literature references suggest that sulphate and calcium are correlated. Nitrate was the third most prevalent ion and was somewhat correlated to the presence of sodium, indicating a reaction between nitric acid and sodium chloride.

Figure 28: Seasonal variations in pollutant concentrations at Bouddha

100

80

60

40

20

0

0.5

0.4

0.3

0.2

0.1

0Nov–Jan Jan–Mar Mar–May May–Aug Aug–Oct Oct–Nov

HN

O3 concentration (m

g/m3)

SO2

NO2

HNO3

O3

SO2,

NO

2,O

3, c

once

ntra

tion

(mg/

m3 )

30

35

40

45

50

55

60

0 5 10 15 20 25 30

O3 (µ

g/m

3)

NO2 (µg/m3)

ICIMOD

Machen Gaun

Harisiddhi

TU KirtipurSingh Durbar

Teaching Hospital

KathmanduDurbar Square

PatanDurbar Square

Bouddha

Figure 29: Annual average O3 concentrations versus annual average NO2 concentrations at nine

continuous monitoring sites

Figure 30: Blackness of filter (soiling) as a function of deposited particle mass for nine


0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 20 40 60 80 100

µg/cm/month

In (

Ro/R

)/m

on

th


31

Validating the Emission Inventory

Emission inventories are difficult to validate. The usual approach is to use a dispersion model to estimate the concentrations in the air at different points that would result from the emissions listed in the inventory under the assumed meteorological conditions. These values can then be compared with measured air concentrations. This was the approach used here with the air concentrations measured using passive sampling. Both the emission inventory and the passive monitoring can be subject to errors. If there is an adequate correlation between the two maps it indicates that the estimates probably reflect the situation on the ground. If the correlation is inadequate, it indicates that more work and modifications are needed. This validation approach has been used in a number of European cities where such reference inventories exist, including Athens, Belfast, Linz, London, Milan, and Stockholm.

Dispersion modelling using The Air Pollution Model (TAPM)

Dispersion modelling calculates the contribution to air concentrations over the area from the emissions included in the database. It can be used to calculate yearly averages for pollutant concentrations. The model can also calculate different air pollution scenarios – a valuable tool for comparing cost efficiencies between different measures. Models are also useful in environmental impact assessments.

Various types of dispersion models are available, but what they all have in common is that they provide a link between emissions and ambient concentrations and make it possible to assess impacts more effectively. The model is a key component of any decision support system which aims to achieve a reduction in ambient pollutant concentrations to meet the air quality goals at low cost.

Dispersion models typically use either observed meteorological data (from a surface-based meteorological station) or a diagnostic wind field model based on available observations. The present study used The Air Pollution Model (TAPM) developed by the Australian CSIRO Atmospheric Research Division. This differs from the two approaches mentioned, in that it solves the fundamental fluid dynamics and scalar transport equations to predict meteorology thus eliminating the need for site-specific meteorological observations. The model predicts the flows important to local-scale air pollution transport, such as sea breezes and terrain induced flows, against a background of larger-scale meteorology provided by synoptic analyses. This means that The Air Pollution Model calculates the meteorology from an included database instead of using input data from meteorological measurements, which is a considerable advantage where the required local data are not available. However, since the results from the model also depend on the emission inventory, the model calculations have to be redone whenever the emission inventory is changed.

Comparison of dispersion model maps with passive monitoring maps

IVL carried out a comparison and correlation between the results obtained in the passive monitoring studies and the results of the rapid urban assessment using dispersion modelling.

Figure 31 shows the dispersion modelling results for SO2, NO2, and particle concentrations in the rainy season and pre-monsoon dry season. Dispersion modelling was only carried out for the Kathmandu Municipality area, not for Lalitpur. The calculations used background concentrations of 0.8 µg/m3 for SO2, and 3.5 µg/m3 for NO2, based on monitoring data on the outskirts of Kathmandu. The calculations indicate that the different meteorological conditions would lead to concentration loads being much lower for all components during the pre-monsoon dry season.

Comparison of the modelled and passive mapping data showed the following:

� For SO2, there were quite large differences between the results of the two methods. This was probably because emissions were kept constant throughout the year in the model, the seasonal changes being climatic only. The main source of SO2 in the model was combustion, whereas in reality the main source was the brick factories, which lay beyond the boundary of the study area and were thus not included in the inventory. These factories were closed during the rainy season, leading to marked seasonal differences. In the model, SO2 emissions showed little seasonal change, and were probably underestimated for the dry season (February–April) and overestimated for the rainy season (July–September).

Figure 28: Seasonal variations in pollutant concentrations at Bouddha

Figure 30: Blackness of filter (soiling) as a function of deposited particle mass for nine



32

Figure 31: Calculated air concentrations (in µg/m3) of SO2, NO2, and particulate matter (contribution from emissions only) during the two mapping campaigns for Kathmandu Municipality

Wet seasonDry season

µg/m3

40

35

30

25

20

15

10

5

0

SO2

µg/m3

400

350

300

250

200

150

100

50

0

Wet seasonDry seasonParticulate matter

Wet seasonDry season

µg/m3

30

25

20

15

10

5

0

NO2


33

� For NO2, there was fairly good agreement between the modelled and measured data during the rainy season, with some higher, and some lower estimates given by the model (Figure 32), but in the dry season, the model underestimated concentrations by about 50% compared to the monitoring data.

� Since monitored particulate concentrations are measured only as deposits, they cannot be directly compared to the atmospheric concentrations obtained through dispersion modelling which were based on specific PM10 and PM2.5 concentrations from the emission inventory. Only the variation in pollution load can be compared. However, the agreement between the calculated contribution (as an air concentration) and the deposition of PM was reasonably good.

Using Population Density Maps to Estimate the Exposure Hazard

When pollutant concentration maps are superimposed on population density maps it is possible to estimate the number of people who may have been exposed to critical levels of pollutants. Population data was obtained from ward-wise population information collected from the municipality. The polygons indicate ward boundaries. Each dot on the map represents 250 people. This information was then overlaid with the interpolated concentration map from the passive monitoring to give a rough estimate of the number of people exposed to different pollutants (Figure 33). In the next phase of this study, it will be possible to use this information to estimate in monetary terms the impact that poor air quality is having on the health of inhabitants in different areas of Kathmandu.

Limitations

The macro-scale emission inventory, the passive monitoring studies, and the dispersion modelling that correlates the two are all subject to a number of limitations:

� Not all polluting sources were included in the macro-scale emission inventory database. For example, brick kilns, which are major polluters, were located outside the RUA-Kathmandu study area, and as a result, emissions from these polluting point sources were not included in the model. Another example is ammonia which is unstable and difficult to monitor. While it was possible to estimate ammonia from fertilizer use, it was not possible to estimate ammonia from waste.

� Emission factors for land use classes such as open areas, parking areas, forests, and green areas were not included.

� The emission factors and activity data used were for the whole valley and not specifically for the study area.

� Geographical and seasonal variations were not included in the emission inventory. To further refine the model, it would be useful to estimate seasonal differences based on GIS and satellite images.

� Kathmandu sits in a bowl-like valley surrounded by high hills, which makes it very difficult to calculate meteorological parameters from modelling alone. Figure 34 shows the worldwide weather grid and the coarse resolution of the data used in the model. The disagreement between the monitoring and modelling data was in part due to the fact that this grid is not fine enough to distinguish subtle variations, and in part due to the fact that Nepal sits on the boundary between NAS 1999+ and CAS 1999+.

Figure 32: Comparisons between modelled and observed data

0

10

20

30

40

50

0 10 20 30 40 50

Calc

ula

ted N

O2 (

µg

/m3)

Rainy Season Dry Season

Monitored NO2 (µg/m3)

Calc

ula

ted

SO

2 (

µg

/m3)

Monitored SO2 (µg/m3)


0

10

20

30

40

0 10 20 30 40




34

Figure 33: Exposure hazard maps showing pollutant emission hotspots and population density

km

Dry season

Rainy season

51 – 6041 – 5031 – 4021 – 3011 – 202 – 10

3,038 – 3,0563,057 – 5,471

5,472 – 8,773

8,774 – 12,89812,899 –19,871

19,872 – 56,832

56,833 – 85,194

85,195 – 107,124

Population distribution

1 Dot = 250 people

Contour of NOx (µg/m3)

Population density


35

Figure 33, continued

Dry season

Rainy season

3,038 – 3,0563,057 – 5,471

5,472 – 8,773

8,774 – 12,89812,899 –19,871

19,872 – 56,832

56,833 – 85,194

85,195 – 107,124

Population density

22 – 2419 – 2116 – 1813 – 1510 – 127 – 94 – 60 – 3


1 Dot = 250 people

Contour of SO2 (µg/m3)

km

continues


36

Figure 33, continued

km

501 – 600401 – 500301 – 400201 – 300101 – 20010 – 100


1 Dot = 250 people

Contour of PM (µg/m3)

3,038 – 3,0563,057 – 5,471

5,472 – 8,773

8,774 – 12,89812,899 –19,871

19,872 – 56,832

56,833 – 85,194

85,195 – 107,124

Population density

Dry season

Rainy season


37

� The monitoring data may have had some uncertainty since only a relatively small number of sites were sampled. A larger number of sampling sites over a wider area might have yielded more accurate pollutant concentration maps. In addition, pollution created outside the study area (such as by brick kilns south of the city) drifts into the city borne by the prevailing winds. Although a background value was assumed for SO2, no boundary measurements were made to measure the actual amount entering the measurement area from outside. Thus it was not possible to estimate the proportion of pollutant measured by the passive samplers that resulted from airborne dispersion of pollutants from emitters outside the area, and the proportion from emitters within the measurement area. As the model only included sources within the area, the results were not directly comparable.

Figure 34: Worldwide weather GRID


38

4. Conclusion

An initial rapid urban assessment for Kathmandu was successfully completed. The process was modified according to local knowledge with the aid of different GIS tools. The results obtained with the help of the RUA indicated that the city core areas and densely populated areas within the valley have the poorest air quality. People living in those areas are at greater risk for diseases caused by ambient pollutants. The highest emissions of PM10 were calculated to be in the vicinity of roads and highly populated city core areas, and of NH3 and NMVOC at the periphery of built-up areas. Road transportation is the major source of pollution in the urban centre of the Kathmandu Valley followed by other forms of domestic and commercial combustion.

In general, the dispersion modelling results showed a reasonable agreement with the passive monitoring data but highlighted various factors that need to be taken into account in any future applications of the model. The emission factors for particulate matter need to be investigated more deeply, and especially the contribution of resuspension needs to be studied more carefully. Use of a generalized model to calculate seasonal meteorological factors also had considerable limitations for the valley, where climate is highly diverse and localized, and affected by marked topological factors.

Overall the RUA provides a useful method for assessment of air quality and highlighting of likely hotspots where accurate values are hard to obtain. However, the approach is likely to be most useful when applied to urban areas that are geographically less varied, and where land use classes are more clearly differentiated. In Kathmandu, the highly variable geography and close integration of different types of land use pose problems for use of this approach. However, the Kathmandu study has been useful as an example of how to put the methodology into practice and as an aid to identifying where the method is likely to be useful and what modifications might need to be made.

1. Introduction

39

References

Adhikari, DP (1999) Air pollution situation and management plan. Kathmandu, Nepal: World Health Organization (WHO) Nepal and Department of Water Supply and Sewerage

Bastola, TS (1998) Response to air pollution impacts: A compendium on environmental statistics 1998. Kathmandu, Nepal: Central Bureau of Statistics, National Planning Commission Secretariat, Government of Nepal

CANN (2008) Clean air in Nepal: Summary of progress on improving air quality. Kathmandu, Nepal: Clean Air Initiative, Clean Air Network-Nepal

CBS (2001) Population census 2001, National report. Kathmandu, Nepal: Central Bureau of Statistics

CBS (2005) Statistical yearbook of Nepal 2005. Kathmandu, Nepal: Central Bureau of Statistics

Cox, RM (2003) The use of passive sampling to monitor forest exposure to O3, NO2 and SO2: a review and some case studies. Environmental Pollution 126(3): 301–311

Devkota, SR; Neupane, CP (1994) Industrial pollution inventory of the Kathmandu Valley and Nepal. Kathmandu, Nepal: Ministry of Industry, Government of Nepal

DHM (2000) Record file of total suspended particles in Kathmandu. Unpublished report prepared by Department of Hydrology and Meteorology, Government of Nepal, Kathmandu, Nepal

Ferm, M; Rodhe, H (1997) ‘Measurements of air concentrations of SO2, NO2 and NH3 at rural and remote sites in Asia.’ Journal of Atmospheric Chemistry 27: 17–29

Ferm, M; de Santis, F; Varotsos, C; (2005) ‘Nitric acid measurements in connection with corrosion studies.’ Atmospheric Environment 39: 6664–6672

Ferm, M; Svanberg, P-A (1998) ‘Cost-efficient techniques for urban and background measurements of SO2 and NO2.’ Atmospheric Environment 32: 1377–1381

Ferm, M; Watt, J; O’Hanlon, S; De Santis, F; Varotsos, C (2006) ‘Deposition measurement of particulate matter in connection with corrosion studies.’ Analytical and Bioanalytical Chemistry 384: 1320–1330

Gautam, C (2006) Final report on action program on air quality management of Kathmandu Valley. Kathmandu, Nepal: Ministry of Environment, Science, and Technology, Government of Nepal

GON (2007) Statistical Year Book of Nepal 2007. Kathmandu, Nepal: Central Bureau of Statistics, National Planning Commission Secretariat, Government of Nepal

Haq, G; Han, W-J; Kim, C; Vallack, H (2002) Benchmarking urban air quality management and practice in major and mega cities of Asia, Stage I. Seoul, South Korea: Korea Environment Institute. www.unep.org/pdf/apma_benchmarking_report.pdf

ICIMOD (2005) 2005 Report on Emission Inventory – Kathmandu. Kathmandu, Nepal (unpublished)

ICIMOD (2007) Kathmandu valley environment outlook. Kathmandu, Nepal

LEADERS Nepal (2000) Record file of PM10, NOx, SOx, CO in Kathmandu. Unpublished report by the Society for Legal and Environmental Analysis and Development Research, Kathmandu, Nepal

Magar, AT (2012) Kathmandu air profile. Kathmandu, Nepal: Kathmandu Metropolitan City; Clean Energy Nepal

MOEST (2006) Ambient Air Quality of Kathmandu Valley 2005. Kathmandu, Nepal: Ministry of Environment, Science and Technology


40

NECG (1990) Survey of environmental pollution in urban and rural areas of Nepal. Kathmandu, Nepal: Nepal Environmental Conservation Group

Pokhrel, S (2002) Climatology of air pollution in the Kathmandu Valley, Nepal. MSc thesis, Southern Illinois University, Environmental Science Program in the Graduate School, Edwardsville, Illinois, USA

Pokhrel, AK; Pokhrel S; Aryal, KR (eds) (1999) Citizens report ’99: Air pollution in the face of urbanization. Kathmandu, Nepal: Society for Legal and Environmental Analysis and Development Research (LEADERS Nepal) and Japan Environment Corporation

Pokhrel, AK; Sharma, BK (eds) (1998) A citizens report on air pollution in Kathmandu: Children’s health at risk. Kathmandu, Nepal: Society for Legal and Environmental Analysis and Development Research (LEADERS Nepal) and Japan Environment Corporation

Sapkota, B (2004) ‘Air pollution transport from Kathmandu Valley.’ Journal of the Institute of Engineering 3(1): 30–42

Sapkota, RP (2005) Stack emission monitoring of the newly introduced brick kilns and their impacts on soil fertility. MSc thesis, Tribhuvan University, Central Department of Environment Science, Kathmandu, Nepal

Shah, JJ; Nagpal, T (eds) (1997) Urban air quality management strategy in Asia: Kathmandu Valley report, World Bank Technical Paper No 378 (URBAIR). Washington DC, USA: World Bank

Tuladhar, B (2008) Air quality management issues and priorities for Nepal. Presentation made at National Stakeholder’s Meeting-Building Partnerships for Clean Air Initiative in Nepal, 31 December 2008, Kathmandu, Nepal

UNEP (2001) Nepal: State of environment 2001. Bangkok, Thailand: UNEP RRC.AP

UNEP/WHO (1994) GEMS/AIR methodology reviews Vol.4: Passive and active sampling methodologies for measurement of air quality. Nairobi, Kenya: United Nations Environment Programme. http://whqlibdoc.who.int/hq/1994/WHO_EOS_94.1-3_(vol4).pdf (accessed September 2011)

WHO (2000) Guidelines for air quality. Geneva, Switzerland: World Health Organization www.who.int/phe/health_topics/outdoorair_aqg/en/ (accessed September 2011)

WHO (2002) The world health report 2002: Reducing risks, promoting healthy life. Geneva, Switzerland: World Health Organization

1. Introduction

41

Bibliography

Devkota, SR (1992) Energy utilization and air pollution in Kathmandu Valley, Nepal. Bangkok, Thailand: Asian Institute of Technology

CBS (2005) Economic survey 2004/2005. Kathmandu, Nepal: Central Bureau of Statistics, National Planning Commission Secretariat, Government of Nepal

Mathema MB; Joshi, AR; Shrestha, SL; Shrestha, CL (1992) Environmental problems of urbanization and industrialization: The existing situation and the future decision. Kathmandu, Nepal: United Nations Development Programme

Mathur, HB (1993) Final report on the Kathmandu Valley Vehicular Emission Control Project (KVVECP). Kathmandu, Nepal: Government of Nepal and United Nations Development Programme

Niraula, BP (1998) Health: A compendium on environmental statistics 1998 Nepal. Kathmandu, Nepal: Central Bureau of Statistics, National Planning Commission Secretariat, Government of Nepal

NESS (1999) Ambient air quality monitoring of Kathmandu Valley, Nepal Environmental and Scientific Services Private Limited, ADB TA 2847-Nep Project. Kathmandu: Asian Development Bank and Ministry of Environment and Population, Government of Nepal

Pandey, MR; Basnet, B (1987) Chronic bronchitis and Cor pulmonale in Nepal, A scientific Epidemiological Study Monograph. Kathmandu, Nepal: Mrigendra Medical Trust

Pandey, RK (1987) Geography of Nepal. Kathmandu, Nepal: Centre for Altitude Geography

Pokhrel, S; Mark, LH (2002) Climatology of air pollution in Kathmandu Valley, Nepal. Illinois, USA: Environmental Science Program, Southern Illinois University

Shrestha, RM; Malla, S. (1993) Air pollution from energy use in a developing country city: The case of Kathmandu, Nepal. Bangkok, Thailand: Energy Programme, School of Environment Resources and Development, Asian Institute of Technology

Yogacharya, KS (1998) ‘Metrological aspects.’ In A compendium of environmental statistics 1998: Nepal. Kathmandu, Nepal: Central Bureau of Statistics. www.cleanairnet.org/caiasia/1412/articles-58960_SPokhrel_Thesis.pdf (accessed February 2010)


42

Annex 1: Details of Study Area and Population

a) Kathmandu Metropolitan City: ward-wise distribution of area and population

Ward ID Area (km2) Population Number of households

1 1.38 8,464 1,689

2 0.81 13,655 3,195

3 3.3 20,782 4,569

4 3.24 29,539 6,768

5 0.79 15,340 3,573

6 3.67 39,316 8,768

7 1.54 39,530 9,332

8 2.54 9,434 2,165

9 3.02 29,263 6,708

10 1.57 25,977 6,168

11 1.84 15,244 3,488

12 0.51 10,313 2,084

13 2.13 29,721 6,429

14 3.03 34,488 7,846

15 3.17 32,441 7,448

16 4.37 43,450 10,789

17 0.66 19,876 4,559

18 0.19 8,065 1,730

19 0.16 7,400 1,477

20 0.16 8,240 1,701

21 0.15 12,369 2,507

22 0.19 5,840 1,009

23 0.1 8,289 1,709

24 0.09 5,272 925

25 0.1 4,310 744

26 0.04 3,764 757

27 0.08 7,789 1,542

28 0.07 5,462 1,088

29 2.19 24,543 5,582

30 0.25 9,896 2,041

31 1.04 14,502 3,252

32 1.28 24,355 5,694

33 0.86 21,597 5,064

34 2.32 46,136 11,039

35 3.95 35,184 8,716

Total 50.76 669,846 152,155

Source: CBS 2001

Annexes

Annexes

43

b) Lalitpur Sub-Metropolitan City: ward-wise distribution of area and population

Ward ID Area (km2) Population Number of households

1 0.41 7,069 1,691

2 1.30 10,459 2,284

3 1.48 10,637 2,365

4 1.81 10,971 2,523

5 0.71 6,573 1,397

6 0.25 6,352 1,311

7 0.24 6,408 1,299

8 0.44 7,355 1,407

9 0.77 8,135 1,706

10 0.81 5,430 1,222

11 0.13 4,238 780

12 0.13 5,677 1,129

13 0.95 6,553 1,400

14 1.85 11,530 2,498

15 2.43 11,352 2,694

16 0.10 5,294 989

17 0.57 6,693 1,509

18 0.13 6,915 1,287

19 0.18 6,048 1,266

20 0.20 6,519 1,447

21 0.09 4,249 906

22 0.46 8,513 1,890

Total 15.43 162,997 35,000

Source: CBS 2001


44

Annex 2: Data Sources for Emission Inventory of Kathmandu (2005)

Fuel consumption in thousands of tonnes of oil equivalent per year Statistical Yearbook of Nepal 2005, Central Bureau of Statistics, Government of Nepal, Kathmandu, Nepal

Fuel consumption in kilotonnes per year IEA Data for Nepal year 2005 – from web page www.iea.org

Fugitive (non-combustion) emissions of NMVOC from distribution and handling of gasoline Nepal Oil Corporation (www.nepaloil.com) estimation made for the Kathmandu valley

Emissions for LTO (landing and take-off) and cruise activities of domestic aircraft Civil Aviation Authority of Nepal (CAAN) (www.caanepal.org.np)

Emissions for LTO (landing and take-off) activities of international aviation Civil Aviation Authority of Nepal (CAAN) (www.caanepal.org.np) (used data for 2006)

Mobile emissions (detailed) of NOx, CO, and PM for on-road vehicles Vehicle registration record, Department of Transport and Management, Kathmandu, 2004/2005

Process (non-combustion) emissions from the production of mineral products Statistical Yearbook of Nepal 2005, Central Bureau of Statistics

Process (non-combustion) emissions of NMVOC from alcoholic beverage manufacture Statistical Yearbook of Nepal 2005, Central Bureau of Statistics

Process (non-combustion) emissions of NMVOC and PM10 from food production Statistical Yearbook of Nepal 2005, Central Bureau of Statistics and Statistics Information on Nepal Agriculture, 2004/2005, Ministry of Agriculture, Government of Nepal

Emissions of NMVOC from solvent and other product use Paint and varnishes consumption estimation from import data taken from www.intracen.org

Ammonia (NH3) emissions from manure management in agriculture Economic survey 2004/2005

Emissions of NH3 and NOx from application of nitrogen-containing fertilizers (fertilizer volatilization, foliar emissions, and decomposing vegetation) Statistics Information on Nepal Agriculture, Ministry of Agriculture, Government of Nepal

Emissions from agricultural residue burning Economic survey 2004/2005, Ministry of Finance, Government of Nepal

Emissions from on-site burning of forests and grasslands Personal communications

Emissions from waste incineration Estimation made from type and amount of waste generated for the Kathmandu Valley from Environment Statistics of Nepal 2005, CBS, National Planning Commission Secretariat, Government of Nepal

Ammonia emissions from human excreta Statistical Yearbook of Nepal 2005, Central Bureau of Statistics estimated for Kathmandu according to information that 50% of urban areas have toilet facilities. Environment Statistics of Nepal 2005, CBS, National Planning Commission Secretariat, Government of Nepal

Annexes

45

Annex 3: The Road Network – Total Traffic and Estimated Emissions Intensity

Classes 1, 2, and 3 are major roads; classes 4 and 5 are minor roads.

Road class Total traffic (number of vehicles)

Road length(metres)

Emission factors for pollutants


1 11,699,297 38,961 0.2 2.03 3.586 0.909 0.01 15.24 2.41

2 18,699,468 40,288 0.7 3.34 5.89 1.495 0.03 25.05 3.96

3 573,051 52,870 0.11 0.111 0.19 0.049 0.00 0.83 0.132

4 328,500 22,780 0.004 0.056 0.098 0.025 0.01 0.93 0.066

5 43,800 4,497 0.002 0039 0.049 0.012 0.00 0.21 0.033

Annex 4: LPG Use in Hotels

Hotel class Type of hotel No. surveyed Number of LPG cylinders* used per month per hotel

1 5 star and 4 star 15 56–120**

2 3 star 13 25–60

3 2 star 22 10–30

4 1 star and no stars 90 6–13

Total 150

* One standard cylinder contains 14.5 kg of LPG fuel

** 120 cylinders used in the high season (5 months) and 56 in the low season; other hotels did not report a distinction between high and low season

Annex 5: Calculation of Fuel Consumption

Fuel consumption in the Kathmandu Valley was calculated from the total value for Nepal in different sectors assuming the following:

Coal: 40% of Nepal total

Petroleum: 66% of Nepal total

High-speed diesel: 35% of Nepal total

Kerosene: 50% of Nepal total

LPG: 55% of Nepal total (50% of total for domestic use)

Fuelwood: 30% of Nepal total

Animal dung: 40% of Nepal total

Biogas: 20% of total for the Central Development Region (10% of CDR for Kathmandu)

Others, fuel oil: 20% of total for Central Development Region (50% of CDR for Kathmandu)


46

Annex 6: Attribute Tables for Area, Line, and Point Sources (screen capture)

A. Emissions from area sources (tonnes/yr)

FID

_Lan

dus

luse

_id

Resi

denc

e

Adm

in

Busi

ness

WBN

D_

ID

HO

USE

HO

LD

Are

a_ n

ew

HH

_ In

dex_

n

HH

_ re

sid_

n

HH

_ ad

min

_n

HH

_ bu

ss_n

SO2

NO

x

CO NM

VO

C

NH

3

PM10

PM2_

5

0 4 35 30 35 0 0 0.52 0.00000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0 0 0 0 29 3,885 421.84 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2 7 0 0 0 16 4,121 635,715.45 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 8 0 0 0 16 4,121 3,918.74 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4 9 0 0 0 16 4,121 261,650.08 0.00094 0.00 0.00 0.00 0.52 5.23 60.96 5.23 119.31 5.23 5.23

5 1 100 0 0 16 4,121 35,842.46 0.00094 33.69 0.00 0.00 0.10 0.13 8.19 0.81 0.10 0.22 0.17

6 9 0 0 0 16 4,121 13,430.68 0.00094 0.00 0.00 0.00 0.03 0.27 3.13 0.27 6.12 0.27 0.27

7 10 0 0 0 16 4,121 29,283.46 0.00094 0.00 0.00 0.00 0.79 1.55 3.92 0.79 0.00 0.00 0.00

8 9 0 0 0 16 4,121 36,228.47 0.00094 0.00 0.00 0.00 0.07 0.72 8.44 0.72 16.52 0.72 0.72

9 2 70 15 15 16 4,121 297,985.99 0.00094 196.07 42.02 42.02 1.85 2.42 154.86 15.29 1.91 4.21 3.19

10 9 0 0 0 16 4,121 157,869.06 0.00094 0.00 0.00 0.00 0.32 3.16 36.78 3.16 71.99 3.16 3.16

11 8 0 0 0 16 4,121 109,000.97 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12 7 0 0 0 16 4,121 10,016.06 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13 1 90 5 5 16 4,121 164,443.57 0.00094 139.12 7.73 7.73 0.64 0.84 53.53 5.29 0.66 1.45 1.10

14 9 0 0 0 16 4,121 6,404.99 0.00094 0.00 0.00 0.00 0.01 0.13 1.49 0.13 2.92 0.13 0.13

15 9 0 0 0 16 4,121 70,578.20 0.00094 0.00 0.00 0.00 0.14 1.41 16.44 1.41 32.18 1.41 1.41

16 7 0 0 0 16 4,121 23,543.66 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

17 9 0 0 0 16 4,121 6,162.63 0.00094 0.00 0.00 0.00 0.01 0.12 1.44 0.12 2.81 0.12 0.12

18 1 100 0 0 16 4,121 54,046.48 0.00094 50.80 0.00 0.00 0.15 0.19 12.34 1.22 0.15 0.34 0.25

19 7 0 0 0 16 4,121 12,303.86 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

20 8 0 0 0 16 4,121 11,381.83 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

21 9 0 0 0 16 4,121 83,870.71 0.00094 0.00 0.00 0.00 0.17 1.68 19.54 1.68 38.25 1.68 1.68

22 8 0 0 0 16 4,121 7,267.00 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23 9 0 0 0 16 4,121 20,391.99 0.00094 0.00 0.00 0.00 0.04 0.41 4.75 0.41 9.30 0.41 0.41

24 8 0 0 0 16 4,121 15,578.33 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

25 2 70 20 10 16 4,121 207,440.18 0.00094 136.50 39.00 19.50 1.02 1.33 85.29 8.42 1.05 2.32 1.76

26 6 0 0 0 16 4,121 25,987.47 0.00094 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

27 7 0 0 0 29 3,885 106,851.07 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

28 9 0 0 0 29 3,885 110,850.51 0.00178 0.00 0.00 0.00 0.22 2.22 25.83 2.22 50.55 2.22 2.22

29 9 0 0 0 29 3,885 17,126.07 0.00178 0.00 0.00 0.00 0.03 0.34 3.99 0.34 7.81 0.34 0.34

30 8 0 0 0 29 3,885 10,626.22 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

31 2 30 20 50 29 3,885 47,639.41 0.00178 25.44 16.96 42.40 1.33 1.74 111.27 10.99 1.37 3.02 2.29

32 9 0 0 0 29 3,885 9,769.91 0.00178 0.00 0.00 0.00 0.02 0.20 2.28 0.20 4.46 0.20 0.20

33 9 0 0 0 29 3,885 1,679.43 0.00178 0.00 0.00 0.00 0.00 0.03 0.39 0.03 0.77 0.03 0.03

34 8 0 0 0 29 3,885 2,482.38 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

35 8 0 0 0 29 3,885 5,313.26 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

36 8 0 0 0 29 3,885 1,889.81 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

37 1 100 0 0 29 3,885 8,757.75 0.00178 15.59 0.00 0.00 0.05 0.06 3.79 0.37 0.05 0.10 0.08

38 1 100 0 0 29 3,885 4,032.54 0.00178 7.18 0.00 0.00 0.02 0.03 1.74 0.17 0.02 0.05 0.04

39 1 80 0 20 29 3,885 4,717.19 0.00178 6.72 0.00 1.68 0.07 0.09 5.72 0.56 0.07 0.16 0.12

40 8 0 0 0 29 3,885 9,769.79 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

41 8 0 0 0 29 3,885 6,124.92 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

42 1 80 5 15 29 3,885 30,991.88 0.00178 44.13 2.76 8.27 0.37 0.49 31.16 3.08 0.38 0.85 0.64

43 8 0 0 0 29 3,885 1,427.89 0.00178 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Annexes

47

B. Emissions from line sources (tonnes/yr)

Id R_SO

2

R_N

Ox

R_CO

R_N

MV

OC

R_N

H3

R_PM

10

R_PM

2_5

Leng

th

leng

th_n

R_SO

2_n

R_N

Ox_

n

R_CO

_n

R_ NM

VO

C_N

R_N

H3_

n

R_PM

10_n

R_PM

2_5_

n

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 104.677 0.00063 0.13378 0.21919 0.02376 0 3.54143 1.23760

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 107.256 0.00064 0.13707 0.22459 0.02435 0 3.62868 1.26809

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 100.005 0.00060 0.12781 0.20941 0.02270 0 3.38337 1.18236

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 100.002 0.00060 0.12780 0.20940 0.02270 0 3.38327 1.18232

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 100.002 0.00060 0.12780 0.20940 0.02270 0 3.38327 1.18232

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 100.027 0.00060 0.12783 0.20946 0.02271 0 3.38411 1.18262

1 0.16405 34.94210 57.25254 6.20646 0 925.00859 323.25540 27341.233 100.115 0.00060 0.12795 0.20964 0.02273 0 3.38709 1.18366

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 114.087 0.00091 0.18334 0.30062 0.03251 0 4.85680 1.69716

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 19.847 0.00016 0.03189 0.05230 0.00566 0 0.84491 0.29524

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.149 0.00080 0.16094 0.26389 0.02854 0 4.26344 1.48982

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.057 0.00080 0.16079 0.26365 0.02852 0 4.25953 1.48845

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.125 0.00080 0.16090 0.26383 0.02854 0 4.26242 1.48946

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.032 0.00080 0.16075 0.26358 0.02851 0 4.25846 1.48808

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.231 0.00080 0.16107 0.26411 0.02857 0 4.26693 1.49104

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 100.115 0.00080 0.16088 0.26380 0.02853 0 4.26200 1.48931

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 105.498 0.00084 0.16954 0.27799 0.03007 0 4.49116 1.56939

2 0.01042 2.09326 3.43232 0.37124 0 55.45256 19.37733 1302.590 84.919 0.00068 0.13646 0.22376 0.02420 0 3.61509 1.26326

2 0.01245 2.50114 4.10112 0.44357 0 66.25755 23.15302 1556.401 9.196 0.00007 0.01478 0.02423 0.00262 0 0.39148 0.13680

2 0.01245 2.50114 4.10112 0.44357 0 66.25755 23.15302 1556.401 101.044 0.00081 0.16238 0.26625 0.02880 0 4.30154 1.50313

3 0.00095 0.19879 0.32563 0.03550 0 5.26312 1.83925 473.302 100.739 0.00020 0.04231 0.06931 0.00756 0 1.12022 0.39147

3 0.00095 0.19879 0.32563 0.03550 0 5.26312 1.83925 473.302 101.031 0.00020 0.04243 0.06951 0.00758 0 1.12347 0.39261

3 0.00143 0.30004 0.49150 0.05358 0 7.94402 2.77612 714.390 123.006 0.00025 0.05166 0.08463 0.00923 0 1.36783 0.47800

3 0.00143 0.30004 0.49150 0.05358 0 7.94402 2.77612 714.390 11.520 0.00002 0.00484 0.00793 0.00086 0 0.12810 0.04477

3 0.00363 0.76198 1.24820 0.13607 0 20.17444 7.05017 1814.248 20.004 0.00004 0.00840 0.01376 0.00150 0 0.22244 0.07774

3 0.00363 0.76198 1.24820 0.13607 0 20.17444 7.05017 1814.248 55.465 0.00011 0.02330 0.03816 0.00416 0 0.61677 0.21554

3 0.00363 0.76198 1.24820 0.13607 0 20.17444 7.05017 1814.248 8.078 0.00002 0.00339 0.00556 0.00061 0 0.08983 0.03139

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 34.792 0.00028 0.05591 0.09168 0.00992 0 1.48113 0.51757

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 78.006 0.00062 0.12536 0.20555 0.02223 0 3.32079 1.16042

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 105.902 0.00085 0.17018 0.27905 0.03018 0 4.50835 1.57540

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 10.073 0.00008 0.01619 0.02654 0.00287 0 0.42882 0.14985

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 103.698 0.00083 0.16664 0.27324 0.02955 0 4.41453 1.54261

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 103.299 0.00083 0.16600 0.27219 0.02944 0 4.39754 1.53668

2 0.00486 0.97526 1.59914 0.17296 0 25.83570 9.02802 606.885 115.260 0.00092 0.18522 0.30371 0.03285 0 4.90673 1.71461

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.445 0.00080 0.16142 0.26467 0.02863 0 4.27604 1.49422

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 25.871 0.00021 0.04157 0.06817 0.00737 0 1.10135 0.38486

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 71.495 0.00057 0.11489 0.18839 0.02038 0 3.04361 1.06356

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 33.980 0.00027 0.05461 0.08954 0.00968 0 1.44656 0.50549

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.833 0.00081 0.16204 0.26569 0.02874 0 4.29256 1.49999

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.592 0.00080 0.16165 0.26506 0.02867 0 4.28230 1.49641

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.362 0.00080 0.16128 0.26445 0.02860 0 4.27251 1.49299

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.235 0.00080 0.16108 0.26412 0.02857 0 4.26710 1.49110

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.044 0.00080 0.16077 0.26362 0.02851 0 4.25897 1.48825

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.107 0.00080 0.16087 0.26378 0.02853 0 4.26166 1.48919

2 0.02567 5.15615 8.45455 0.91444 0 136.59152 47.73051 3208.558 100.359 0.00080 0.16128 0.26445 0.02860 0 4.27238 1.49294


48

C. Emissions from point sources (hotels) (tonnes/yr)

H_Id H_SO2 H_NOx H_CO H_NMVOC H_NH3 H_PM10 H_PM2_5

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

1 0.290 0.380 24.300 2.400 0.300 0.660 0.500

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

2 0.145 0.190 12.150 1.200 0.150 0.330 0.250

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

3 0.058 0.076 4.860 0.480 0.060 0.132 0.100

4 0.029 0.038 2.430 0.240 0.030 0.066 0.050

Annexes

49

Annex 7: Passive Monitoring – Particle Deposition and Gaseous Concentrations in the Seasonal Campaigns

Station Latitude (ºN)

Longitude(ºE)

Altitude (masl)

Rainy season campaign Mass µg/cm2

SO2 µg/m3

NO2µg/ m3

Dry season campaign Mass µg/cm2

SO2 µg/m3

NO2µg/m3

Start date Stop date Start date Stop date

ICIMOD 27.6465 85.3232 1,327 5/13/2007 10/4/2007 27 0.8 6.1 – – – – –

Balaju hospital 27.7284 85.3039 1,302 6/25/2007 10/2/2007 55 8.8 1/31/2008 4/30/2008 89 – –

Balaju VSC 27.7249 85.2964 1,314 – – – – – 1/29/2008 4/30/2008 48 – 14.3

Balkhu Controller’s Office 27.6846 85.2950 1,303 – – – – – 1/31/2008 4/30/2008 27 8.7 11

Baluwatar, National College

27.7233 85.3308 1,302 6/29/2007 10/2/2007 23 – – 1/30/2008 4/29/2008 32 – –

Bhaktapur Durbar Square 27.6726 85.4292 1,343 6/30/2007 10/10/2007 21 – – 2/1/2008 4/29/2008 31 – –

Bhaktapur Durbar Square 27.6714 85.4287 1,339 – – – – – 2/1/2008 4/29/2008 42 – –

Bhatbhateni 27.7167 85.3325 1,321 6/28/2007 10/4/2007 18 – 9.7 1/30/2008 5/1/2008 43 – 14.2

Bhimsenthan 27.7022 85.3041 1,310 7/10/2007 10/3/2007 47 – 18.1 1/31/2008 5/1/2008 57 –

Bhotebahal 27.6986 85.3099 1,305 6/26/2007 10/4/2007 51 – 16.7 2/1/2008 5/1/2008 61 – 21.2

Bouddha 27.7206 85.3614 1,327 5/14/2007 10/2/2007 86 1.6 40.8 – – – – –

Budhanilkantha 27.7804 85.3581 1,445 7/9/2007 10/2/2007 16 – – 1/31/2008 4/30/2008 29 – –

Chabahil 27.7178 85.3475 1,326 6/28/2007 10/3/2007 36 1.8 25.9 1/30/2008 4/29/2008 40 13.6 29.4

Chetrapati 27.7116 85.3077 1,313 6/26/2007 10/3/2007 15 – 11.4 2/7/2008 5/19/2008 96 – 11.8

Dallu–Heumat Marga 27.7101 85.2973 1,309 6/27/2007 10/13/2007 42 – – 2/2/2008 4/22/2008 59 – –

Dhobighat 27.6732 85.3008 1,299 8/5/2007 10/9/2007 24 – – 3/1/2008 5/19/2008 52 – –

Dilli Bazaar 27.7055 85.3254 1,305 7/3/2007 10/4/2007 138 – 19.2 1/30/2008 4/29/2008 91 – 26.3

Ganabahal, Jhochen 27.7027 85.3069 1,302 6/26/2007 10/4/2007 39 – – 1/30/2008 5/1/2008 64 – –

Gaushala 27.7077 85.3443 1,326 6/28/2007 10/3/2007 63 – 16.7 1/30/2008 4/29/2008 73 – 23.8

Godawari 27.5920 85.3877 1,664 7/17/2007 10/4/2007 3 – – – – – – –

Gongabu–Gunstar 27.7403 85.3140 1,310 6/28/2007 10/2/2007 22 0.6 10.7 1/31/2008 4/30/2008 3.1 14.8

Gwarko 27.6687 85.3346 1,300 6/28/2007 10/3/2007 106 12.5 1/30/2008 4/29/2008 118 19.6

Gyaneswor–Mali Gaun 27.7055 85.3254 1,303 6/27/2007 10/3/2007 107 21.7 1/30/2008 4/29/2008 50 – –

Harisiddhi 27.6288 85.3464 1,364 5/13/2007 10/4/2007 21 1.5 5.2 – – – – –

Indrachowk 27.7029 85.3096 1,303 6/26/2007 10/4/2007 106 1.7 18.5 1/31/2008 5/1/2008 109 11.1 21

Jamal RS 27.7078 85.3150 1,304 6/26/2007 10/4/2007 99 33.6 1/31/2008 4/29/2008 140 – 42.1

Jyatha 27.7078 85.3150 1,304 6/26/2007 10/4/2007 18 13.4 1/31/2008 4/29/2008 28 – 19.5

Kalanki Chowk RS 27.6932 85.2815 1,319 – – 3/20/2008 4/30/2008 142 – –

Kalanki RS 27.6947 85.2861 1,328 6/27/2007 10/2/2007 144 0.9 13.1 1/31/2008 5/1/2008 74 10.9 23.1

Kalimati RS 27.6996 85.2987 1,443 6/28/2007 10/3/2007 161 3.3 25.5 1/30/2008 3/21/2008 201 14.5 52.6


27.7042 85.3079 1,314 5/15/2007 10/2/2007 37 1 16.4 – – – – –

Kirtipur–Nagaon 27.6704 85.2759 1,369 7/9/2007 10/16/2007 65 – – 1/26/2008 5/14/2008 63 – –

Kirtipur–Panga 27.6711 85.2796 1,365 7/8/2007 10/8/2007 14 – 5.6 1/26/2008 5/1/2008 31 – 8.2

Kirtipur–Samal 27.6796 85.2724 1,373 7/8/2007 10/7/2007 44 – – 1/26/2008 4/29/2008 69 – –

Koteswor–Tinkune RS 27.6788 85.3489 1,306 6/28/2007 10/3/2007 167 1.4 21.2 2/1/2008 4/29/2008 217 14.4 32.1

Kupondol, Jwagal 27.6836 85.3253 1,296 6/28/2007 10/3/2007 29 6.9 1/30/2008 5/1/2008 66 11.5

Lazimpat RS 27.7211 85.3199 1,317 6/29/2007 10/2/2007 88 2.2 22.6 1/30/2008 4/29/2008 89 8.3 31.6

Lokanthali 27.6725 85.3590 1,317 6/27/2007 10/3/2007 26 6.4 2/1/2008 2/29/2008 254 32.2

Mahankalsthan, Tudikhel 27.7040 85.3139 1,309 7/3/2007 10/4/2007 194 2.3 29.4 1/31/2008 4/29/2008 67 12.5 30.6

Maitighar 27.6934 85.3216 1,304 – – – – – 2/1/2008 5/1/2008 58 15 26.9

New Baneswor 27.6925 85.3407 1,316 6/27/2007 10/3/2007 26 0.8 10.3 1/27/2008 5/1/2008 42 20.3 15.4

New Baneswor BK1 27.6920 85.3317 1,310 6/27/2007 10/4/2007 18 0.7 9 1/30/2008 5/9/2008 71 17.4 12.8

New Baneswor HS 27.6885 85.3253 1,311 6/27/2007 10/3/2007 61 1.1 12.4 2/1/2008 5/9/2008 48 17.9 18.9

New Baneswor–RR school

27.6955 85.3383 1,316 – – – – 2/1/2008 5/1/2008 85 – –

New Road Parking 27.7037 85.3113 1,317 6/26/2007 10/9/2007 47 – – 1/31/2008 5/1/2008 77 – –

New Summit, Purano Baneswor

27.7008 85.3370 1,319 6/27/2007 10/3/2007 31 – – 1/30/2008 4/29/2008 37 – –

NTC, Kupondol 27.6686 85.3345 1,300 7/3/2007 10/3/2007 47 – 22.8 2/1/2008 4/30/2008 39 – 28.5

Patan Durbar Square 27.6725 85.3251 1,326 5/13/2007 10/2/2007 69 1 13.5 – – – – –

Patan Hospital 27.6684 85.3217 1,328 6/28/2007 10/3/2007 134 – – 1/30/2008 4/30/2008 217 – –

Patan Industrial Area 27.6659 85.3265 1,326 6/28/2007 10/3/2007 14 1.7 7.7 1/30/2008 4/30/2008 30 15.6 15.5


50

Purano Baneswor HS 27.7017 85.3395 1,321 6/27/2007 10/3/2007 158 3.3 27.3 1/30/2008 4/29/2008 101 23.4 31.5

Putalisadak 27.7070 85.3227 1,280 6/26/2007 10/9/2007 372 – – 1/28/2008 4/29/2008 1681 – –

Ranipokhari 27.7079 85.3153 1,307 6/26/2007 10/4/2007 81 – – 1/31/2008 4/29/2008 93 11.3 21.3

Ratopul–Battishputali 27.7078 85.3374 1,307 6/29/2007 10/3/2007 53 – – 1/30/2008 4/29/2008 62 – –

Rural site, Matsyagaun 27.6616 85.2535 1,493 5/14/2007 10/2/2007 6 0.5 3 – – – – –

Samakhushi RS 27.7258 85.3140 1,308 6/29/2007 10/2/2007 43 – 21.8 1/31/2008 5/1/2008 34 – 24.9

Sanepa RS 27.6884 85.3061 1,297 6/27/2007 10/4/2007 34 0.6 9.2 2/2/2008 4/22/2008 78 10.2 13.2

Satungal RS 27.6871 85.2477 1,369 6/29/2007 10/2/2007 107 – 15.6 1/31/2008 5/1/2008 135 – 21.2

Singh Durbar, Ministry Env.

27.6950 85.3262 1,302 5/14/2007 10/2/2007 36 0.4 13.5 – – – – –

Sorhakhutte RS 27.7184 85.3095 1,319 6/27/2007 10/2/2007 58 0.9 19.9 1/30/2008 4/30/2008 – 5 24.3

Swayambhu (Sitapaila) 27.7176 85.2814 1,352 6/28/2007 10/9/2007 22 – 6.9 1/30/2008 5/1/2008 26 – 10.8

Teaching Hospital, Maharajgunj

27.7353 85.3298 1,339 5/14/2007 10/2/2007 36 0.8 16.7 – – – – –

Thamel 27.7152 85.3113 1,321 6/26/2007 10/3/2007 30 1.1 16.9 2/5/2008 5/1/2008 77 7.7 26.1

Thimi 27.6732 85.3816 1,184 6/27/2007 10/3/2007 33 – – 2/1/2008 4/29/2008 62

Tinthana–Naikap RS 27.6871 85.2661 1,338 6/29/2007 10/2/2007 608 3.7 27.9 1/31/2008 5/1/2008 275 19.1 31.2

Tribhuvan University, Kirtipur

27.6837 85.2842 1,336 5/14/2007 10/2/2007 0.6 7.4 – – – – –

Tripureswor RS 27.6945 85.3142 1,296 – – – – – 2/8/2008 4/29/2008 41 – 23.6

Annexes

51

Annex 8: Passive Monitoring, Results of Year-long Continuous Monitoring at Nine Sites

a) Pollutant concentrations

Station Start time End time SO2 µg/m3 STP

NO2 µg/m3 STP

O3 µg/m3 STP

HNO3 µg/m3 STP

ICIMOD 11/14/2006 1/18/2007 8.0 <0.1 39 0.24ICIMOD 1/18/2007 3/16/2007 13.9 11.0 55 0.23ICIMOD 3/16/2007 5/13/2007 16.7 8.0 87 0.53ICIMOD 5/13/2007 8/6/2007 1.2 6.4 58 0.26ICIMOD 8/6/2007 10/4/2007 0.4 5.7 40 0.16ICIMOD 10/4/2007 11/1/2007 0.2 5.5 43 0.25Harisiddhi 11/15/2006 1/16/2007 23.0 10.4 43 0.15Harisiddhi 1/16/2007 3/21/2007 43.5 10.4 56 0.24Harisiddhi 3/21/2007 5/13/2007 30.7 9.5 52 0.31Harisiddhi 5/13/2007 8/7/2007 2.7 6.4 55 0.17Harisiddhi 8/7/2007 10/4/2007 0.2 3.9 35 0.11Harisiddhi 10/4/2007 11/7/2007 0.3 5.6 40 0.18Machen Gaun 11/14/2006 1/31/2007 3.1 3.1 50 0.13Machen Gaun 1/31/2007 3/21/2007 2.4 3.4 57 0.15Machen Gaun 3/21/2007 5/14/2007 4.9 3.8 100 0.41Machen Gaun 5/14/2007 8/7/2007 0.8 3.7 51 0.23Machen Gaun 8/7/2007 10/2/2007 0.2 2.2 34 0.11Machen Gaun 10/2/2007 11/15/2007 0.3 2.0 37 0.10Patan Durbar Square 11/15/2006 1/31/2007 9.3 32.8 32 0.31Patan Durbar Square 1/31/2007 3/21/2007 10.7 28.3 45 0.31Patan Durbar Square 3/21/2007 5/14/2007 15.8 26.2 74 0.43Patan Durbar Square 5/13/2007 8/7/2007 1.4 <0.1 55 0.26Patan Durbar Square 8/7/2007 10/2/2007 0.6 13.5 33 0.19Patan Durbar Square 10/2/2007 10/31/2007 0.6 15.5 37 0.23Singh Durbar, Ministry of Env. 11/15/2006 1/31/2007 7.0 27.6 30 0.24Singh Durbar, Ministry of Env. 1/31/2007 3/21/2007 8.7 26.4 46 0.25Singh Durbar, Ministry of Env. 3/21/2007 5/14/2007 11.1 20.2 79 0.56Singh Durbar, Ministry of Env. 5/14/2007 8/2/2007 <0.2 14.9 0.29Singh Durbar, Ministry of Env. 8/2/2007 10/2/2007 0.4 12.2 33 0.22Singh Durbar, Ministry of Env. 10/2/2007 11/6/2007 0.5 14.1 36 0.23Kirtipur, University 11/15/2006 1/31/2007 5.3 14.2 37 0.19Kirtipur, University 1/31/2007 3/21/2007 5.1 8.7 50 0.24Kirtipur, University 3/21/2007 5/14/2007 9.1 10.8 79 0.46Kirtipur, University 5/14/2007 8/5/2007 1.0 6.8 50 0.27Kirtipur, University 8/5/2007 10/2/2007 0.2 5.9 30 0.16Kirtipur, University 10/2/2007 11/15/2007 0.1 8.0 36 0.18Kathmandu Durbar Square 11/16/2006 1/31/2007 4.9 30.5 32 0.34Kathmandu Durbar Square 1/31/2007 3/21/2007 6.1 28.0 47 0.42Kathmandu Durbar Square 3/21/2007 5/15/2007 11.2 24.3 75 0.60Kathmandu Durbar Square 5/15/2007 8/7/2007 1.5 18.6 52 0.34Kathmandu Durbar Square 8/7/2007 10/2/2007 0.5 14.1 34 0.16Kathmandu Durbar Square 10/2/2007 11/1/2007 0.8 18.2 38 0.24Teaching Hospital 11/16/2006 1/31/2007 2.5 26.6 32 0.26Teaching Hospital 1/31/2007 3/21/2007 3.1 25.7 45 0.25Teaching Hospital 3/21/2007 5/14/2007 4.8 24.0 80 0.51Teaching Hospital 5/14/2007 8/7/2007 0.8 19.4 51 0.27Teaching Hospital 8/7/2007 10/2/2007 0.7 14.0 33 0.18Teaching Hospital 10/2/2007 11/6/2007 14.6 37 0.23Bouddha 11/16/2006 1/31/2007 4.1 35.7 25 0.24Bouddha 1/31/2007 3/22/2007 5.9 34.1 35 0.21Bouddha 3/22/2007 5/14/2007 8.2 35.2 57 0.44Bouddha 5/14/2007 8/7/2007 2.4 25.6 41 0.19Bouddha 8/7/2007 10/2/2007 0.7 15.9 27 0.10Bouddha 10/2/2007 11/6/2007 0.7 20.2 29 0.15


52

b) Annual average pollutant concentrations

Station Start date End date SO2 NO2 O3 HNO3

µg/m3 µg/m3 µg/m3 µg/m3

ICIMOD 11/14/2006 11/1/2007 6.9 7.4 56 0.28

Harisiddhi 11/15/2006 11/7/2007 17.1 7.8 48 0.19

Machen Gaun 11/14/2006 11/15/2007 2.0 3.1 55 0.19

Patan Durbar Square 11/15/2006 10/31/2007 6.5 24.6 46 0.29

Singh Durbar, Ministry of Env. 11/15/2006 11/6/2007 5.8 19.5 44 0.30

Tribhuvan University, Kirtipur 11/15/2006 11/15/2007 3.4 9.2 47 0.25

Kathmandu Durbar Square 11/16/2006 11/1/2007 4.2 22.6 46 0.35

Teaching Hospital 11/16/2006 10/2/2007 2.2 21.2 49 0.28

Bouddha 11/16/2006 11/6/2007 3.7 28.3 37 0.22

Annexes

53

c) Particle deposition and chemical content of particles

Station Start date Stop date µg/cm2/month

mass Cl- NO3- SO42– NH4+ Ca2+ Mg2+ Na+ K+

ICIMOD 11/14/2006 1/18/2007 37 0.04 0.25 2.36 0.2 1.48 0.05 0.06 0.08

ICIMOD 1/18/2007 3/16/2007 26 0.03 0.24 2 0.16 1.12 0.05 0.09 0.07

ICIMOD 3/16/2007 5/13/2007 67 0.13 0.93 3.05 0.12 2.73 0.14 0.11 0.22

ICIMOD 5/13/2007 8/6/2007 27 0.1 0.37 0.55 0 1.09 0.03 0.03 0.05

ICIMOD 8/6/2007 10/4/2007 27 0.07 0.25 0.21 <0.001 0.53 0.02 <0.05 0.04

ICIMOD 10/4/2007 11/1/2007 20 0.07 0.33 0.22 0.02 1.31 0.16 0.32 0.07

Harisiddhi 11/15/2006 1/16/2007 – – – – – – – – –

Harisiddhi 1/16/2007 3/21/2007 – – – – – – – – –

Harisiddhi 3/21/2007 5/13/2007 93 0.24 0.57 4.7 0.08 3.13 0.09 0.53 0.59

Harisiddhi 5/13/2007 8/7/2007 36 0.12 0.25 0.68 0 0.81 0.02 0.1 0.42

Harisiddhi 8/7/2007 10/4/2007 7 <0.03 0.07 0.12 <0.006 0.26 0.01 <0.03 0.03

Harisiddhi 10/4/2007 11/7/2007 9 <0.05 0.11 0.1 <0.003 0.33 0.02 <0.07 0.05

Machen Gaun 11/14/2006 1/31/2007 11 0.02 0.17 0.29 0.04 0.3 0.04 <0.05 0.09

Machen Gaun 1/31/2007 3/21/2007 8 0.04 0.41 0.4 0.06 0.43 0.02 0.05 0.03

Machen Gaun 3/21/2007 5/14/2007 17 0.03 0.32 0.43 0.02 0.63 0.03 <0.08 0.07

Machen Gaun 5/14/2007 8/7/2007 9 0.02 0.16 0.18 0.01 0.26 0.01 <0.05 0.02

Machen Gaun 8/7/2007 10/2/2007 3 <0.02 0.04 0.05 <0.001 0.18 <0.012 <0.02 <0.04

Machen Gaun 10/2/2007 11/15/2007 6 <0.03 0.06 0.07 <0.003 0.11 <0.015 <0.03 <0.05

Patan Durbar Square 11/15/2006 1/31/2007 69 0.15 0.49 1.49 0.18 1.92 0.08 0.16 0.12




Patan Durbar Square 8/7/2007 10/2/2007 71 0.15 0.49 0.43 <0.001 1.48 0.04 0.09 0.06


Singh Durbar, Ministry of Env. 11/15/2006 1/31/2007 14 0.02 0.12 0.35 0.06 0.43 0.02 <0.04 0.03

Singh Durbar, Ministry of Env. 1/31/2007 3/21/2007 27 0.03 0.2 0.53 0.05 0.74 0.03 <0.06 0.05

Singh Durbar, Ministry of Env. 3/21/2007 5/14/2007 36 0.06 0.33 0.75 0.04 1.09 0.04 0.04 0.1

Singh Durbar, Ministry of Env. 5/14/2007 8/2/2007 47 0.04 0.17 0.14 <0.002 0.5 0.01 0.05 0.04

Singh Durbar, Ministry of Env. 8/2/2007 10/2/2007 25 0.05 0.28 0.2 <0.004 0.84 0.02 <0.04 0.03

Singh Durbar, Ministry of Env. 10/2/2007 11/6/2007 13 <0.05 0.13 0.08 <0.003 0.29 0.02 <0.06 <0.07

Tribhuvan University, Kirtipur 11/15/2006 1/31/2007 11 0.01 0.07 0.25 0.04 0.28 0.02 0.03 0.02


Tribhuvan University, Kirtipur 3/21/2007 5/14/2007 22 0.04 0.26 0.48 0.04 0.68 0.03 <0.08 0.12

Tribhuvan University, Kirtipur 5/14/2007 8/5/2007 12 0.04 0.14 0.12 <0.002 0.24 0.01 <0.03 0.07

Tribhuvan University, Kirtipur 8/5/2007 10/2/2007 7 0.02 0.08 0.08 <0.004 0.21 0.01 <0.02 <0.03

Tribhuvan University, Kirtipur 10/2/2007 11/15/2007 6 <0.03 0.07 0.05 <0.003 0.21 0.02 0.07 <0.04

Kathmandu Durbar Square 11/16/2006 1/31/2007 57 0.14 0.57 1.28 0.16 1.58 0.07 0.12 0.12



Kathmandu Durbar Square 5/15/2007 8/7/2007 39 – – – – – – – –

Kathmandu Durbar Square 8/7/2007 10/2/2007 35 0.11 0.36 0.29 <0.006 0.87 0.03 <0.08 0.05

Kathmandu Durbar Square 10/2/2007 11/1/2007 73 0.24 0.73 0.52 <0.003 2.06 0.08 0.18 0.14

Teaching Hospital 11/16/2006 1/31/2007 43 0.3 0.34 0.69 0.03 1.45 0.07 0.08 0.07




Teaching Hospital 8/7/2007 10/2/2007 30 0.05 0.28 0.26 <0.001 0.91 0.02 <0.06 <0.04

Bouddha 11/16/2006 1/31/2007 34 0.07 0.38 0.65 0.08 1.33 0.04 0.05 0.04

Bouddha 1/31/2007 3/22/2007 34 0.06 0.49 0.74 0.09 1.58 0.06 0.06 0.05

Bouddha 3/21/2007 5/14/2007 50 0.08 0.45 0.63 0.03 1.69 0.05 0.1 0.08

Bouddha 5/14/2007 8/7/2007 116 0.49 0.76 1.05 0 2.87 0.07 0.15 0.15

Bouddha 8/7/2007 10/2/2007 56 0.12 0.43 0.38 1.28 0.03 <0.07 0.03

Bouddha 10/2/2007 11/6/2007 75 0.23 0.59 0.4 0.01 2.45 0.06 0.09 0.08


54

d) Annual averages of particle deposition and chemical content of particles at nine sites

Station Start date Stop date µg/cm2/month

mass Cl- NO3- SO42- NH4+ Ca2+ Mg2+ Na+ K+

ICIMOD 11/14/2006 11/1/2007 35 0.08 0.40 1.45 0.09 1.36 0.06 0.09 0.09

Harisiddhi 11/15/2006 11/7/2007 38 0.12 0.26 1.38 0.02 1.13 0.03 0.18 0.31

Machen Gaun 11/14/2006 11/15/2007 9 0.02 0.19 0.24 0.02 0.32 0.02 0.05 0.05


Singh Durbar, Ministry of Env. 11/15/2006 11/6/2007 28 0.04 0.20 0.34 0.03 0.64 0.02 0.05 0.05




Bouddha 11/16/2006 11/6/2007 63 0.19 0.53 0.69 0.04 1.89 0.05 0.09 0.08

Annexes

55

Annex 9: Summarized Results of the Emissions Inventory for the Kathmandu Valley

Sector Total emissions (kilotonnes/year)

SO2 NOx CO NMVOC NH3 PM10 PM2.5 CO2

Combustion in energy industries 0.021 0.114 0.003 0.001 0.000 0.001 0.001 13.340

Combustion in manufacturing industries 3.014 0.838 0.389 0.053 0.000 – – 274.063

Transport 0.388 11.371 20.051 5.086 0.092 85.199 13.472 446.689

Combustion in other sectors 0.655 0.818 40.121 3.945 0.523 1.0578 0.852 588.771

Industrial processes – – – 0.787 – 0.3183 0.541 10.205

Agriculture 0.008 0.089 1.057 0.086 1.705 0.0867 0.087 –

Waste 0.011 0.066 0.928 0.074 1.298 0.4199 0.384 –

Total anthropogenic 4.097 13.297 62.548 10.032 3.618 87.083 15.338 1,333.068


56

Reviewers

Nguyen Thi Kim Oanh, Asian Institute of Technology, Thailand

Karin Sjöberg, IVL Swedish Environmental Research Institute

Martin Ferm, IVL Swedish Environmental Research Institute

Marie Hager-Eugensson, IVL Swedish Environmental Research Institute

Technical support

IVL Swedish Environmental Research Institute

Marcus Liljeberg

Milla Malander

United Nations Environment Programme (UNEP)

Iyngararasan Mylvakanam

Wah Wah Htoo

1. Introduction

57

About ICIMOD

The International Centre for Integrated Mountain Development, ICIMOD, is a regional knowledge development and learning centre serving the eight regional member countries of the Hindu Kush Himalayas – Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan – and based in Kathmandu, Nepal. Globalization and climate change have an increasing influence on the stability of fragile mountain ecosystems and the livelihoods of mountain people. ICIMOD aims to assist mountain people to understand these changes, adapt to them, and make the most of new opportunities, while addressing upstream-downstream issues. We support regional transboundary programmes through partnership with regional partner institutions, facilitate the exchange of experience, and serve as a regional knowledge hub. We strengthen networking among regional and global centres of excellence. Overall, we are working to develop an economically and environmentally sound mountain ecosystem to improve the living standards of mountain populations and to sustain vital ecosystem services for the billions of people living downstream – now, and for the future.

ICIMOD gratefully acknowledges the support of its core and programme donors: the Governments of Afghanistan, Austria,

Bangladesh, Bhutan, China, Germany, India, Myanmar, Nepal, Norway, Pakistan, Sweden, and Switzerland, and the

International Fund for Agricultural Development (IFAD).


58

International Centre for Integrated Mountain DevelopmentGPO Box 3226, Kathmandu, NepalTel +977-1-5003222 Fax +977-1-5003299Email [email protected] Web www.icimod.org

ISBN 978 92 9115 267 4

urban assessment of air quality

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