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Impact of the China Pakistan Economic Corridoron snow cover and glaciers in the Himalayan,Karakorum, and Hindukush regionLiaqat Karim

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Impact of the China Pakistan Economic Corridor on snow cover and glaciers in the

Himalayan, Karakorum, and Hindukush region

A Thesis Presented

by

Liaqat Karim

Submitted to the Graduate School of the

University of Massachusetts Amherst in partial fulfillment

of the requirements for the degree of

Master of Science in Environmental Engineering

December 2017

Environmental and Water Resources Engineering

ii

Impact of the China Pakistan Economic Corridor on snow cover and glaciers in the

Himalayan, Karakorum and Hindukush region

A Thesis Presented

by

Liaqat Karim

Approved as to style and content by:

iii

Dedicated

To

My mom, Mrs. Bibi Hakim, who despite being unschooled ensured I attended

school and did my homework on time. She never compromised on my education and

facilitated me with all the comfort she could so that I could focus on my studies.

&

My first boss and mentor, late Mr. Hidayat Hassan, who polished my skills and

introduced me to a new thinking process during my first two professional years at Hagler

Baily Pakistan.

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ACKNOWLEDGMENTS

My deepest gratitude to Dr. Colin Gleason for investing time and effort in this

project. His resolute dedication and enthusiasm towards this study made it possible for me

to complete this study in a very short time period, despite a lot of complications. I am

grateful to him for putting trust in me and accepting me as his student on an eleventh hour

request.

I would also like to thank Dr. David Reckhow for his encouragement and support

during last two years. Without his support, I would have never been able to work on this

project. Apart from being a very supportive academic advisor, he has been a great human

being.

Since this degree was fully funded by Fulbright program, I am eternally obliged to

Fulbright Program for the monetary investment in me.

I would like to thank my siblings and sister-in-laws for their continuous support

who have always encouraged me towards excellence. I would also like to thank my friends

too, who supported me during last two years. Without their emotional support this journey

would have been difficult.

I would like to thank Ghulam Murtaza, ArcGIS expert at Hagler Bailly Pakistan,

who introduced me to ArcGIS and guided me.

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ABSTRACT

Impact of the China Pakistan Economic Corridor on snow cover and glaciers in the

Himalayan, Karakorum, and Hindukush region

December, 2017

LIAQAT KARIM, MS., COLLEGE OF ENGINEERING

UNIVERSITY OF MASSACHUSETTS AMHERST

Directed by: Assistant Professor Dr. Colin J. Gleason

The Himalayan, Karakorum, and Hindukush (HKH) region is home to some of the

world’s largest glaciers. These glaciers provide water resources for local people, yet

numerous studies suggest a rapid decrease in their volume in recent decades. The change

in the albedo of snow due to deposition of particulate matter (PM) is an important driver

of this melting in addition to increased temperature. Here, we assess the change in albedo

and melt rate within 50 km of Karakorum Highway (KKH) due to the increase in the traffic

from China Pakistan Economic Corridor (CPEC), the multi-billion dollar flagship project

under the Chinese initiative ‘One Belt, One Road’. We seek to quantify the effect of CPEC

by coupling models of PM emission, dispersion, deposition, and ultimately concentration

within the snowpack, thus yielding a change in albedo for a five-year period. Data are scare

in the region, thus we have used a scenario-based approach to encompass a range of

possible futures considering various combinations of fleet age, silt loading (dust

production), and traffic increases across 27 scenarios. Results indicate that proximity to the

KKH is important, as snow within 20 km of the KKH will experience 35 % more change

in albedo as compared to snow further away. We also show that albedo change is highly

sensitive to its drivers, and CPEC would have minimal effect if the vehicles are new and

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silt loading is controlled, regardless of the increase in traffic. However, if vehicles are not

regulated for emissions and roads are not maintained, our results ultimately indicate that

the CPEC could permanently alter the energy balance of the HKH via deposition of large

amounts of PM.

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Table of Contents

1. Introduction ..................................................................................... 1-1

2. Data and Methodology ................................................................... 2-7

2.1 Study Area .................................................................................................2-7

2.2 Traffic Forecast .........................................................................................2-7

2.3 Emission Calculations ..............................................................................2-9

2.3.1 MOVES2014a for Emissions from Exhaust, Brake Wear, and Tire Wear .................................................................................................2-9

2.3.2 AP-42 Emission Factors for Emission from Suspended Material on Road ............................................................................................... 2-10

2.3.3 Total Emissions ............................................................................... 2-12

2.4 Dispersion and deposition Modeling ..................................................... 2-13

2.4.1 Model .............................................................................................. 2-13

2.4.2 Model Sources and Receptors ........................................................ 2-14

2.4.3 Meteorological Data ........................................................................ 2-16

2.4.4 Model Inputs ................................................................................... 2-17

2.4.5 Model Flow Chart ............................................................................ 2-19

2.4.6 Scenarios ........................................................................................ 2-19

2.5 Concentration of Deposits in Snow ....................................................... 2-20

2.6 Albedo Changes ...................................................................................... 2-21

2.7 Glacial Melting ......................................................................................... 2-23

3. Results ........................................................................................... 3-24

3.1 Deposition on Snow and Change in Albedo.......................................... 3-24

3.2 Spatial Variation in Snow Albedo Decrease .......................................... 3-29

3.3 Change in Melting Rate ........................................................................... 3-30

4. Discussion ..................................................................................... 4-32

4.1 Change in Snow Albedo and Melting ..................................................... 4-32

4.2 Scenario Analysis ................................................................................... 4-34

4.3 Limitations of the Current Analysis ....................................................... 4-36

5. Conclusion .................................................................................... 5-38

6. References ..................................................................................... 6-40

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LIST OF TABLES Table 1: Baseline Traffic Count on KKH (WAPDA, 2012) and Emission Factors ........ 2-10

Table 2: Summary of variables and ranges used for modeling ................................... 2-19

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LIST OF FIGURES

Figure 1: Study area with snow cover and receptors used for AERMOD modeling .... 2-16

Figure 2: Description of the methodology in this study ............................................... 2-19

Figure 3: Change in albedo for old and new snow, and overall deposition and concentration in old snow. ................................................................... 3-25

Figure 4: Relative sensitivity between number of vehicles, age of fleet, sL, and albedo change for old and new snow.. ............................................................ 3-28

Figure 5: Change in Albedo for a moderate scenario (traffic 10 times the BT, 50% vehicles from 1990 and 50% from 2017, sL= 0.6 g/m2).. ...................... 3-29

Figure 6: Concentration of BCeqv in Snow for Moderate Scenario and Old Snow ....... 3-31

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1. Introduction

The greater Himalayan region (Hindu Kush, Karakoram, and Himalaya-HKH),

has some of the largest nonpolar glaciers on earth. Many of the world’s largest rivers

emerge from the HKH (Indus, Ganges, Brahmaputra, Mekong, Yellow and Yangtze),

and these provide water to more than 1.4 billion people in south Asia (BIPSS, 2017;

Immerzeel et al., 2016; Zhang et al., 2010; Zhou et al., 2013).Water in these rivers,

especially during the dry season, is highly dependent on the rate of glacial melt: melting

snow and ice contributes about 70 % of summer flow in the main Ganges, Indus, Tarim,

and Kabul Rivers, and 50–60 % in other major rivers during non-monsoon seasons

(Immerzeel et al., 2016; Jeelani et al., 2012; Ming et al., 2015; P. Singh et al.,, 2004;

Singh et al, 1997). In addition to the billions of people downstream, small mountainous

communities living in the valleys between the mountains of the HKH are even more

sensitive to changes in the fragile glacial environment. This sensitivity exacerbates

harsh living conditions for locals given their dependence on local water resources and

associated food insecurity, Glacial Lake Outburst Floods (GLOF), fragile economy,

and limited representation in national and international politics (Bajracharya et al.,

2016; Nogués-Bravo et al., 2007).

Dependence on cryospheric resources in the HKH thus creates obvious concerns

as our climate alters towards an uncertain future. It has been suggested that some areas

of the most populated regions on Earth, including western China and India, are likely

to ‘run out of water’ during the dry season if the current warming and glacial melting

trends continue for several more decades (Barnett et al., 2005). There is consensus

among scientists that glaciers in Asia are losing mass every year, but the rate at which

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they are melting is less certain. Barnett et al., [2005] suggested an annual mass loss of

4 ± 20 Gt yr−1 for glaciers in HKH region as compared to 47–55  Gt yr−1 published by

Matsuo & Heki, [2010] between 2002 and 2010. A study conducted in Himalayan

valleys near Mount Everest showed that glacier surface area had loss of 14.3±5.9 %

(0.27 % yr-1) from 396.2 km2 to 339.5 km2 in 1958 to 2011 with the loss by 0.12 % yr-

1 in 1958-75 and 0.70 % yr-1 in recent years; with smaller glaciers (surface area < 1km2)

having a more rapid disappearance rate, resulting in up to 43 % glacier disappearance

during these years (Thakuri et al., 2013). While the globally averaged mass balance of

glaciers and ice caps is negative, an anomalous gain of +0.11±0.22 m yr−1 water

equivalent has been reported for some glaciers in Karakorum range (Bolch et al., 2017;

Gardelle et al., 2012; Hewitt, 2005; Minora et al., 2013). Bolch et al., [2017] showed

that the glaciers in Hunza River basin (central Karakoram) maintained a balance or

insignificant loss in glacier mass between 1973 and 2009. Despite this, there is little

doubt that the glaciers across the HKH region are losing mass due to increase in surface

temperature.

Beyond the macro-scale changes in climate that have increased HKH glacial

melting, there is another mechanism that has potential to amplify warming trends: the

change in snow albedo due to deposition of pollutants including dust and black carbon

(BC) (Barnett et al., 2005; Box et al., 2012; Brock et al., 2000; Brun et al., 2015;

Dumont et al., 2012). For instance, deposition of 5 to 50 ng of BC per gram of snow

(ng/g) can reduce the broadband (0.3–2.8 µm) albedo of snow by as much as 0.04,

depending on snow grain size (Warren & Wiscombe, 1985). In the pan-Arctic, 25 ng/g

BC has resulted a general albedo reduction of 0.02 for glaciated surfaces (Warren &

Clarke, 1990). These albedo changes of 0.02-0.04 are undetectable by either the naked

eye and satellites, but even this small change in albedo is significant for climate and

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snow melting (Hansen et al.,, 2012; Jacobson, 2004). However, albedo change from BC

is sometimes easily observed with the naked eye, especially as it accumulates over time.

Experiments conducted by Conway et al [1996] on Blue Glacier in the Olympic

Mountains of Washington, USA, showed that BC deposited on snow has long-term

impacts; a 30 % decrease in snow albedo could cause an increase in the snow ablation

(mass loss) by 50%. Therefore, the effects of BC on increased melt are undisputed, and

BC released from anthropogenic activities could be a cause of increase in global glacial

melt, especially in areas where there is economic activity nearby glaciers.

BC is not the only airborne particulate matter than influences albedo, as airborne

dust has a similar particle size but has not been studied in detail (Hansen et al., 2005;

Hansen et al., 2004; Painter et al., 2007). Doherty et al. [2010] showed that non BC

constituents, inlcuding brown carbon and dust, absorb 40 % of total absorbed light in

artic snow and 50 % in Greenland. Painter et al. [2007] showed that the distrubed desert

dust near snow covered mountain range in San Juan Mountains, USA shortened the

snow cover period by an order of 1 month. These results suggest that dust causes

significant changes in albedo when accumulated on the snow on large amount. We here

follow results from Warren [1984] for estimating the effect of dust, which suggested

that dust particles are 50 times less affective in decreasing the albedo of snow (Aamaas

et al., 2011; Doherty et al., 2010; Vogelmann et al., 1988; S. G. Warren, 1984).

Apart from changing the albedo after deposition, Particulate Matter (PM- BC

and dust) in the atmosphere absorbs solar radiation, hence warming the surrounding air

and blocking radiation to the earth; causing a cooling effect on low altitude areas and a

warming effect on high altitude areas (Ramanathan et al., 2007). This means that

particulate matter enhances melting of snow/ice in two ways; first by decreasing the

albedo of snow and second by warming the air at higher altitudes where glaciers are

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typically located. In sum, the impact of PM on glaciers in the Himalayas is as

substaintial as the impact of climate warming (Brun et al., 2015; V. Ramanathan &

Carmichael, 2016).

Previous studies have shown that the HKH region receives BC from northern

India, central China, Nepal, Pakistan, the Middle East, and Africa (R. Gurung et al.,

2011). This region also recieves dust in large amounts from various sources, inlcuding

local land sliding events and wood and fossil fuel burning (Gustafsson et al., 2009).

Wood burning is particularly prevalent as small communities living in these small

valleys depend on wood burning to stay warm during the winters. Therefore, the HKH

region receives up to 500 ug/m2/yr BC from a variety of sources (UNEP, 2011): high

elevation and concentrated mountains provide an effective barrier and makes this region

a dust sink. For instance Hispar glacier, situated in the Karakoram, received

422 ug/m2/yr to 672 ug/m2/yr of dust during the period between 1986-1988 alone

(Wake, et al., 1994). Therefore, any activity which could be a source of air pollution in

HKH will also increase deposition of those pollutants on the local glaciers and an

increase in the melting rate of the snow.

The HKH region is now faced with such an activity: the creation of the China

Pakistan Economic Corridor (CPEC). CPEC is a multi-billion flagship project under

the Chinese initiative ‘One Belt, One Road’ (OBOR). The objective of CPEC is to

revive the old Silk Road which connected China to Europe between the 3rd century

BCE and 14th century CE (Fallon, 2015). CPEC is a combination of new transportation

infrastructure (roads, railways), energy development, and industrial development

projects. The goal of CPEC is to link Kashghar in Xinjiang, an autonomous region in

northwest China, with the Gwadar Port on the Arabian Sea in Baluchistan, Pakistan.

Completion of CPEC will decrease the shipping time for imports and exports to and

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from China by almost 30 days, eventually lowering the shipping cost – all while

avoiding contested routes near countries like Taiwan, Vietnam, the Philippines,

Indonesia, and India (Chowdhary, 2015). China is investing 62 billion USD on this

project (Ministry of Planning, 2016b). A critical facet of this project is the expansion

of the Karakorum Highway (KKH), as the KKH is the only viable land route between

Kashghar and the interior of Pakistan. The KKH passes through valleys between high

mountains in the HKH region of western China and northern Pakistan. A railway track

alongside the KKH is under review for feasibility, but is not yet approved (Ministry of

Planning, 2016a). Thus, KKH is expected to see a significant increase in the traffic after

CPEC commences (Sareen, 2016). According to an anonymous employee working at

the Sost Dry Port, Hunza GilgitBaltistan, Sost dry port is expecting a minimum of 500

Chinese containers every day after CPEC, each of which is equivalent to three 2-axle

Pakistani trucks. This increase in the traffic will increase the air pollution in the valleys,

potentially changing the amount of pollutant deposition on snow and glaciers in the

area and thus decreasing the snow albedo and increasing the meting rate of snow.

Therefore, we have here estimated the expected change in albedo resulting from

CPEC (and thus glacial melt and water resource availability) for the KKH corridor for

a 5 year period from 2012 to 2016. To do so, we first modeled traffic and dust emissions

from the KKH, then coupled this to the airborne dispersion model AERMOD, and

finally calculated change in albedo given deposition of BC and dust resulting from

increased traffic. Given the uncertainty surrounding both the pace and scale of

development, we have adopted a scenarios-based approach, which gives the resulting

albedo change for a range of possible future increases in traffic and other physical

controls on emissions. These increases are modelled with reanalysis data, which allows

for more accurate estimates of the atmospheric parameters that control pollution at the

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cost of anticipating future changes in HKH climate. Thus, our results are conservative,

as they assume a stationary climate, but this approach mitigates climate uncertainty and

allows us to assess economic uncertainty. This manuscript details our procedures and

highlights our findings, concluding with a synthesis of likely future outcomes.

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2. Data and Methodology

2.1 Study Area

We here study a 765 km section of the KKH starting near Kashgar, China

(38.98N, 75.52E) and ending near Besham, Pakistan (35.53N, 73.57E). We use a buffer

of 50 km around this road to model the impacts of increased traffic, and this study area

includes the alpine glaciers located in northern Pakistan and western China along KKH.

This includes an area of 1508 km2 snow cover, which was manually digitized using

high resolution Google Earth imagery. We here consider only the white snow cover that

is currently unaffected by PM to assess the impact of the CPEC.

2.2 Traffic Forecast

It is expected that the traffic on KKH will drastically increase after the

commencement of CPEC related projects, but the exact amount of expected traffic is

still unknown as neither China nor Pakistan have published any projections . However,

we assume that China will utilize KKH to its full extent, as KKH will be a shorter route

for China to import and export goods into and out of its Northwestern interior. Based

on the imports and exports of China in recent years, and the projection of future

economic growth, we can expect a large increase in the volume of traffic. In 2015,

China imported 1,185 billion USD and exported 823.6 billion USD, with a positive

trade balance of 361.5 billion USD (EIA, 2015). Even if a fraction of these imports and

exports are transported through KKH, the traffic will increase exponentially. For

instance, in April 2015, China's total crude oil imports were over 7 million barrels per

day (bbl/d) rising at a rate of 19 % as compared to 6.2 million bbl/d in 2014. Just a

million bbl/d of crude oil would need more than 10,000 trucks per day (Sareen, 2016).

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Since the number of trucks cannot be estimated precisely, we have used a scenarios

based method to assess the impact of increased volume of traffic.

We have used a baseline traffic count, hereafter BT, (Table 1) conducted on

KKH to forecast traffic for our scenarios (WAPDA, 2012). The traffic count for study

was conducted in southern part of study area, hence it might overestimate the traffic on

northern part of KKH as it includes traffic to and from the valleys between Kashghar,

China and Besham, Pakistan. However, this traffic survey was carried during March

when trade between China and Pakistan is closed due to heavy snowfall in the northern

Pakistan, and therefore it might underestimate the actual baseline conditions, especially

for the months between April and November. However, this is the most reliable

available data about current traffic on KKH, therefore this was used as a BT. Based on

this BT, we calculated average number of vehicles traveling within the study area. This

was done by assuming a uniform speed of 40 km and an average number of 20 work

hours each day for all types of vehicles and therefore an Average Daily Distance

Travelled (ADDT) by all vehicles of 800 km/day. The length of road to be modeled

(765 km) was divided by ADDT (800 km) and multiplied by the number of vehicles

counted at a given point (Table 1) to determine the total number of vehicles in the study

area. Change in speed and work hours won’t change the emission rate because these

have inverse effects on emission factors for a fixed number of vehicles per day. For

instance, increasing speed would increase the distance travelled by vehicles, but will

decrease the number of vehicles in the study area: this increase in emission due to more

distance is proportional to the decrease in emissions due to fewer vehicles occupying

the road at any given time.

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2.3 Emission Calculations

USEPA categorizes emission from roads into two types; 1) the emission from

exhaust, brake wear, and tire wear, which should be estimated using EPA’s MOVES

model, and 2) the emission from the suspended material on the road, which should be

estimated using AP-42 factors (USEPA, 2017). Assuming all BC in the emissions

comes from type 1, we have estimated the BC from the vehicles using MOVES.

2.3.1 MOVES2014a for Emissions from Exhaust, Brake Wear, and Tire Wear

MOVES2014a is the latest version of a model that estimates emissions from

exhaust, brake wear, and tire wear. The input parameters required by MOVES include

population and vehicle miles traveled (VMT) by vehicle type; temporal and spatial

distributions of VMT by vehicle type; age distribution and average speed distribution

by vehicle type; road type distribution; ramp fractions and types; formulations and

market shares of fuels; inspection and maintenance programs by vehicle type by model

year; and ambient meteorological conditions. Most of these parameters are not available

for our study area, therefore, we have used default values in MOVES, which represent

data for USA (Koupal et al., 2014; USEPA, 2017b). By using default parameters, we

have likely underestimated the actual emissions on KKH because emission controls on

trucks are not strictly implemented in Pakistan, hence vehicles in Pakistan would

release more emissions as compared to vehicles in USA (Hartman et al., 1997; Qadir,

2002; Sánchez-Triana et al., 2014; Waheed et al., 2012). This model estimates emission

of PM (which includes BC) and BC (only) from vehicles based on their manufacture

year and type. Older vehicles have larger emissions as compared to newer ones,

therefore, we have estimated emission factors assuming best and worst case scenarios

of a completely new or a completely outdated fleet, using 1990 and 2017 as our example

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manufacture years. Emissions for vehicles manufactured in 1990 represent a worst case

scenario where the vehicles used are older and release larger amounts of PM, whereas

vehicles manufactured in 2017 are cleaner and release less amounts of PM. Emissions

from one vehicle for each vehicle type were estimated for one mile of travel and this

was used as an emission factor to calculate the emissions from the road by multiplying

it with the number of vehicles and the distance traveled. These emission factors are in

close agreement with emission factors estimated by Cai et al., [2013].

Table 1: Baseline Traffic Count on KKH and Emission Factors, using data from WAPDA, [2012].

Vehicle Type Weight BT on KKH

Total PM (g/miles/vehicle)

Black Carbon (g/miles/vehicle)

(Tons) Number/day Year 1990 Year 2015 Year 1990 Year 2015

Motor Cycle 0.3 242 0.0384 0.0383 0.007 0.005

Car 1.65 813 0.0350 0.0075 0.008 0.002

jeep 1.98 231 0.0350 0.0075 0.008 0.002

Pick Up 3 457 0.0438 0.0094 0.010 0.003

Mini Bus 10 291 1.1667 0.0298 0.900 0.002

Trucks (3-axle) 29 294 0.7847 0.0110 0.500 0.001

Heavy Loader (2-axle) 17.5 89 1.2829 0.0342 1.000 0.034

Tractors 2.3 45 0.7847 0.0110 0.500 0.001

Military vehicles. 2 44 0.0438 0.0094 0.010 0.003

2.3.2 AP-42 Emission Factors for Emission from Suspended Material on Road

Apart from the emissions from exhaust, brake wear, and tire wear, emissions

from lose material on road surfaces (the so called ‘surface loading’) is the other major

source of emissions from roads. USEPA recommends use of AP-42, Compilation of

Air Pollutant Emission Factors, for estimating emission from lose material on road

(USEPA, 2017a). We have used the latest version of AP-42 emission factors, [USEPA,

2011], for this Study. This version of the paved road emission factor equation only

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estimates particulate emissions from re-suspended road surface material. The emission

factors were estimated using following equation recommended by USEPA for paved

roads in AP-42.

Ext = [𝑘(𝑠𝐿)0.91 ×𝑊1.02](1 −𝑃

4𝑁) (1)

Where Ext = emission from road (same units as k), k = particle size multiplier for

particles (g/VKT), sL = silt loading, W = average vehicle weight, P = number of "wet"

days with at least 0.254 mm (0.01 in) of precipitation during the averaging period, and

N = number of days in the averaging period (e.g., 365 for annual, 30 for monthly).

USEPA recommended k values of 0.15 g/VKT and 0.65 g/VKT for PM2.5 and PM10,

respectively, have been used for the estimation.

USEPA recommends a value of 0.06 g/m2 for sL on paved roads, however this

is highly variable in different parts of the world. sL depends on the amount of suspended

material on road and the rate at which it is depleted from the road and is replenished

from sources near the road (USEPA, 2011). The value of sL is uncertain and changes

from time to time and place to place depending on geographic and weather conditions.

For instance, values of sL on a road in Shanghai changed from 0.17 – 1.28 g/m2 in 2007

to 0.17 – 0.72 g/m2 in 2014 (Wang et al., 2017). Wang et al., [2017] also found sL

values of up to 4.59 g/m2 for some roads in Shanghai, whereas Chen et al., [2012]

measured an average value of 3.82 g/m2 and a highest value of 24.22 g/m2 for roads

near downtown Beijing. Given the geography of the study area, it is expected that the

sL on the KKH will be highly variable and even greater than the values measured in

urban areas of China. Since KKH passes through mountainous slopes and is exposed to

large amounts of debris from the slopes above it; blockage of KKH due to debris is a

common phenomenon (Mir, 2017; PamirTimes, 2014). Literature review shows that no

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data regarding silt loading has been collected for this road in particular and this region

in general. Given the uncertainty revolving around sL and high dependency of emission

on sL, we have calculated emissions using three sL values that correspond to USEPA

recommended value for US roads (best case), and more likely values of sL that increase

by one and two orders of magnitude from this USEPA value.

USEPA recommends that users calculate the weighted average of the traffic to

be used in Equation 1 for ‘W’ therefore, we calculated the weighted average of vehicles

using the number of vehicle and the weight of each vehicle. The gross weights

mentioned in Table 1 have been used for calculating the average weight for whole fleet

of traffic, and the average weight of all types of vehicles except trucks are based on

average weights available for various manufacturers. For trucks the permissible gross

weight of trucks set by National Highway Authority (NHA) of Pakistan has been used

(NHA, 2017).

2.3.3 Total Emissions

We calculated total emissions given the emissions from vehicles and the road

discussed in the previous two sections. The emission factors for total PM including BC

from Section 2.3.1, which were in units of g/miles/veh for each vehicle type, were first

converted into units of g/km/veh. After unit conversion, emission factors from Section

2.3.1 were added to emission factors from Section 2.3.2 to get a single emission factor

for all PM in g/km/veh, which was multiplied with the number of a particular vehicle

type and the distance traveled by that type in one day. This gave us emission in g/day,

which was converted to g/s and divided by the area of road being modelled to get

emission in g/s/m2 as required by AERMOD (Section 2.4). We broke the KKH into six

discrete segments that act as uniform emission sources to minimize the model runtime,

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hence the KKH in the study area was modelled as six grids with one large segment of

KKH in the middle of each grid and results were combined to get the overall impact.

These large segments of KKH in the girds were further divided into 10 km road

segments within each grid to include the road curves.

2.4 Dispersion and deposition Modeling

2.4.1 Model

AERMOD, USEPA’s state-of-the-art regulatory model, is a steady-state

Gaussian plume model applicable to rural, urban, flat, and complex terrain areas with

point, area, and volume type emission sources. The deposition algorithms in AERMOD

are based on the equations discussed in Wesely et al., [2000] with some modifications

based on peer review (U.S. EPA, 2004a). AERMOD requires AERMAP, a terrain

preprocessor, to processes the terrain data provided as Digital Elevation Model (DEM)

files in conjunction with a layout of receptors and sources to be used in AERMOD

control files. DEM files from the ASTER GDEM, which is a product of METI and

NASA, were used for the study area (NASA Laboratory Jet Propulsion, 2016).

Moreover, AERMOD requires a preprocessor (AERMET) that organizes and processes

meteorological data and estimates the necessary boundary layer parameters for

dispersion calculations. Details of Met data will be discussed in Section 2.4.3.

AERMOD calculates hourly deposition using the location of emission sources and

weather conditions in the area at provided points, referred to as the receptors (Agency

et al., 2011; Cimorelli et al., 2005; Lee & Peters, 2004; Perry et al., 1994; U.S.

Environmental Protection Agency, 2016; U.S. EPA, 2004b; USEPA, 2015). The

runtime of AERMOD depends on the number of sources, number of receptors and time

period for which the analysis is being carried out. In this study we have tried to

14

minimize the runtime of the model without compromising the accuracy of results from

AERMOD, as fine-resolution modelling requires computations on the order of days.

The details about sources, receptors, DEM files, and meteorological data are discussed

below:

2.4.2 Model Sources and Receptors

Emissions from road sources can be modeled as line sources or adjacent volume

sources in AERMOD. Here we have modeled KKH as a compound line source. This

compound line is required as sources in AERMOD are treated as rectangular sources;

AERMOD connects the starting and ending coordinates with the width of road to make

a rectangular emission source, which misses the curves in the road. Therefore, a single

segment would both underestimate road length and intersect mountains instead of

remaining in the low-elevation valleys. We have chosen six segments as a balance: we

could divide the road into smaller segments to do a more precise representation, but this

would increase the runtime of AERMOD to the order of days for a single year’s

simulation. This large runtime is a byproduct of AERMOD’s intended use as a small-

area emissions models (e.g., smokestacks, intersections, single streets in a city). We

performed an initial sensitivity experiment modulating the length of these segments and

their resulting deposition on the snow cover areas. These tests showed that the

deposition on the snow cover wasn’t impacted when the length of segments was

increased from 2 km to 10 km provided the width of the road was increased to

compensate for missing road area lost due to generalization. This generalization does

greatly increase emission and deposition within 5 km of the road, so significant changes

in deposition were observed within this distance. However, since all glaciated surfaces

are greater than 5km from the road, this generalization decreased model runtime by

several order of magnitude without measurable impact on glacial deposition.

15

AERMOD models deposition at discrete receptors, which can be defined using

Cartesian grids, Polar grids, or Discrete Receptors(Lee & Peters, 2004). We used

discrete receptors to further decrease the runtime by only modeling deposition on

glaciated surfaces. We use the results from these points to interpolate deposition for the

whole snow cover area, and thus a greater number of receptors would give more precise

interpolation results at a cost of increased model runtime. Therefore, to get precise

results with minimum number of receptors, an initial assessment was carried out to

correlate the number of receptors and their impact on the overall deposition. This

assessment showed the deposition farther than 20 km from the KKH didn’t change

significantly with increase in number of receptors as the PM was physically limited by

the high elevation of the HKH at this distance. However, we determined that a denser

receptor network near the road resulted in more precise results for the area within 20

km of the road. Therefore, our final receptors on snow-covered areas within a 20 km of

KKH have a distance of 2 km between them, whereas outside this area the receptors

have 5 km distance between them.

Figure 1 shows the discrete receptor network, road, and snow cover used for this study.

16

Figure 1: Study area with snow cover and receptors used for AERMOD modeling

2.4.3 Meteorological Data

AERMOD requires hourly surface weather data and morning sounding data to

model deposition of PM. Surface weather data included dry bulb temperature, dew

17

point temperature, relative humidity, wind speed, wind direction, pressure and cloud

cover. Upper air data required are pressure, wind speed, wind direction, temperature

and relative humidity at several heights. Authorities like Pakistan Meteorological

Department (PMD), Pakistan EPA (PakEPA) and GilgitBaltistan EPA (GBEPA) were

contacted for these data, but none were willing to provide the required weather data.

Therefore, gridded 6 hourly reanalysis weather data at a spatial scale of 0.5˚×0.5˚ was

acquired from National Center for Atmospheric Research (NCAR) database for the

years between 2012 and 2016 (Saha et al., 2011) . These five-year data included the x-

component of wind, y-component of wind, temperature, pressure, precipitation, and

cloud cover for the study area in 12 discrete vertical layers.

These data were processed using AERMET and used in the AERMOD

deposition modeling. AERMOD only accepts single values for meteorological

parameters given its provenance as a small-area model. Thus, we were required to

summarize our gridded reanalysis data by taking the mean of all parameters within each

of our six model grids. This was another advantage of using a compound line for the

KKH, as we were able to capture more spatial heterogeneity in meteorology.

2.4.4 Model Inputs

For a line source, AEMOD requires following input parameters;

1. Location (starting and ending coordinates of the source/segment with elevation

above the Mean Sea Level (MSL))

2. Deposition Method: AERMOD has two options for deposition. Method 1 is

used when more than 10% of the particles have diameter of 10 µm or larger and

Method 2 is used when less than 10% of the particles have diameter of 10 µm

or larger. We have used Method 2 in this Study because road emissions typically

18

give PM smaller than PM10, and also because the size distribution of these

emissions is not well known (U.S. EPA, 2004a).

3. For Method 2 deposition, AERMOD requires the mean diameter of the particles

and the percent of PM2.5. These both were calculated using the emission rates

calculated in Section 2.3.3. For mean diameter of particles, weighted average

of 2.5 µm (PM2.5) and 10 µm (PM10) was calculated, whereas for percent of

PM2.5 particles, we used divided the emission rate of PM2.5 by emission rate of

PM10 and used that ratio.

4. Emission Rate: in g/s/m2 from Section 2.3.3

5. Initial vertical dimension and Average release height above ground: We used

USEPA recommend methods mentioned in Appendix J of U.S. EPA, [2015].

To calculate both these parameters the weighted average heights of all types of

vehicles in the fleet were used.

19

2.4.5 Model Flow Chart

Figure 2: Description of the methodology in this study

2.4.6 Scenarios

A total of 27 scenarios have been modelled for this study to analyze the sensitivity of

number of vehicles, vehicle age, and sL as a factorial of three plausible values for each

variable. The summary of variables used in the scenarios is shown in Table 2.

Table 2: Summary of variables and ranges used for modeling

Variable Range Scenarios Rationale

Traffic BT to 100x BT BT, 10x BT, and 50x BT

The increase in traffic after CPEC is not clear. BT, a moderate increase in BT, and a significant increase in BT capture our expectations of future traffic

Manufacture year of vehicles

1990 to 2017 1990 only, 2017only, 50% from each year

No data available for emissions from vehicles in Pakistan, therefore this combination

20

provides a good analysis for the dirty and cleaner vehicles

Silt Loading (sL) 0.06 - 6 g/m2 0.06, 0.6, 6

USEPA recommends 0.06 g/m2, however literature review shows that sL may go up to 24 g/m2 on dirty roads. Here we increase the USEPA recommended value by a factor of 10 and 100 to ensure we incorporate the variability of sL on KKH given the road passes though unstable slopes.

2.5 Concentration of Deposits in Snow

The deposition modeling ultimately determines how much PM, including dust

and BC, will be deposited on the snow surface. Using the emission factors mentioned

in Table 2, percentage of BC in the total emissions (Section 2.3.3) was calculated and

it was assumed that this same percentage of BC reaches the snow. BC is considered to

have a factor of 50 greater effect at decreasing the snow albedo than dust, thus BC is of

particular interest (Warren, 1984). However, Painter et al., [2007] concluded that large

amounts of dust on snow can reduce the snow cover period by magnitude of months,

therefore including the impact of dust on albedo is important. To do so, we have

converted the dust deposition into Equivalent BC (BCeqv) by assuming 50 units of dust

will have same impact on albedo as 1 unit of BC based on results from Warren, [1884].

BCeqv = BC + (PM-BC)/50 (2)

The concentration of these deposits physically interacts with snow as it ages,

and their effects are enriched in the top layer of snow over time because BC floats on

top of melted water and usually settles down on the top layer as the snow layers melt

(Doherty et al., 2010). The top 2 cm layer of the snow contains most of these deposits,

and thus this layer is reasonable for albedo reduction estimation (Aoki et al., 2000;

Tanikawa et al., 2009; Yasunari et al., 2010). This is consistent with literature as e.g.

Tanikawa, et al. [2009] showed that the top 2 cm layer snow had 8 to 15 times more

21

deposits as compared to layers between 2 cm and 30 cm. Based on these studies we

have assumed that deposits will be uniformly mixed in top 2 cm layer of the snow.

Concentration of BCeqv (ppb) in snow controls the albedo of snow (Hadley &

Kirchstetter, 2012), and is inversely proportional to the density of snow for the same

amount of deposition (ng/m2), where deposition is the process whereby PM arrives on

the snow surface and concentration is the resulting mix of snow and PM. Therefore,

greater snow densities correspond to lower concentration of BCeqv for same deposition,

whereas snow with lesser density would show more concentration of BCeqv per unit

weight for same deposition, therefore, density of snow is an important variable.

Experiments conducted in 1985 by Hewitt [2011] at Biafo Glacier in Karakorum range

near the study area revealed that the density of snow varied between 250 kg/m3 and 650

kg/m3 depending on the age of snow. Experiments conducted by Fujita et al., [1998] at

Yala Glacier in Langtang Valley, Nepal during 1996 and 1998 showed that the density

of snow varied between 195 kg/m3 for new snow and 512 kg/m3 for older snow.

Yasunari et al., [2010b] using the results from Fujita et al., [1998] estimated the impact

of BC deposition on Himalayan glaciers for both new and old snow using 195 kg/m3

for new snow and 512 kg/m3 for the old snow. We do not have and could not obtain

estimates of snow density for this study, and as such we have used two values for

density of snow; 195 kg/ m3 for new snow and 512 kg/m3 for old snow. We assume that

snow age is uniform in the critical snow area within 20 km of the KKH, and thus this

forms another sensitivity factor we use to evaluate impacts of CPEC.

2.6 Albedo Changes

The change in albedo of snow due to BC depends on various factors including

snow grain size, snow density, snow depth and the interaction of sunlight with the

22

underlying surface, tree cover, and solar zenith angle (Brandt et al., 2011; Conway et

al., 1996; Grenfell & Light, 2002; Hadley et al., 2012). While all these variables have

an impact on snow albedo, zenith angle and grain size have the most significant impact

on the snow albedo for snow layers with more than 1 m thickness (Wiscombe &

Warren, 1980). Change in albedo due to BC contamination is directly related to the

grain size and inversely proportional to the zenith angle (Hadley & Kirchstetter, 2012;

Wiscombe & Warren, 1980): BC changes the albedo of snow more if the grain size is

larger or if the zenith angle is smaller.

Therefore, we are required to make assumptions about these two key parameters

to estimate changes in albedo due the deposition of PM. Butt [2012] estimated the snow

grain size our same study area and found that 94.5% of the snow grains were fine,

corresponding to a grain size of less than 200 μm. Moreover, the zenith angle in the

study area remains under 60° for most of the year, and due to high altitude of mountains

around the study area, a zenith angle more than 60° will not reach the snow given the

extreme topography. Therefore, we have used a grain size of 200 μm for the snow in

study area and have calculated the change in albedo for zenith angle of 60°. We have

estimated the change in albedo of snow due to BC based on Δα=0.0024 (BCeqv)k ,where

Δα is the change in albedo due to BC contamination, BCeqv is the concentration of BCeqv

in ppb, and k = 0.295 (Reff 0.114) (Reff is the effective grains size in µm).This is derived

using the empirical model from Hadley et al., [2012] and validated using results from

Hadley et al., [2012] for 60° zenith angle. We choose 60° for two reasons a) 60° is

considered as a more climatically relevant solar zenith to understand effects of BC on

climate (Hadley & Kirchstetter, 2012) b) 60° zenith angle predicts lesser reduction in

albedo due to BC as compared to 0°, and hence we avoid overestimation of albedo

reduction due to BC.

23

2.7 Glacial Melting

Glacial mass balance in the year 2000 for Karakoram and Himalaya is estimated

to be -6.6 ± 1 Gta−1 (Chaturvedi et al., 2014). However, Kääb et al. [2012] suggested a

mass loss of -12.8 ± 3.5 in the Himalayan region. Bajracharya et al. [2015] suggested a

slow but steady change in Hindukush region. Bolch et al. [2017] and Naz et al. [2008]

suggested a small increase in the glacier mass in some parts of Karakorum region,

however, Chaturvedi et al. [2014] concluded that by 2030, 5 % of glacier area in central

Karakorum and 13 % glacier area in western Himalayan region may disappear,

suggesting an overall loss of snow and glaciers in the study area. Thus, we expect that

albedo changes will exacerbate any current melting trends.

The conservation of energy dictates that a decreased albedo must result in

increased radiation at the snow surface, and thus increased melt. However, the

proportionality of this increased melt is heterogeneous. For instance, Conway et al

[1996] concluded that a decrease of 30 % in albedo caused an increase of 50 % in the

snow melting on Blue Glacier in Olympic Mountains of Washington, USA,; which

corresponds to ratio of ablation to albedo (A/alpha) of 1.6.. Yasunari et al. [2010]

estimated that an albedo change of 2.0 to 5.2 % could increase the annual discharge of

the Tibetan glacier by 11.6–33.9%; which corresponds to an A/alpha of almost 6.

Kohshima et al. [1993] calculated an increase of 30% in ablation due to a 10 % increase

in albedo of snow on Yala glacier, Nepal; which corresponds to an A/alpha of 3. Thus,

we might expect A/alpha to range from 1.6 to 6, and lacking any detailed measurements,

we again use these best and worst case scenarios to assess melt.

24

3. Results

3.1 Deposition on Snow and Change in Albedo

Our results indicate that all scenarios, including the best case baseline, show

significant deposition of PM on snow within the study area and the resulting change in

albedo ranges from 0.0 to 0.5 depending on the number of vehicles on KKH, age of

vehicles and the sL on road. Based on the BT mentioned Table 1, the average PM

deposition in the study area over the period of 5 years varies between 5.5 x104 ng/m2

(35% BC) for the best case baseline scenario [2017 fleet, 0.06 sL] and 3.5 x 107 ng/m2

(1.2 % BC) for the worst case baseline scenario [100% vehicles from 1990, sL=6 g/m2]

corresponding to average change in albedo between 0.004 – 0.03 for old snow and 0.007

– 0.051 for new snow, respectively. These results represent the range of possibilities

for emissions from BT even before the increase in traffic from CPEC. For the scenarios

where the traffic is 10 and 50 times BT, the minimum and maximum PM deposition in

the study area over the 5-year period increases by a factor of 9.9-10 and 47.8-50,

respectively. As expected, this deposition is inversely related to the distance from the

road and the altitude of the snow cover area: snow closest to the KKH will face the

greatest decrease in albedo. Glaciers within 20 km of KKH will face 35 % more change

in average albedo as compared to glaciers outside 20 km from KKH. Figure 3 shows

a summary of average change in albedo for all the scenarios, along with average

deposition and concentraion of BCeqv in old snow for all the scnerios.

25

Figure 3: Panel ‘a’ shows the change in albedo for old and new snow, whereas panel ‘b’ shows overall deposition and concentration in old snow for the 27 scenarios mentioned in Table 2. The error bars indicate the one-sigma spatial variability of each variable in the study area.

The change in albedo is higher for new snow due to lesser density and older

snow has lesser change in albedo due to higher density, hence lower concentration of

BCeqv. For instance, in a Moderate Scenario [traffic 10 times the BT, 50% vehicles from

1990 and 50% from 2017, sL= 0.6 g/m2], the average change in albedo for new snow is

0.157, whereas for the same scenario the average change in albedo for old snow is 0.093

corresponding to almost 40 % reduction in albedo. Figure 4 shows the sensitivity

between changes in albedo and other variables. Change in Albedo follows the same

trend as the deposition and concentration of BCeqv in snow: there is the greatest change

in albedo near the KKH and lesser change in albedo away from KKH (Figure 5). For

26

example, a maximum reduction of 0.13 in snow albedo close to KKH and minimum

reduction of 0.015 in snow albedo away from KKH is predicted for the moderate

scenario assuming old snow. For new snow, the maximum and minimum reduction in

albedo increases by 0.03 and 0.22, respectively. All scenarios will follow a similar

spatial trend. In an ideal case [2017 manufactured vehicles, sL=0.06 g/m2, traffic

remains same as BT], hereafter IC Scenario, there would be less than 0.007 reduction

in the albedo of both new and old snow. However, with any increase in traffic, sL or

age of the vehicles this reduction will increase and in a Worst Case [traffic 50 times the

BT, 50% vehicles from 1990 and 50% from 2017, sL=6 g/m2], hereafter WC Scenario,

the average albedo reduction will be 0.42 and 0.26 in new snow and old snow,

respectively.

Modeling shows that the amount of deposition on snow is most sensitive to

increases in traffic. Deposition increases linearly with increase in traffic if all the other

variables including sL, weather parameters, and the vehicle manufacturing date is kept

constant. This also assumes that future vehicle fleets have same ratio between different

vehicle types. For instance, if the ratio of trucks to passenger cars is 2.5, it should remain

2.5 in future for the emissions to increase with the same factor. After the number of

vehicles on KKH, the age of vehicles has the next largest impact on deposition of BC.

We expect vehicles manufactured in 1990 would emit 95.4% more PM (62 % BC) than

vehicles manufactured in 2017, hence increasing deposition of BC on the snow. Figure

4 clearly shows the importance of traffic and fleet age, as albedo change increases left

to right both across combinations of fleet age and sL and within each combination.

sL increases the total PM deposition significantly, but does not have as simple

a relationship to albedo as increases in traffic or fleet age: the effects of sL are most

evident with a newer fleet. This is because since it does not increase the deposition of

27

BC directly, it has lesser impact on change of albedo as compared to the number of

vehicles or the age of the fleet. For instance, a 100 % increase in sL increases the PM

deposition by almost 88 %, however this increase is 100% dust and doesn’t contain BC.

When converted into BCeqv, this is only 2 % increase in BCeqv. This could be seen in

Figure 3b, where average concentartion of BCeqv for scnearios with 1990 vehicle fleet

shows higher values than those of 2017, when comapared with the deposition values

(scenarios 26 and 27). This is because of greater emissions from exhaust in scenario 26

as it incorporates a 1990 vehicle fleet. However, deposition of dust could would

increase to a point where dust dominates the process of changing albedo as suggested

by Painter et al., [2007], given the large range of sL (0.06-6 g/m2). For example, with

sL= 6 g/m2 and fleet year of 2017, only 0.093 % of deposition will be from exhaust,

corresponding to only 0.058 % BC: the rest of the deposition will be from silt loading,

which still causes substantial albedo change (Figure 4, bottom right). Figure 4 clearly

shows that while fleet age is a dominant factor controlling albedo change, it is less

important at higher sL and that albedo change is most sensitive to sL as vehicles become

newer in age.

28

Figure 4: This figure shows the relative sensitivity between number of vehicles, age of fleet, sL, and albedo change for old and new snow. Each column reflects a static age of fleet as indicated at bottom, and each row has a constant sL, as indicated at right. The three x-axis values in each plot reflect the three traffic scenarios. Thus, this figure shows the overall sensitivity of our analysis and reveals that sL and fleet age have strong controls on emissions regardless of increases in traffic.

29

Figure 5: Change in Albedo for a moderate scenario (traffic 10 times the BT, 50% vehicles from 1990 and 50% from 2017, sL= 0.6 g/m2, old snow). Change in albedo is greatest near the road and less far away from the road. All the scenarios follow a similar trend for change in albedo.

3.2 Spatial Variation in Snow Albedo Decrease

If all of the snow in the study area were old, the results show that for the IC, the

change in albedo would be less than 0.01 for 99.98% of the snow cover in the study

30

area: effectively having no impact on glaciers. For the Moderate case scenario, 96.40%

of the snow cover will have more than 0.02 change in the albedo, whereas 27.20% will

experience more than 0.05 change and only 0.7% of the snow cover will experience an

albedo change of more than 0.09. In the WC, 100% snow cover will have more than

0.1 change in the snow albedo and more than 45 % snow cover will experience an

albedo change of more than 0.2. In sum, 20/27 scenarios suggest more than 75% of

snow cover will experience an albedo change of 0.02, and nine scenarios suggest more

than 75 % of snow cover will face an albedo change of 0.05 or more.

New snow will experience more change in the snow albedo. For instance, 26

scenarios suggest more than 0.02 decrease in snow albedo for at least 30% of the snow

cover with new snow and 23 scenarios suggest more than 0.02 change in albedo for at

least 75% of the snow cover in study area. For the new snow, fourteen scenarios,

including the Moderate Scenario, suggest more than 0.05 decrease in snow albedo for

at least 80 % of the snow cover.

3.3 Change in Melting Rate

We use the average change in albedo to predict average change in melting rate

for the study area. This obscures the spatial trends in melting, but allows us to easily

assess the overall impact of CPEC. The minimum potential change in melting rate

(ΔMmin) is estimated using a A/alpha=1.6 and old snow, and the maximum potential

melting rate (ΔMmax) is estimated using A/alpha =6 and old snow. In an IC Scenario,

the average change of albedo in study area is estimated to be 0.04%, which corresponds

to a ΔMmin of 0.7% and a ΔMmax of 2.50 %. For a Moderate Scenario, ΔMmin will be

8.0%, whereas, ΔMmax is estimated to be 30.3 %. For a WC Scenario the ΔMmin is

estimated to be 34.4 % and ΔMmax is estimated to be 129.6%.

31

Figure 6: Concentration of BCeqv in Snow for Moderate Scenario (traffic 10 times the BT, 50% vehicles from 1990 and 50% from 2017, sL= 0.6 g/m2) and Old Snow

32

4. Discussion

4.1 Change in Snow Albedo and Melting

Most of the mountain communities living in the HKH along KKH rely on

snowmelt for drinking and irrigation purposes, and since the effect of CPEC is greatest

near KKH, these communities will be directly impacted. Any change in albedo due to

higher deposition near the KKH will shorten the snow cover period, hence putting the

local populace on a risk of water shortage, especially during the late summer and fall

seasons. Apart from water shortage, deposition of pollutants from exhaust on drinking

water resources could have health impacts as emissions from the vehicles contain

pollutants like lead, which could have serious health problems in the end users. In the

longer term, we can envision an accumulation of deposits: our results are for just five

years. This accumulation will cause ablation close to the KKH, and ablation should also

occur further away from KKH. Lutz et al., [2016] concluded that the snowmelt in the

study area plays a major role in flooding as it exacerbates the level of flooding when

added to the heavy precipitations during monsoon season. Thus, early melting of snow

and overall increase in the melting will increase the intensity of flooding in downstream

basin. All told, our results suggest that CPEC could have a negative impact on the

water resources in the KKH, in particular if the fleet is older and sL is high.

We have shown that the number of vehicles is an obviously important factor,

and our results will confirm the intuition of many readers. However, we have shown

that emission from exhaust is also highly important. Since BC has 50 times more impact

on snow albedo as compared to dust, the age of the fleet will be the most important

factor in determining BC exhaust. Older vehicles emit up to 95.4% more PM as

33

compared to new ones, and since most of the PM from the exhaust is BC, this will

change albedo more. By controlling the emissions from exhaust, 95 % of the BC on

snow could be avoided, hence decreasing the impact on snow melt. In a moderate

scenario, only 8 % of the deposits will be from exhaust, but will cause up to 77 % of

the total change in albedo. Thus, minimizing impact of CPEC on water resources must

consider modernizing the fleet.

Emission from exhaust will account for all the BC from the road, but loose

material on road will add dust particles to air which will also settle on the snow and will

affect the albedo. As shown, this sL is most significant if vehicles are new and if the sL

on KKH is greater than 0.6 g/m2. Since KKH passes through mountainous slopes, dust

on the road is easily replenished, increasing the value of sL. The value of sL will be

highly variable for each segment of KKH, and change during different seasons, hence

it would not be appropriate for the study to choose a single sL value, especially if

expected value in this area is more than 0.06 g/m2, as the impact of sL gets significant

after this. In the worst case scenario, more than 99 % of the deposits will be from road

dust, rather than from the exhaust. However, controlling sL in the study area will be a

big challenge. This could be accomplished through extensive roadworks to prevent

erosion, but given the length of the KKH this will be costly. On the other hand,

emissions from the exhaust could easily be controlled by using strict emission limits on

vehicles. If the controls on exhaust emissions are fully implemented, the sL is still an

important consideration given results in Figure 4.

In general, for a given concertation of BC, change in albedo of snow is greater

for denser snow, however, in this study greater snow density results in a lesser change

in albedo. This is because the concentration of BCeqv in this study is inversely related

to snow density, as we convert deposition from AERMOD to BCeqv. We assume more

34

mass snow in the top 2 cm layer of snow by assuming a greater density, hence for same

amount of deposition the top layer has lesser amounts of BCeqv per unit mass of snow.

Figure 6 gives an example of this concentration. Although the exact amount of

deposition on snow cannot be determined due to lack of data, especially data related to

traffic, roads, and snow for the particular study area, our estimation for different

scenarios show a similar spatial trend and provide a good insight for various variables

that play an important role in the snow albedo reduction phenomenon due to BC and

dust contamination. We could use the results from this study to estimate deposition for

any combination of variables for the same weather data. Deposition on the snow is

directly related to the emission rate from the road, and it changes linearly with change

in emission rate.

4.2 Scenario Analysis

Our scenarios-based approach attempts to encompass a range of plausible future

conditions in the KKH. Based on literature, the Moderate Scenario seems to be the most

plausible future estimate of the study, but our results indicate the importance and

sensitivity of several factors that contribute to increased melt. Given the amount of

investment by Chinese government and their needs, we expect the traffic on KKH will

at least increase by a factor of 10 after CPEC is commenced. Moreover, due to the

unstable slopes along the KKH, we expect dust will be replenished quickly on the KKH

increasing the sL values, hence 0.6 g/m2 seems reasonable as an average. As discussed

in Section 2.3.1, vehicles in Pakistan are not monitored for exhaust emissions, therefore

most of the fleet will be emitting significant PM, so our assumption of half modern and

half old vehicles is conservative. Given this, we feel that the Moderate Scenario seems

to be a reasonable estimation of the impact of CPEC on glaciers in HKH region. This

scenario estimates an average change of 0.087 in new snow albedo in study area, with

35

a maximum of 0.21 near the road and minimum of 0.026 in the areas far away from the

road. Assuming a fresh snow albedo of 0.9, this average change in albedo corresponds

to a 9.6 % decrease in the snow albedo, and on basis of previous research this may

increase the snow melting rate by a minimum of 15.4 % and up to a maximum of

57.5 %. However, for older snow the average change in albedo is 0.05, which

corresponds to a 5.5 % increase in the snow albedo. This can increase the snow melting

rate by a minimum of 8.8 % and a maximum of 33 %. Hence, there will always be

combination of old and new snow in the study area, the change in melting rate will

remain between 8.8 % and 57.5 %. Since the snow density increases with time, old

snow changes will be more plausible, therefore the most plausible average change in

albedo would be 5.5 % corresponding to an increase in melting rate between 8.8% and

33 %.

Highest increase in the snowmelt would be experienced by snow near the KKH,

hence communities living near the KKH will be highly affected. Snow on taller

mountains and further away from the KKH will have lesser change in albedo, hence

less change in snowmelt would occur. Given the large snow cover near the KKH, and

dependency of communities on these water resources, even a small change in melting

rate becomes very significant. Since, small changes in albedo could reduce snow cover

period by a magnitude of month as suggested by Painter et al. [2007], this change in

albedo becomes highly important for the people of HKH region along KKH. A one

month decrease in snow cover period would make the water scarce season, late summer

and fall, worse for the local populace. In a longer run this will become more serious

and water availability will become an issue even during spring. Floods from the fast

melting will pose a threat to the local infrastructure including roads and water channels

in the area too.

36

4.3 Limitations of the Current Analysis

The limitations of this study primarily stem from a lack of data. We lack a

rigorous, year round traffic count on the KKH, we lack any observed weather data in

study area, we lack snow characteristics, we lack sL values, and we are unsure about

the extent and plans of specific CPEC projects. In addition, neither the Chinese and

Pakistani government have released any documents related to the exact number of

increase in traffic on KKH yet. Traffic on the KKH will ultimately depend on the

imports and exports of China also on the capacity of KKH. Moreover, we were unable

to obtain weather data as these data simply do not exist for the study area, except for

few stations (not available for research) which usually measure wind and temperature

data. AERMOD uses weather data to determine the deposition, therefore site specific

weather data could make the analysis more reliable. Thus, our use of global reanalysis

data is not as ideal as in situ data, especially considering the rugged mountain

environment. These data are also problematic in that they do not account for future

climate scenarios, but adding yet another layer of uncertainty was undesirable: our

results thus reflect if CPEC had occurred these past five years. Furthermore, emissions

from vehicular exhaust are not monitored in Pakistan, therefore, we don’t have access

to type and amount of pollutants released by the vehicles. It is well known that trucks

with diesel engines are used to transport goods in Pakistan, and these release huge

amount of PM if they are not maintained and inspected regularly. Hence, we have

assumed best, worst and a moderate vehicle fleet for our study. Using default database

about emissions from exhaust in MOVES2014a might underestimate the actual

emissions, given USA has stringent regulations about exhaust emissions. Snow

characterizes like density and grain size vary across the study area and we are forced to

assume either typical values or an average value for the study area. Since both density

37

and grain size play an important role in determining the change in albedo due to BC,

using actual measured density and grain size for different sections of the study area

would lend more confidence in our results. Finally, sL plays an important role regarding

deposition of dust on snow due to the KKH, but we don’t have data about sL for the

whole study area. We have used data from China and USA to define a range which

would cover the possible sL values on KKH. Addressing these data limitations would

be costly, time consuming, and politically and physically challenging in this

environment. Given the immediacy of CPEC, and the large impacts of some scenarios

in our results, we feel this study is important as a first demonstration of the impacts that

could be expected, despite our lack of data.

We have tried not to overestimate the changes due to deposition by analyzing

our results across multiple densities of snow, as a higher density of snow (512kg/m3)

decreases the concentration of BC for same deposition. Ultimately, our 27 scenarios

and two snow densities produce a map of possible effects for 54 different combinations

of physical variables. While these ranges are broad and rest on literature rather than

observation, we do feel this study provides a great insight to potential scenarios related

to impact of CPEC on the glaciers in KHK region. The results include all the potential

sources related to CPEC that could emit PM and cause change in albedo, and our results

demonstrate the need for a more thorough study related to impact of CPEC on the water

resources in HKH region.

38

5. Conclusion

This manuscript focuses on the impact of emissions from traffic related to

CPEC, a multi-billion flagship project (62 billion USD) between Pakistan and China,

on snowmelt in the HKH region: the biggest snow and glacier water reservoir outside

the Polar Regions. Due to lack of data and uncertainty about the projects related to

CPEC, we have used a scenarios- based approach to conduct this study. The results of

the study show that CPEC could have serious negative effects on water resources,

especially for glaciers nearest to the KKH. Depending on the proximity to the KKH,

for the most plausible scenario, snow in the study area will receive enough deposition

from emissions to change the albedo by 5.5 % on average in the study area

corresponding to an increase in melting rate between 8.8% and 33 %. This change in

melting rate would be more near the KKH and will be smaller away from the KKH

because more PM will be deposited near KKH. On average, glaciers within 20 km of

KKH will face 35 % more change in Albedo as compared with the glaciers outside

20 km. Apart from the increase in number of vehicles on KKH, the vehicle fleet age is

the most significant variable, as it determines the amount of BC on snow. However, sL

on KKH will define the amount of dust that will be deposited on the glaciers, and it can

cause significant albedo change even with a clean modern fleet.

This greatest change in melting rate will be on the glaciers closest to KKH and

hence closest to the communities living near KKH. Therefore, we expect CPEC will

have significant impacts on people living along KKH that depend on these snow related

water resources for drinking and irrigation purposes. Apart from putting them at a water

shortage risk due to decreased duration of melt season, deposition of metals and other

39

contaminants from the vehicular emission on their water resources could make

communities vulnerable to health related issues. Since most of the communities in these

small valleys do not have water treatment plants and water quality is not monitored in

the natural reservoirs, contamination in snow may not be detected immediately.

In future, site-specific data should be collected for this area and this study

should be updated to reflect actual conditions in the area. However, we feel that our

scenarios approach reflects plausible futures and suggests that mitigation is needed to

protect the glaciers of the HKH should their current state be considered desirable. We

find that 20/27 scenarios indicate at least 75 % of glacier area twill experience more

than a 0.02 decrease in albedo, and 9/27 scenarios indicate at least 75 % of glaciers will

face more than 0.05 decrease in albedo. Ultimately, our results indicate that controlling

fleet emission and silt loading could mitigate the negative impacts of increase traffic,

but if old vehicles are used on poorly maintained roads there is potential for permanent

undesirable changes to the glaciers in the region. Therefore, local actors should repeat

our analysis to reduce the uncertainty in this estimate or provide data to the international

community to do so.

40

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