SOURCE SPECIFIC FOG DEPOSITION OF BLACK CARBON FROM THE ATMOSPHERE

Applied Environmental Science Master's Programme School of Business, Engineering and Science

Halmstad University Sweden

SOURCE SPECIFIC FOG DEPOSITION OF BLACK CARBON

FROM THE ATMOSPHERE

Master’s thesis (15 ECTS)

Petra Dolšak

Supervisor:

Working supervisor:

Company:

Examiner:

Halmstad, 23 May 2016

Contents

Abstract

Acknowledgment Abbreviations

1. Introduction ... 5

1.1 Black Carbon ... 5

1.2 Aerosols Source Apportionment ... 6

1.3 Planetary Boundary Layer ... 7

1.4 Cloud Condensation Nuclei (CCN) ... 8

1.5 Fog Deposition of Black Carbon ... 9

1.6 Aim of the Study ... 10

2. Methods ... 11

2.1 Study area ... 11

2.2 Definition of fog situation ... 12

2.3 Data analysis ... 13

2.4 Aethalometer ... 14

3. Results ... 15

3.1 Meteorological conditions ... 15

3.2 Diurnal variation of BC ... 18

3.3 Concentration of Black Carbon during different durations of fog ... 21

3.4 Concentration of Black Carbon during constant durations of fog ... 24

4. Discussion ... 26

5. Conclusion ... 30

6. References ... 31

Summary/Abstract

Black carbon (BC) plays an important part in global climate change. In addition, long term exposure to BC is closely related to pulmonary and cardiovascular mortality. BC is formed by the incomplete combustion of carbonaceous compounds. In urban environments the main sources come from the burning of biomass for domestic heating and diesel vehicles. The typical lifespan of airborne BC is about a week and is treated as a short-lived climate pollutant. Wet deposition, which is more significant closer to the source, is the primary deposition mechanism and condensation of water is dependent on the sources of BC. Measurements with aethalometers determine the sources of BC concentrations, particularly fossil fuel combustion from traffic (ff) and wood smoke (bb).

The in-situ measurements in this study reveal that the different source apportionment of BC emissions with different initial properties of BC behaves differently during the fog periods. Foggy periods from the March and January 2015 data set were carefully collected. In January, the fog occurred throughout the entire observation time, while in March the fog occurred for different durations, from 1 to 7 hours. A linear regression between the normalized BC, BCbb, BCff concentrations and 7-hour periods at night was calculated for each individual period. The comparison of slope values (k), standard errors and p-values of different sources of specific BC emissions was then made. Despite there not being a great difference between the slope values of BCbb and BCff in the January data set, the results revealed that BC emissions from biomass burning have fewer non- statistically significant values than the BC emissions from vehicle exhaust. This study corresponds to the different initial properties of fresh aerosols from both sources and indicated an increased fog deposition of BC from biomass burning.

Key words: black carbon, fog deposition, source apportionment, CCN

Acknowledgments

I own a big thanks to my supervisor Dr. Marie Mattsson for helping me. At the beginning, it was a completely new field for both of us, but by taking a bite of the apple of BC emissions and their corresponding effects on fog deposition we arrived at a new droplet of knowledge in the field of air pollution. Throughout my work process, she supported me greatly and encouraged me with her teaching abilities and experiences in the area of greenhouse gases. The person who I would like to give a lot of credit to for finishing my thesis in a high-quality way that I am very proud of is Dr. Griša Močnik, my working mentor and managing director of Aerosols d.o.o., Research and Development Dept., Ljubljana, Slovenia. He is a very strong person, comfortable in his field, with a comprehensive knowledge of BC emissions, which he gained by publishing various scientific papers on that topic and by attending conferences all over the world. He has guided me towards reaching my full potential and performance. I would also like to thank Dr. Luka Drinovec, who lead a research team at Aerosol d.o.o., where enjoying work and helping each other comes first. He supported me with my analyses and offered me extraordinary support, assistance and friendship every time when I hit a wall. And finally, I would like to mention my family, boyfriend, friends and school colleagues, who encouraged me and helped me to finish my study abroad. I would not have made it without the joy and relaxation that you brought me.

ABBREVIATIONS

ACT Absolute coating thickness ARSO Slovenian Environment Agency BC Black Carbon

BCbb Black carbon from biomass burning BCff Black Carbon from fossil fuel CCN Cloud Condensation Nuclei EEA European Environmental Agency EF Emission factories

EPA United States Environmental Protection Agency EU European Union

IPPC Integrated pollution prevention and control PBL Planetary boundary layer

PM Particulate matter RH Relative Humidity

SLCP Short-lived climate pollutants WHO World Health Organization

1. Introduction

1.1 Black Carbon

One of the adverse air pollutants, concentrations of which regularly exceed limit values in EU Member States is PM. According to the EEA, PM is known as an aerosol and it is a suspension of fine solid particles or liquid droplets in the air. The EU air quality legislation covers PM

and the smaller parts PM

PM

covers particulate which are smaller than 10 microns or less, while PM

are smaller than 2.5 microns or less and both of them affect public health (WHO, 2006). The part of PM, directly associated with combustion, is mostly known as soot or BC in scientific terminology. BC aerosols are carbonaceous compounds which strongly absorb visible light; their evaporation temperature is close to 4000K; they are insoluble in water; are composed of tiny spherules with sizes of <10nm to approximately 50nm in diameter with graphite-like structure (Petzold et al., 2013). BC is a product of incomplete combustion processes commonly from biomass burning and the combustion of fossil fuels and therefore it is solely of primary sources and cannot be a secondary aerosol. It can be emitted directly into the atmosphere from naturally occurring events such as open burning in savannas and forest fires, or from anthropogenic sources such as agriculture burning, residential cooking and heating, vehicle emissions and emissions from industries. Minor contributions of BC come from aircraft and ship emissions (Bond et al., 2013).

BC, most often sourced from anthropogenic activities, is treated as the second most important agent, which has a direct and indirect effect on the climate and air quality(Bond et al., 2013). A total climate forcing is estimated to +1.1W/m

with 90%

uncertainty boundary from +0.17 to +2.1W/m

from which of those the direct forcing

is calculated as +0.71 W/m

with 90% uncertainty boundary of +0.08, +1.27 W/m

(Bond et al., 2013). The sources of BC emit also other emissions with either warm or

cool impact on the Earth’s surface. So, the specific source has specific uncertainties

(Bond et al., 2013).

Contrary to the long-lived greenhouse aerosols (such as CO

, NH

, N

O), the lifespan is orders of magnitude shorter and it is estimated about a week, depending on the amount of precipitation, which is a significant BC air removal mechanism (Cape et al., 2012).

Due to that feature, BC belongs to the short-lived climate pollutants (SLCP) together with CH

, O

and HFCs. SLCP aerosols reduce radiative forcing and affect air pollution. In the near future, reducing SLCP represents a challenge, which could lead to a substantial decrease in global climate change in a relatively short time (Carmichael et al., 2013;

Shindell et al., 2013).

BC is a good indicator of air quality in the area where the main pollutant originates from the combustion process. Depending on the length of the BC exposure, studies are divided into long-term and short-term. Premature mortality and hospitalization are consequences of short-term high exposure to BC, while long-term exposure frequently causes lung cancer, morbidity, cardiovascular and pulmonary diseases (Valavanidis et al., 2008;

WHO, 2012). Due to its size BC penetrates deep into the human respiratory system and crosses into the circulatory system, causing inflammation and oxidative damage (Janssen et al., 2011) or even neurotoxic disease when it reaches the brain. Some of the studies even correlate BC with other kinds of cancer, due to the fact that it could carry carcinogenic compounds (Terzano et al., 2010).

Measurements of PM

which were made close to a road reveal that 40–70% of all particles come from BC emissions. In the same study, a relationship was also indicated between the exposure to traffic-related air pollution and the exacerbation of asthma (Janssen et al., 2011). Moreover, a strong association was found between exposure to combustion emissions and systolic blood pressure (Baumgartner et al., 2014).

1.2 Aerosols Source Apportionment

Emissions of carbonaceous matter from different sources feature different optical

properties. Absorption can be used to separate contributions from different sources and

allows identification of the sources-specific BC. Emissions from diesel exhaust are BC

rich and intensive black and feature very poor dependence of absorption on the

wavelength, while combustion of biomass is a greater source of aromatic and other organic compounds with increased absorption in the ultraviolet, blue and visible parts of the light spectrum. This difference at short wavelengths can be used to perform source apportionment of BC (Andreae and Gelencsér, 2006; Kirchstetter et al., 2004; Liu et al., 2015).

The dependence of optical absorption on the wavelength can be characterized by the Ångström exponent (α) (Ångström, 1929). The absorption coefficient (b

) is inversely proportional to the wavelength (λ), as it is represented in Equation 1 (Sandradewi et al., 2008a).

𝑏

∝ 𝜆

(1)

In a study made by Kirchstetter et al. (2004) they investigated the difference between the Ångström exponents for diesel soot and for wood burning soot. They summarized that wood burning soot features an α around 2 while completely black aerosols, such as from diesel fuel combustion, features an α close to 1.

Sandradewi (2008a) and co-workers described the source apportionment model using a multi-wavelength aethalometer (model AE31). Study was located in an Alpine village with 2.200 inhabitants, where 77% of the houses use wood burning for heating system.

They investigated the variability and diurnal cycles of the aerosol light absorption and the Ångström exponent (α) during winter and summer periods. They report highest α value during winter due to the influence of wood burning throughout the day.

1.3 Planetary Boundary Layer

Almost all emissions occur at the lowest part of the atmosphere (troposphere) or in the

Planetary Boundary Layer (PBL), where consequently most of the measurements of

pollution are made (Ogrin et al., 2016). This is the lowest part of the atmosphere and due

to contact with the surface, it responds to changes in surface radiative forcing and strong

vertical mixing. Their height varies strongly from 100m in very stable conditions to more

than 3km while there is strong convection in the air. The structure of the boundary layer alters during the day and it is very closely related with BC concentration in the atmosphere. When PBL height is lower, the concentrations of aerosols in the atmosphere are higher (Quan et al., 2013; Cohen et al., 2015).

1.4 Cloud Condensation Nuclei (CCN)

The IPCC document Summary for Policymakers claims that BC contributes to the changes in surface albedo, mostly because of the changes and deposition of BC aerosols on the surface, especially on the snow (IPPC, 2013). This is a direct effect, when the Earth’s surface absorbs more heat from the sun compared to the situation under usual circumstances. Besides a warming effect, BC also has a cooling effect, when BC aerosols scatter solar radiation and less warmth reaches the Earth’s surface (Kondo, 2015).

Furthermore, BC also has an indirect effect on the Earth’s climate changes (Bond et al., 2013). Aerosols act as cloud condensation nuclei (CCN) activating cloud or fog droplets.

Under the particulate conditions BC aerosols can become CCN. Activation of CCN is

mostly dependant on the particle size and its chemical composition. A big part of CCN

activation is played by the atmospheric water vapour pressure below which the aerosols

stay in a stable condition or above which it becomes a CCN activator (Seinfeld and

Pandis, 2006). BC is a very inert aerosol, but through the process of coating by sulphate,

nitrate and organic matter, it can reach the proper size and hydrophilic property to act as

a CCN activator (Farmer et al., 2015). Until now, there is still little information on fresh

non-coated emitted BC and its activation of CCN. The process of activating BC aerosols

that plays the role of CCN is depicted in Figure 1, where the size of aerosols in particular

stages of the process is also represented. The average lifespan of a CCN is about a week

and during that time CCN will experience 5–10 cloud activation or cloud evaporation

cycles before being removed from the atmosphere through precipitation (Seinfeld and

Pandis, 2006).

Figure 1:Schematic pictures of aging of the BC aerosols and acting as CCN. Adapted from Kondo (2015) with author permission.

1.5 Fog Deposition of Black Carbon

Fog plays an important role in cleaning carbonaceous aerosols from the atmosphere. Some studies reveal that, on average, the scavenging efficiency of BC ranges from 6% to 39%

or even 50% (Gilardoni et al., 2014; Heintzenberg et al., 2016). Even though precipitation is more efficient, fog plays an important role in areas where have a long and dense fog, especially during the winter. In a study conducted by Gilardoni et al. (2014) it was concluded that BC aerosols are scavenged with the help of mixing with water-soluble aerosols. This fact was proved in study made by Kaul and others (2011), where it was found that in fog the biomass burning and secondary aerosols production are the most often compounds.

The overall scavenging efficiency is determined by aerosol size and its composition. The

oxidation process make aerosols more hygroscopic, while condensation leads to the

growth of their size, and coagulation connects both of these properties together (Herckes

et al., 2013). An important property that should not be neglected in the research of fog

deposition is the lifespan of aerosols. More polluted areas with more freshly emitted BC

aerosols are more likely to experience a smaller scavenging efficiency than areas with

more aged soot compounds (Herckes et al., 2013).

During a fog, absorbing aerosols are in the increase, which leads to the decrease of incoming solar radiation and furthermore cause disruption in the radiation budget over the surface. The α-exponent for biomass burning and vehicle exhaust aerosols during fog remain the same as it is on a clear day (Das, 2015).

1.6 Aim of the Study

Local air quality studies are important for estimating the exposure of the public to particulate air pollution. The concentrations of particulate matter depend both on the pollution sources and dilution governed by local weather. Local conditions, such as wind directions, wind speed, temperature and relative humidity can strongly affect BC deposition at a specific point in space and have to be taken into account in any kind of study which covers the topic of air pollutions. In recent decades fog and cloud composition and their activators have been studied intensively. BC emissions, which are emitted from different sources, have different initial properties, which could be crucial for the fog process. Biomass burning emissions (BCbb) are a greater source of aromatic and other organic compounds and have increased absorption in the ultraviolet, blue and visible parts of the light spectrum, while diesels exhaust (BCff) emissions are more rich and intensive black.

A lot of effective effort has been made to decrease emissions from vehicles such as reorganization of the public transport and restricted transport in the city center (Titos et al., 2015) but only few of the restrictions are connected with the BC emission from the small woods stoves. Additionally, wood combustions is also tracer of harmful gases such as carcinogenic 1,3-butadiene and secondary non-fossil aerosols (Gaeggeler et al., 2008). We have to be especially aware of winter time, when emissions of BC from biomass burning are on the peak.

Based on previous research this study tries to find an answer to these two questions:

 Is there any relation between the duration of fog and BC concentration?

 Does fog have a different impact on BCbb and BCff?

2. Methods

2.1 Study area

Ljubljana basin has specific location due to the topography and weather conditions. It is surrounded by hills and on the north side by steep mountains which are part of the Alpine range. Weak wind and constantly temperature inversion are frequently occurring during winter, which has impact on the regularly exceed limit values of PM

in EU Member States. A temperature inversion is a phenomenon, when the ground temperature is colder than the temperature of air mass above. In those conditions warmer emissions are not able to be transported away (Jacob, 1999). The sampling location of BC concentrations was placed in Ljubljana basin (Figure 2a) at the address of Na lazih 30, SI-1351 Brezovica pri Ljubljani with the coordinates 46°1.137’ N, 14°22.781’ E and at an altitude of 298m (Figure 2b). It is placed in settlement Laze, 0.5km away from the dual carriageway E70.

In urban environment the main sources come from the burning of biomass for domestic

heating and diesel vehicles. The measurement site was selected on the criteria for the air

quality monitoring as proposed by the European Environment Agency and it is treated as

a suburban zone (EEA, 1999). Measurements of BC concentration were made with

instrument Rack Mount Aethalometer® Model AE33 (Magee Scientific). The model has

been described in detail by Drinovec et al. (2015). Meteorological parameters: wind

speed, direction, RH, temperature were measured at the same location. These

measurements were compared to the meteorological measurements at ARSO (Slovenian

Environment Agency), which is located 10km away.

Figure 2a: Micro location of the measurement site (yellow pin) in the Ljubljana basin.

Figure 2b: Macro location of the measurement site (yellow pin) in the Ljubljana basin.

2.2 Definition of fog situation

In previous studies, on the topic of fog and BC concentrations, different definitions of

conditions when fog occurred (Zhi et al., 2014; Das, 2015; Zhi et al., 2014; Safai et al.,

2008). In this analysis it was assumed that fog is defined as the time, when the average

relative humidity (RH) is more than 95% with little (less than 2mm) or no precipitation and the wind speed less than 2m/s. This categorization of a fog period was additionally verified by comparing the dew point temperature and measured ambient temperature.

According to A Dictionary of Geography (Mayhew, 2015) the dew point is “The temperature that a body must be chilled to for it to become saturated with respect to water, so that condensation can begin”. The approximation to get the dew point (T

) in unit °C is represented in Equation 2, given the air temperature (°C) and relative humidity (%) (Wanielista et al., 1997).

𝑇

= ((

100

)

) ∗ (112 + 0.9𝑇) + 0.1𝑇 − 112 (2)

2.3 Data analysis

Measurements were taken from 8 January to June 2015, but for this study we used only the data from 8–31 January and from March 2015 due to the different fog occur. The data was taken at 1-minute resolution; BC unit ng/m

and percentages of BC from biomass burning aerosols. The measurement data were processed using Microsoft Excel 2010 and the program IBM SPSS statistics 20.0 was used for the statistical analyses.

The concept of the data analyse of the concentrations in this study is present in Figure

3.

The concentrations were normalized with this equation: BC / BC

, where BC

present the

concentration of BC at 22:00. They were determined each day (average 7 hours night

situation starting at 22.00) separately. The 7 concentrations and different or the same

durations of fog were fitted with the linear regression. After that, the scavenging

coefficient value was determined, the standard error of this coefficient, significant value

and F value. In March, when fog is more common during the late evening and early

morning, an analysis of difference between the duration of fog and concentration of BC

in the atmosphere was made. In January due to the more stable weather conditions, an

analysis of the 7-hours duration of fog and concentration of BC in the atmosphere was

made. At least it was made linear regression, where dependent values represent the

scavenging efficient and the independent variables represented duration of fog.

Outliers or concentrations which are distant more than the other concentrations from the data set have an influence on the statistically significant values and also on the slope values. In some cases it is better for the final result that they are suspended from the data.

Figure 3: The concept of the data analyses method in this study.

2.4 Aethalometer

An aethalometer is an instrument for measurements of BC with very high time resolution.

The aethalometer is a well-known optical instrument and was used in various studies which investigate the impact of BC on climate and human health (Herich et al., 2011;

Gianini et al., 2013; Andreae and Gelencsér, 2006; Sandradewi et al., 2008a; Sandradewi et al., 2008b). The aethalometer is a real-time device for measuring light absorption of BC. It provides measurements at 7 different wavelengths (λ) ranging from 370nm to

For January data it was made a comparison of slope coefficients of common, biomass burning and fossil fuel black carbon concentration on different days

For March data it was made linera regression , where dependent value s are slope coefficient and independent values are durations of fog.

Comparison scavenging efficient between BCbb and BCff with attention on siginificant value.

Detremination of the scavenging coefficient; standard error; significant value, F-value.

BC concentrations from 22.00 until 5.00 (7-hours, night) were fitted with linear regression.

BC, BCbb and BCff values were normalized by this equation: BC/ BC₀ BC₀ present the concentration of BC at 22:00.

Selected months

January 7-hours fog occurance

March

different duration of fog occurance (from 1 hour to 7 hours)

950nm, which provides the characterization of BC absorption in the range from the ultraviolet to the infrared wavelengths (Drinovec et al., 2015).

3. Results

Meteorology has a strong impact on the dilution of emissions in the atmosphere and, due to that, it is important to represent meteorological conditions besides the main result. In Section 3.1 we present the temperature, RH and wind speed/direction for January and March 2015. The diurnal profiles of BC, BCbb and BCff emissions are presented in Section 3.2.

3.1 Meteorological conditions

Variations in temperature and RH and also stronger winds in March impact the evening

and morning occurrences of fog. In contrast, the constant, longer periods of fog are

consequences of slowly varying in temperature and RH values and weaker wind speed as

it is characterized in January 2015. In the sections below we present temperature, relative

humidity and wind speed/direction for the periods from 8–31 January and from 1–27

March.

Figure 4: The average hourly temperature in January and March: Air temperature (blue) and the dew point temperature (red) and relative humidity values in January and March 2015. The shadow areas

represent the obtained situations.

In Figure 4 we see the average hourly temperature and hourly average dew point in

January 2015. The average temperature was 3.5°C (+/- 4.0) with a minimum value of -

6.0°C and a maximum value of 12.7°C. In March the average temperature was 5.9°C (+/-

3.9) with a minimum value of -2°C and a maximum value of 16°C. When comparing the graphs it is clear that the greater variance in temperature is in March impacting on the evening and morning occurrence of fog. At the moment when the air temperature and dew point are very close to each other or they are equal, the fog occurred. The matching of those in January and March 2015 are presented in graph above (Figure 4).

Figure 4 also shows the RH values in January and March 2015. The average value in January was 90 % (+/- 8.7) and the minimum value was 56%. The average March value was 75 % (+/-19) and the minimum value was 35%. It is clear that the more stable values are in January, when consequently longer and stronger fog periods occur. Due to weather conditions with weaker wind in January fog can stay in troposphere more than a day.

Wind plays an important role in transport and dispersion of BC in the atmosphere. Figures 5 and 6 show the wind roses (unit: m/s) for January and March. In January the strongest wind blew from the west. In contrast, in March the strongest wind blew from east and south-east. Comparing the wind from March and January it is clear that the wind in March was strongest.

Figure 5: Wind rose in January (left) and March (rightt) 2015 (unit: m/s).

In Figure 6 we present the logarithmic fitting line of BC concentration and wind speed.

The high r value (r=0.82), present in Fig. 7, establishes that higher wind speed reduce BC

concentration. The March data set was used (n = 624; p<0.000). From the graph below it is clear that strong wind has an influence on the BC concentrations in the air.

Figure 6: Dependence of black carbon concentration on wind speed with logarithmic fitting line in March 2015.

3.2 Diurnal variation of BC

The diurnal profile of emissions is defined by the contribution from traffic (BCff) and also from biomass burning (BCbb) and furthermore influenced by the daily evolution of the planetary boundary layer and its stability. The diurnal profiles of BC emissions for all days in January and March 2015 are presented in Fig. 8 and Fig. 9. The graphs show the average monthly concentration by hour.

In January, the average concentration for BC was 3.13µg/m

(+/- 0.56), for BCbb was 1.61µg/m

(+/- 0.44) and for BCff was 1.52µg/m

(+/-0.26). The standard deviation for both sources was quite stable throughout the time period. A moderate late evening peak of BC concentration is observed, which starts around 17.00 until 22.00. After that there is a slight decrease of concentration. The morning peak of BC emissions is noticeable around 7.00.

In March 2015 the average concentration of BC was 2.45µg/m

(+/-0.78), for BCbb was

1.08µg/m

(+/-0.41) and for BCff was 1.37µg/m

(+/-0.43). In comparison with the diurnal profile of BC for January, a steeper increase of concentration is observed around 17.00 in the afternoon and sharper drop after 7.00 in the morning.

The average concentration of BC for March was a bit lower than it was in January. A closer look at the concentrations from biomass burning in March reveals that the average concentration was lower by a third than in January. From the both figures (Fig. 7 and Fig.

8) are clear that the contribution of BCbb and BCff are similar. In January was a little more BCbb (52%) than BCff aerosols (47%), when outdoor temperature are lower and people use wood for heating system. Contrary in March was a slightly more aerosols from BCff (56%) than from BCbb (44%).

Figure 73: Diurnal variation of BC, BCbb and BCff emissions in January 2015. The shaded areas framed with dotted line represent the standard deviation.

Figure 8: Diurnal variation of BC, BCbb and BCff emissions in March 2015. The shaded areas framed with dotted line represent the standard deviation.

3.3 Concentration of Black Carbon during different durations of fog

For the selected days in March, when fog occurred for 1 hour or more, a linear regression between BC, BCbb, BCff and durations of fog was made using the SPSS program for 7- hours night period (Table 1). Table 1 includes 6 columns, in first column is represent durations of fog (from 1-hour to 7-hours), then is followed by date and last three columns present the scavenging coefficient, standard error, p-value and F-value for BC, BCbb and BCff aerosols.

Two periods when fog occurred for two days in a row (2

–3

and 3

–4

; 10

–11

and

11

–12

of March) and one period when fog occurs for 4 days (16

–17

, 17

–18

; 18

–

19

and 19

–20

of March) were analysed. All calculated scavenging coefficient from

the relationships between BC and durations of fog for the selected periods in Table 1 are

slightly negative, except the scavenging coefficient for BCff on 18-19 March and for BC

and BCbb on 11-12 March which are slightly positive. The biggest scavenging coefficient

in BC was -0.094 during 1-hour fog period, in BCbb -0.092 during 7-hours fog period and

in BCff -0.105 during 3-hours fog period. The scavenging coefficient values which show

statistically significant (p<0.05) relationships are marked with *. Non-statistical

significant values (p>0.05) are 3 in the BCbb and 4 in the BCff columns.

Table 1: Comparison of scavenging coefficient, the standard error, p-value and F-value for relationship between BC, BCbb, BCff and durations of fog on selected dates in March with different durations of fog.

Statistically significant p-values are marked with *.

Duration of fog (hours)

date BC BCbb BCff

1.00 19.3-20.3

Scav. Coef. -0.058 -0.057 -0.058

std. error 0.013 0.017 0.023

p-value F-value

*0.004 19.876

*0.016 10.960

*0.045 6.414 3.00 10.3-11.3

Scav. Coef. -0.094 -0.086 -0.105

std. error 0.018 0.015 0.026

p-value F-value

*0.003 24.131

0.505 0.503

*0.001 44.426 3.00 18.3-19.3

Scav. Coef. -0.012 -0.026 0.008

std. error 0.013 0.013 0.017

p-value F-value

0.384 0.882

0.093 3.971

0.692 0.172 4.00 11.3-12.3

Scav. Coef. 0.002 0.014 -0.008

std. error 0.02 0.014 0.03

p-value F-value

0.901 25.823

0.362 31.898

0.792 16.799 4.00 23.3-24.3

Scav. Coef. -0.039 -0.061 -0.011

std. error 0.01 0.01 0.016

p-value F-value

*0.010 13.956

*0.001 37.667

0.506 0.501 5.00 17.3-18.3

Scav. Coef. -0.056 -0.081 -0.037

std. error 0.014 0.021 0.018

p-value F-value

*0.008 15.631

*0.008 15.092

*0.083 4.299 6.00 3.3-4.3

Scav. Coef. -0.070 -0.058 -0.087

std. error 0.008 0.016 0.014

p-value F-value

*0.000 69.566

*0.011 13.156

*0.001 1.503 7.00 2.3-3.3

Scav. Coef. -0.057 -0.082 -0.030

std. error 0.019 0.016 0.024

p-value F-value

*0.026 8.588

*0.002 25.661

0.266 1.503 7.00 16.3-17.3

Scav. Coef. -0.061 -0.068 -0.054

std. error 0.015 0.019 0.017

p-value F-value

*0.007 15.931

*0.012 12.591

*0.018 10.541 7.00 25.3-26.3

Scav. Coef. -0.074 -0.092 -0.065

std. error 0.025 0.017 0.029

p-value F-value

*0.026 8.657

*0.002 27.978

*0.070 4.844

Figure 9, Figure 10 and Figure 11 show slope coefficient for BC, BCbb and BCff as a

function of the duration of the fog period for all examined periods. For all 3 graphs the

fitting line is slightly negative, what is a consequence of fog deposition. The greater slope

is shown for the BCbb graph, where k value is -0.006 (R

=0.14). On the other hand, BCff

has k value -0.002 (R

=0.01) and consequently for common BC the k value is -0.003 (R

=0.06). Comparing the BCbb and BCff graphs, it can be concluded that the BCbb points are closer to fitting line than the BCff points.

Figure 9: Dependence of scavenging coefficient of black carbon concentration on different durations of fog with standard errors bars.

Figure 40: Dependence of scavenging coefficient of black carbon concentration from biomass burning on different durations of fog with standard errors bars.

Figure 11: Dependence of scavenging coefficient of black carbon concentration from fossil fuel on different durations of fog with standard errors bars.

3.4 Concentration of Black Carbon during constant durations of fog

For selected days in January, when the fog occurred a throughout consecutive 7-hour

period, linear regressions between BC, BCbb, BCff and durations of fog were made using

the SPSS program (Table 2). There are 2 longer periods of fog occurrences, from 19

to

22

and from 26

to 29

January. There is not a big difference between scavenging

coefficient values of BC, BCbb and BCff but BCff has 8 scavenging coefficients with

significant-values more than 0.05 and, contrary to that, BCbb has only 2 scavenging

coefficients. This result shows that BCbb scavenging coefficients vary stronger than BCff

(Figure 13). The highest scavenging coefficient for BCbb is -0.104, while for BCff is –

0.088, what is represented in Figure 13.

Table 2:Comparison of slope value, standard error, p-value and F-value for BC, BCbb and BCff on selected dates in January with constant durations of fog. Statistically significant p-values are marked with

*.

Duration of fog (hours)

date BC BCbb BCff

7.00 11.1-12.1

Scav. Coef. -0.088 -0.122 -0.039

std. error 0.024 0.04 0.023

p-value F-value

0.11 13.142

*0.024 9.099

0.135 2.983 7.00 14.1-15.1

Scav. Coef. -0.025 -0.070 0.035

std. error 0.013 0.015 0.031

p-value F-value

0.112 3.473

*0.003 21.817

0.295 1.315 7.00 19.1-20.1

Scav. Coef. -0.008 -0.021 0.012

std. error 0.01 0.011 0.019

p-value F-value

0.419 0.754

0.09 4.065

0.53 0.445 7.00 20.1-21.1

Scav. Coef. -0.075 -0.089 -0.057

std. error 0.02 0.013 0.038

p-value F-value

0.1 13.622

*0.000 48.546

0.186 2.224 7.00 21.1-22.1

Scav. Coef. -0.078 -0.067 -0.088

std. error 0.02 0.016 0.028

p-value F-value

*0.008 15.626

*0.005 18.611

*0.019 10.032 7.00 22.1-23.1

Scav. Coef. -0.061 -0.067 -0.057

std. error 0.024 0.02 0.032

p-value F-value

*0.045 6.336

*0.014 11.573

0.127 3.137 7.00 26.1-27.1

Scav. Coef. -0.028 0.001 -0.076

std. error 0.02 0.023 0.042

p-value F-value

0.215 1.924

0.956 0.003

0.12 3.281 7.00 27.1-28.1

Scav. Coef. -0.050 -0.041 -0.062

std. error 0.009 0.012 0.022

p-value F-value

*0.002 28.422

*0.015 11.494

*0.034 7.457 7.00 28.1-29.1

Scav. Coef. -0.086 -0.104 -0.036

std. error 0.023 0.028 0.028

p-value F-value

*0.009 14.238

*0.01 13.648

0.237 1.722 7.00 29.1-30.1

Scav. Coef. -0.015 -0.055 0.052

std. error 0.014 0.019 0.043

p-value F-value

0.32 1.173

*0.027 8.514

0.276 1.435

The figure below (Figure 12) shows the slope coefficient of BC, BCbb and BCff with

standard errors on different days in January. It is clear that BCff emissions have 3

positive coefficients, while BCbb has only 1 low positive value (0.001, on 26-27

January). BCbb slope values (k coefficient) are mostly all of the time lower than BCff

slope values (k values), except the days on 21-22, 26-27 and 27-28 January.

Figure 125: Slope coefficients between common, biomass burning and fossil fuel black carbon concentration and durations of fog on different days in January with standard errors bars.

4. Discussion

Fog plays an important role in cleaning carbonaceous aerosols from the atmosphere.

(Gilardoni et al., 2014; Heintzenberg et al., 2016). The scavenging efficiency is determined by aerosol size and its composition, which makes aerosols more hygroscopic through the process of condensation and coagulation (Herckes et al., 2013; Farmer et al., 2015; Kondo, 2015; Bond et al., 2013). Basis in the fact that BC from different sources have also different initial properties my study reveals that BC from wood burning have higher affinity to water than BC from vehicle exhaust.

For this research is besides the good definition of fog situation important also the location of the measurement instrument. Fog is defined as the time, when the average relative humidity (RH) is more than 95% with little (less than 2mm) or no precipitation and the wind speed less than 2m/s. Moreover this definition was verified by comparing air temperature and dew point.

The measurement site in this study has very almost the same contributions of BCbb and

BCff. This could be explained by the location of the measurement site, which is in a

settlement where households use wood for their heating systems and it is furthermore

located 0.5km away from the dual carriageway to the north (Figure 2a in 2b). The equal

contribution from both sources is an important for analysing the main results in this study and is shown in the diurnal variation of BC, which is present in section 3.2. The diurnal profiles of concentrations for March and January vary a lot. It is observed the morning peak of BC emissions around 7.00 in January and could be caused by the morning rush hour. One of the reasons for the decline in concentration of BC during the day is, in addition to the smaller contribution from sources, also the dilution in the increasing mixing height of the planetary boundary layer (Dall'Osto et al., 2013; Sahu et al., 2011).

While the steeper drop and increase of BC concentrations in March could be explained by the warmer temperature and greater number of sunny days in March and by people not using domestic heating during the day.

The in-situ measurements in this study shows the slightly difference in the slopes of scavenging coefficients as a function of the fog period duration which is shown in the Fig.

10 and among the different sources of BC (Fig. 11, Fig. 12). The scavenging efficient line for the most situations is slightly negative. Furthermore, BCbb has a greater scavenging efficient of the fitting line (k = -0.6%; R

= 0.14) than BCff (k = -0.2 %; R

= 0.01). Even it was expect that BCff would have less statistically significant scavenging efficient, it was shown that there is no particular differences between numbers of significant values;

BCbb have 3 and BCff have 4. From this analyses it could be concluded that different source specific BC behave differently in the fog. Until the durations of fog time is less than 7-hours we could not prove any differences. One of the limitations in this section is that there is only one period when fog occurs for 1-hour, 5-hour and 6-hour. Additionally, the in-situ measurements could not provide the particular age of the aerosols and were limited to the calm, only 7-hour, night periods. The aging process, when BC aerosols become hydrophilic is assumed to last for about one day (Kondo, 2015 and references therein). To get more accurate results in the future it is suggested to repeat this study under controlled laboratory conditions, where you can regulate RH and temperature values to get the same conditions as they are in the fog. In laboratory you should also control the coating process (Dusek et al., 2006). A more accurate result would also be provided by a bigger dataset of measurements with longer durations.

Analysis of the measurements from fog periods of longer duration shows a different

impact of fog on BCbb and BCff. The 7-hour-long fog period research reveals that BC

from biomass burning features a larger absolute value of the scavenging coefficients than BCff, which has 8 out of 10 non-statistically significant slopes of scavenging coefficients.

From the Table 2 and Figure 13 we can clearly conclude that BC emissions from diesel exhaust behave differently in longer fog periods than BC emissions from biomass burning and thus the relationship becomes stronger for BCbb. Furthermore, this result indicates that fog has different impact on these emissions due to the different features of black carbon and brown carbon (Andreae and Gelencsér, 2006). Fresh BCff are especially smaller than BCbb. BCbb is larger and, due to the condensation process more quickly coated with other water-soluble compounds. Under this conditions BC aerosols can act as CCN activating cloud or fog droplets. Gong and co-workers (2016) reports that BCff has a BC core with a 60–80nm diameter with a thin absolute coating thickness (ACT) (50- 130nm), in comparison with the highly aged traffic emissions aerosols, which have ACT 130–300nm in diameter. They found out that fresh BCbb has a diameter of 80–130 nm with ACT 110–130nm. According to this study, the main reason that aerosols reach the droplet mode that they are highly aged traffic aerosols and biomass burning aerosols. The same results have been revealed in the study conducted by Collett (2008) with the study on source type markers, where they found out different scavenging efficiency in fog on a carbonaceous aerosols from different source types. The greater effect was made on aerosols from wood smoke than on vehicle exhaust, suggesting that the atmospheric lifespan of BCbb is smaller, due to the different properties of aerosols. Fog has a bigger effect on wood burning aerosols or has a bigger scavenging effect which leads to a smaller lifespan of these aerosols.

My study added insight of fog deposition of fresh BC from to different sources of BC

aerosols. The most crucial and sensitive part of my study is the definition of a fog period,

which was verified by comparing the dew point and air temperature. The benefit of my

study is also the good position of the measurement station, which provides an equal

contribution of wood burning and vehicle emissions. On the other hand, the in-situ

measurements barely control the aging of aerosols and contribution between fresh and

aging soot aerosols. In my case, it was assumed that the amount of sources was the same

throughout the entire observation time. In the future, it is suggested to take into account a

larger data set, which could reveal the pattern of fog impact on BCbb and BCff. Moreover,

comparing foggy days and days with clear sky will provide new information about the

contribution of fog deposition to total reduction of BC concentrations (Kaul et al., 2011).

Overall, more field work is needed to confirm the general knowledge about the

composition and chemistry of fogs and cloud formation around the world and its wet

deposition of aerosols.

5. Conclusion

The in-situ measurements in this study reveal that the different source apportionment of BC emissions with different initial properties of BC behaves differently during the fog periods. Foggy periods were collected from the March data set and compared to BC emissions from biomass burning and fossil fuels for 7-hour night periods. For each foggy period linear regressions between BC, BCbb, BCff and durations of fog were made and at least all slope values were compared between each other. The results revealed that BC emissions from biomass burning have fewer non-statistically significant values than the BC emissions from vehicle exhaust.

These prove the fact that aerosols from different sources have different properties, which behave differently in foggy situations. So, fog deposition is determined by aerosol size and its composition. Condensation leads to the growth of the aerosol’s size and coagulations affect the different compositions of the aerosols and both of these processes lead to hygroscopicity. Overall, the main reasons that aerosols become droplets are the highly-aged traffic particles and biomass burning particles.

The results from Section 3.3, when BC emissions were studied with different durations of fog show slightly difference in the scavenging coefficients slope as a function of the fog period duration. Repeated research under controlled conditions in a laboratory or using a bigger data set and with larger observed durations will give us a better pattern. This assumption is additionally proved in section 3.4, where it was analysed 7-hours fog situation and BC emissions.

According to this study, biomass burning emissions have a greater affinity for water than

vehicle exhaust aerosols due to the initial properties of the emitted aerosols. So, in the

future, our focus should not be only on vehicle exhaust emissions but we must dedicate

more attention to the mitigation of wood burning emissions.

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