From figure 7 it was observed that the closing of doors significantly reduced the carbon dioxide concentration decay rate implying a reduction in the ventilation rates achieved when both doors and windows were open. Opening windows and closing doors reduced the mass flow rate to 3.16 kg/s (i.e. a reduction of 17%). This means doors play a significant

Figure 8 illustrates the decay of the tracer gas concentration for configurations III and IV, i.e., for open doors and closed windows, and for when both windows and doors were closed. Comparing configurations I and III it was found that the opening of doors and closing windows reduced the mass flow rate from 3.83 kg/s to 0.67 kg/s (i.e. a reduction of 82%). Configuration IV produced the minimum tracer gas concentration decay rate. It took approximately 69 minutes for the tracer gas to decay to the background concentration. When both doors and windows were closed, the infiltration and exfiltration airflow was through the unintended gaps, such as, gaps between floor and door, roof and wall, etc.

Fig. 8. Tracer gas concentration decay for configuration III and IV [Makaka, Meyer; 2008].

With reference to figures 7 and 8 and taking a reference initial tracer gas concentration of 0.28% the time taken for the tracer gas concentration to decay to the background concentration varied depending on the type of the ventilation components in use. Table 2 summarizes the time taken for the carbon dioxide concentration to decay from 0.28% to the background concentration. From Table 2 it can be seen that windows have a higher ventilation effect (shorter decay time) than doors. However this depends on the wind speed and direction and the orientation of the ventilation component with respect to the wind

Somerset East experience W(600 150)N prevailing winds, and when windows are open and doors closed, the west side windows capture the prevailing winds which then escape through the east and south windows, and to a lesser extent through the north side windows. This gives an effective controllable air inflow and outflow by adjusting the opening area of

role in the ventilation of the PSH.

direction.

windows.

Several decay periods can be observed from figures 12 through 13. For each period, the initial and final times were determined and a normalized concentration, *Cn*, was calculated for each measurement time:

$$C\_n = \frac{C(t) - C(0)}{C(0) - C\_0} \tag{4}$$

where C(0) is the initial concentration at the beginning of the decay period and *C*0 is the background concentration and was found to be 0.234%. This background concentration was first deducted from the carbon dioxide concentration to get the increase resulting from the instant of injection. Figures 9 and 10 show the graphs of Ln(Cn) versus time for different ventilation component configurations. The air change rate, which is the slope of the line that represents Ln(Cn) versus time was calculated for each graph.

Fig. 9. Logarithmic graph of concentration: configuration I and II [Makaka, Meyer; 2008].

Fig. 10. Logarithmic graph of concentration: configuration III and IV [Makaka, Meyer; 2008].

Table 3 shows the calculated specific airflow exchange rates ( ) for different ventilation component configurations. In this table, the confidence intervals were calculated from the dispersion of the concentration measurements around the regression line, using 0.1% probability (99.9% confidence). When all the designed ventilation components were closed (configuration IV) the least specific air exchange rate ( ) of (0.29 0.03) h-1 was observed as compared to other ventilation component configurations. The equivalent outdoor airflow rates ( *Meq* ) for each ventilation component configurations were calculated using the expression: *Meq V* , where *V* is the volume of the house. The house has a volume of 34.56 m3 and an envelope area of 61.1 m2. Assuming that the flows are due to the envelope leakage the specific leakage rate ( *SL* ) was obtained using the expression: *eq L M S A* where A is the area of the envelope and results are summarized in table 3

Table 3. Specific airflow rates calculated from the various CO2 concentration decays [Makaka, Meyer,; 2008]

The ventilation rate determines the indoor environment as the incoming air carries with it thermal energy. If the outdoor temperature is higher than the indoor, and as the outdoor air flows indoors, the tendency is to raise the indoor temperature. The rate of ventilation heat flow is approximated by equation. 1200 *Q MT v eq* . Taking the average indoor-outdoor temperature difference *T* to be 7°C and the ventilation rates from Table 3, the ventilation heat flow rates were calculated. Table 3 also shows the summary of results for the rate of ventilation heat flows for different ventilation component configurations. Depending on the indoor and outdoor temperature difference, configuration I, which has the highest rate of ventilation heat flow, can result in excess heat gains or losses. However, adjusting the effective open areas of the ventilation components can regulate the heat gain/loss, thus keeping the indoor environment within the comfort levels.

It must be noted that the rate of ventilation heat flow for configuration IV is through the unintended ventilation path ways, i.e. through gaps between doors and floors, etc, since all designed ventilation components were closed.

#### **8. Indoor temperature variation**

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Fig. 10. Logarithmic graph of concentration: configuration III and IV [Makaka, Meyer; 2008].

component configurations. In this table, the confidence intervals were calculated from the dispersion of the concentration measurements around the regression line, using 0.1% probability (99.9% confidence). When all the designed ventilation components were closed

as compared to other ventilation component configurations. The equivalent outdoor airflow rates ( *Meq* ) for each ventilation component configurations were calculated using the

m3 and an envelope area of 61.1 m2. Assuming that the flows are due to the envelope

leakage the specific leakage rate ( *SL* ) was obtained using the expression: *eq*

Table 3. Specific airflow rates calculated from the various CO2 concentration decays

Specific air exchange

*V* , where *V* is the volume of the house. The house has a volume of 34.56

Equivalent outdoor air flow rate *Meq* (m3/h)

9.58 0.04 331.08 1.38 5.42 772

1.74 0.02 60.13 0.69 0.98 140

0.84 0.04 29.03 1.38 0.48 68

0.29 0.03 10.02 1.04 0.16 24

) for different ventilation

) of (0.29 0.03) h-1 was observed

*L*

*S*

Specific leakage rate *SL* (m3/h.m2)

*M*

*A* where A

Rate of ventilation heat flow *Qv* (J/s)

Table 3 shows the calculated specific airflow exchange rates (

is the area of the envelope and results are summarized in table 3

rate (h-1)

(configuration IV) the least specific air exchange rate (

component status

windows open

and windows open

windows closed

windows closed

Configuration Ventilation

I Doors and

II Doors closed

III Doors open and

IV Doors and

[Makaka, Meyer,; 2008]

expression: *Meq*

#### **8.1 Prediction of indoor temperature**

The mathematical description of thermal behaviour of building systems is a complex process. It involves the modelling of several interconnected subsystems, each containing long-time constants, non-linearities and uncertainties such as convection coefficients, material properties, etc. The indoor temperature is affected by a number of stochastic parameters, which include, wind speed and direction, relative humidity, solar radiation and outdoor temperature. The random infiltration rate and thermophysical properties (such as thermal conductivity of the walls and heat capacity of the room) have an impact on the indoor temperature.

The statistical approach allows estimating the probabilistic future thermal behaviour of buildings based on monitored statistical information, such as outdoor temperature, relative humidity, etc. The stochastic behaviour of the occupants in operating the ventilation components and other indoor human activities greatly affects the thermal performance of buildings. Some can even choose to close doors to avoid pets to get indoor thus compromising the thermal performance of the house. These parameters sometimes are uncertain and in some cases are difficult to find the exact information.

#### **8.2 Correlating Tin and Tout**

Figure 11 shows the best-fit linear regression correlations of the indoor and outdoor temperatures for summer. It was seen that the indoor and outdoor temperatures had different correlations for low temperatures (less than 28°C), and for high temperatures (greater than 28°C). At low temperatures the correlation factor was found to be 2 *R* 0.8064 while for high temperatures the correlation factor was 2 *R* 0.6028 . At low temperatures the correlation was found to be stronger implying that at low temperatures there is less dependence on other parameters such as relative humidity as compared to high temperatures. At high temperatures relative humidity was found to be very low (minimum), while the solar radiation was maximum. This suggests that relative humidity and solar radiation have significant influence on the indoor temperature at high outdoor temperatures. The peak indoor temperatures were found to occur during cooking periods and thus creating a departure from the low temperature variation trend. A departure of about 57% was observed from the low temperature variation trend to high temperature variation trend and this departure is indicated in figure 11 by a double arrow. This departure can be due to indoor heat sources, such as heat from stove. At low temperatures the correlation between the indoor and outdoor temperature was found to be:

$$T\_{in} = 0.691 T\_{out} + 11.67\tag{5}$$

while at high temperatures it was found to be:

$$T\_{in} = 1.088 T\_{out} + 0.308\tag{6}$$

Fig. 11. Summer: Correlation of indoor and outdoor temperature [Makaka, Meyer; 2011].

Figure 12 shows the best-fit correlation of the indoor to the outdoor temperature for winter period. At high outdoor temperatures (above 28°C) the correlation factor was found to be 2 *R* 0.4568 while at low temperatures (less than 28°C) it was 2 *R* 0.7391 . At low temperatures the correlation is relatively stronger than at high temperatures implying that the outdoor temperature has a greater influence on the indoor temperature at low temperatures. The departure from the low temperature variations trend was found to be about 62% and is indicated in figure 19 by a double arrow. This departure may be attributed to indoor heating and activities which are independent to the outdoor temperature.

At low temperatures the correlation function between the indoor and the outdoor temperature was found to be:

$$T\_{in} = 0.657 T\_{out} + 8.671\tag{7}$$

while at high temperatures it was found to be:

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while the solar radiation was maximum. This suggests that relative humidity and solar radiation have significant influence on the indoor temperature at high outdoor temperatures. The peak indoor temperatures were found to occur during cooking periods and thus creating a departure from the low temperature variation trend. A departure of about 57% was observed from the low temperature variation trend to high temperature variation trend and this departure is indicated in figure 11 by a double arrow. This departure can be due to indoor heat sources, such as heat from stove. At low temperatures

0.691 11.67 *T T in out* (5)

1.088 0.308 *T T in out* (6)

the correlation between the indoor and outdoor temperature was found to be:

Fig. 11. Summer: Correlation of indoor and outdoor temperature [Makaka, Meyer; 2011].

to indoor heating and activities which are independent to the outdoor temperature.

Figure 12 shows the best-fit correlation of the indoor to the outdoor temperature for winter period. At high outdoor temperatures (above 28°C) the correlation factor was found to be 2 *R* 0.4568 while at low temperatures (less than 28°C) it was 2 *R* 0.7391 . At low temperatures the correlation is relatively stronger than at high temperatures implying that the outdoor temperature has a greater influence on the indoor temperature at low temperatures. The departure from the low temperature variations trend was found to be about 62% and is indicated in figure 19 by a double arrow. This departure may be attributed

At low temperatures the correlation function between the indoor and the outdoor

 *Tin* = 0.657*Tout* + 8.671 (7)

while at high temperatures it was found to be:

temperature was found to be:

$$T\_{in} = 1.052T\_{out} + 1.449 \tag{8}$$

From the correlation functions for summer and winter it was observed that the outdoor temperatures have significant influence on the indoor temperature. It must also be noted that, if a change occurs in the comfort parameters (e.g., temperature) that might result in discomfort, occupants react in a way that tend to restore their comfort, and there is no precise temperature at which one opens a window, but as the temperature rises there is an increase in probability that windows need to be opened. In figure 12, a significant number of data points appear as outliers. Although the occupants never used a proper heater, it is highly likely that at times they used a two-plate electrical stove to heat the indoor environment, thus giving rise to outliers. It must be noted that the outliers are above the trend line, confirming that at times a heating system was used. However the occupants never reported the use of a heater or fan.

Fig. 12. Winter: Correlation of indoor and outdoor temperature [Makaka, Meyer; 2011].

The wide dispersion of the points about the trend line also indicates that the indoor temperature is not affected by the outdoor temperature only but by other factors such as ventilation rate, relative humidity, wind speed and solar radiation. The correlations also suggest that the passive solar house is freeze resistance since when the outdoor temperature is 0ºC, the indoor temperature would be about 8°C in winter and much higher (about 12°C) in summer. From the measured data it was observed that on the 29/5/29 at around 06h30 the outdoor temperature dropped to about 1.6°C and the indoor temperature only dropped to 9.4°C. This indicates the high heat retention capacity of the thermal mass (fly ash bricks), such that when the outdoor temperature drops to zero, the stored thermal energy in the wall continue to heat the indoor air keeping it at a temperature above 8°C.

#### **8.3 Correlating Tin and outdoor relative humidity**

Figure 13 shows the correlation of indoor temperature to outdoor relative humidity for summer. The data points were found to follow two distinct distribution trends, i.e., at low (less than 34%) and high (greater than 34%) relative humidity. The correlations factors were found to be very weak, for low relative humidity the correlation factor was found to be, 2 *R* 0.2774 while for high relative humidity it was found to be 2 *R* 0.3099 . The differences in correlation strength at low and high relative humidity suggest that there are other variables which dominate at low relative humidity and not at high relative humidity. At low relative humidity solar radiation was found to have a greater influence on the indoor relative humidity variation. The low correlation factors indicate that there are other parameters which have a higher impact on the indoor temperature than the outdoor relative humidity. At low outdoor relative humidity (less than 34%) the correlation was found to be:

$$T\_{in} = -0.3395(RH)\_{out} + 40.464$$

while at high relative humidity the correlation was:

$$T\_{in} = -0.1184 \text{(RH)}\_{out} + 33.389 \tag{10}$$

Figure 14 shows the correlation of the indoor temperature and outdoor relative humidity for the winter period. The data points were found to be more scattered than for the summer. The high scatter is due to the variability of the Somerset East weather, which can change two to three times a day and a change of indoor activities due to the change of season. The dependence of the indoor temperature with the outdoor relative humidity was found to be different for low and high outdoor relative humidity. For low relative humidity the correlation factor was found to be 2 *R* 0.2657 while for high relative humidity the correlation factor was 2 *R* 0.4027 . Low relative humidity was found to correspond to high temperatures, which is directly related to the solar radiation. High solar radiation was found to correspond to low relative humidity. At low relative humidity the correlation function was found to be:

$$T\_{in} = -0.6862 \text{(RH)}\_{out} + 36.65$$

while at high relative humidity the correlation function was found to be:

$$T\_{in} = -0.1004(RH)\_{out} + 22.70\tag{12}$$

#### **8.4 Effect of solar radiation on indoor temperature**

Figure 15 shows the correlation of the indoor temperature and solar radiation. The correlation factor was found to be 2 *R* 0.5873 . It must be noted that the indoor temperature is affected by a number of factors, which include heat generated by the occupants and from equipments such as stove and refrigerator.

Figure 13 shows the correlation of indoor temperature to outdoor relative humidity for summer. The data points were found to follow two distinct distribution trends, i.e., at low (less than 34%) and high (greater than 34%) relative humidity. The correlations factors were found to be very weak, for low relative humidity the correlation factor was found to be, 2 *R* 0.2774 while for high relative humidity it was found to be 2 *R* 0.3099 . The differences in correlation strength at low and high relative humidity suggest that there are other variables which dominate at low relative humidity and not at high relative humidity. At low relative humidity solar radiation was found to have a greater influence on the indoor relative humidity variation. The low correlation factors indicate that there are other parameters which have a higher impact on the indoor temperature than the outdoor relative humidity. At low outdoor relative humidity (less than 34%) the

Figure 14 shows the correlation of the indoor temperature and outdoor relative humidity for the winter period. The data points were found to be more scattered than for the summer. The high scatter is due to the variability of the Somerset East weather, which can change two to three times a day and a change of indoor activities due to the change of season. The dependence of the indoor temperature with the outdoor relative humidity was found to be different for low and high outdoor relative humidity. For low relative humidity the correlation factor was found to be 2 *R* 0.2657 while for high relative humidity the correlation factor was 2 *R* 0.4027 . Low relative humidity was found to correspond to high temperatures, which is directly related to the solar radiation. High solar radiation was found to correspond to low relative humidity. At low relative humidity the correlation function

Figure 15 shows the correlation of the indoor temperature and solar radiation. The correlation factor was found to be 2 *R* 0.5873 . It must be noted that the indoor temperature is affected by a number of factors, which include heat generated by the occupants and from

while at high relative humidity the correlation function was found to be:

**8.4 Effect of solar radiation on indoor temperature**

equipments such as stove and refrigerator.

0.3395( ) 40.464 *Tin RH out* (9)

0.1184( ) 33.389 *Tin RH out* (10)

0.6862( ) 36.65 *Tin RH out* (11)

0.1004( ) 22.70 *Tin RH out* (12)

**8.3 Correlating Tin and outdoor relative humidity**

while at high relative humidity the correlation was:

correlation was found to be:

was found to be:

Fig. 13. Summer: Correlation of indoor temperature and outdoor relative humidity

Fig. 14. Winter: Correlation of indoor temperature and outdoor relative humidity.

Fig. 15. Evolution of indoor temperature with solar radiation [Makaka, Meyer; 2011].

The indoor and outdoor temperature difference gives the direction of heat flow. High solar radiation was found to be associated with low indoor and outdoor temperature differences. Maximum indoor temperatures were found to occur at around 14h00. In summer the roof was found to attain high temperatures and the roof being of iron sheets (good thermal conductor) contributed significantly to the indoor temperature distribution. The high roof temperature diminishes the indoor and outdoor temperature difference. The correlation function between indoor temperature and solar radiation, *I*, was found to be:

$$T\_{in} = 0.0075I + 21.27\tag{13}$$

Equation (13) shows that when the solar radiation is zero (at night), the indoor temperature would be within the thermal comfort levels (about 21ºC). During the day the thermal mass would have absorbed solar radiation and releases it at night thus keeping the indoor environment within the comfort levels.

#### **8.5 Temperature modeling**

The generalized mathematical model for predicting indoor temperature of any building has to take into account two types of data: on one hand the climatic conditions to which the building is exposed and, on the other hand, the thermal properties of the building. In an unoccupied house one can have a complete control over the conditions of the house, whether to open or close; shade or unshade the windows, etc. During the experimental period, the ventilation components can be maintained at the same configuration without indoor heat generation or cooling.

In occupied buildings the situation is very different, as the occupants have complete freedom to change the conditions according to their changing needs or desires. The activities of the occupants have a significant influence on the indoor temperature distribution. Predicting the indoor temperature is a very difficult and complex process, as random variables come into play, such as the closing and opening of the ventilation components. Outdoor weather variables such as wind speed, temperature, solar radiation and relative humidity are key determinant factors of the indoor temperature. However it must be noted that the indoor temperature is also linked to the heat fluxes generated in the house by appliances.

The performance of the house was observed to depend on how the occupants operate the house, and the thermal behaviour of the house in winter was seen to be different from that in summer. Based on the monitored results, the first stage in the development of the experimental predictive model was to analyze the patterns between (i) *Tin* and *Tout* (ii) *Tin* and *I* (iii) *Tin* and *RHout* (iv) *Tin* and *Vw* . Taking into account these possible dependences predictive models for the indoor temperature for summer and winter were developed. A linear dependence was proposed as the above relation patterns were observed to be linear.

$$T\_{in} = a\_1 \, \text{"} \, T\_{out} + a\_2 \, \text{"} \, RH\_{out} + a\_3 \, \text{"} \, I + a\_4 \, \text{"} \, V\_w + a\_5 \tag{14}$$

Where:

*T*in *=* Indoor temperature *T*out = Outdoor temperature *Vw* = Wind speed *I* = Solar radiation *RHout* = Outdoor relative humidity *i a* = Regression coefficient

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The indoor and outdoor temperature difference gives the direction of heat flow. High solar radiation was found to be associated with low indoor and outdoor temperature differences. Maximum indoor temperatures were found to occur at around 14h00. In summer the roof was found to attain high temperatures and the roof being of iron sheets (good thermal conductor) contributed significantly to the indoor temperature distribution. The high roof temperature diminishes the indoor and outdoor temperature difference. The correlation function between indoor temperature and solar radiation, *I*, was found

Equation (13) shows that when the solar radiation is zero (at night), the indoor temperature would be within the thermal comfort levels (about 21ºC). During the day the thermal mass would have absorbed solar radiation and releases it at night thus keeping the indoor

The generalized mathematical model for predicting indoor temperature of any building has to take into account two types of data: on one hand the climatic conditions to which the building is exposed and, on the other hand, the thermal properties of the building. In an unoccupied house one can have a complete control over the conditions of the house, whether to open or close; shade or unshade the windows, etc. During the experimental period, the ventilation components can be maintained at the same configuration without

In occupied buildings the situation is very different, as the occupants have complete freedom to change the conditions according to their changing needs or desires. The activities of the occupants have a significant influence on the indoor temperature distribution. Predicting the indoor temperature is a very difficult and complex process, as random variables come into play, such as the closing and opening of the ventilation components. Outdoor weather variables such as wind speed, temperature, solar radiation and relative humidity are key determinant factors of the indoor temperature. However it must be noted that the indoor temperature is also linked to the heat fluxes generated in the house by

The performance of the house was observed to depend on how the occupants operate the house, and the thermal behaviour of the house in winter was seen to be different from that in summer. Based on the monitored results, the first stage in the development of the experimental predictive model was to analyze the patterns between (i) *Tin* and *Tout* (ii) *Tin* and *I* (iii) *Tin* and *RHout* (iv) *Tin* and *Vw* . Taking into account these possible dependences predictive models for the indoor temperature for summer and winter were developed. A linear dependence was proposed as the above relation patterns were observed to be linear.

*T a \* T a \* RH a \* I a \* V a in out* 1 2 34 5 *out w* (14)

0.0075 21.27 *T I in* (13)

to be:

environment within the comfort levels.

**8.5 Temperature modeling**

indoor heat generation or cooling.

appliances.

Where:

The constant 5 *a* takes into account heat generated indoors either by appliances or occupants. The above-proposed model is a simplification of a complex dependence. It must be noted that all the parameters involved are not independent. For example, the outdoor temperature greatly depends on irradiance and wind speed. Using regression analysis, the coefficients in equation (14) were determined.

For summer the following model was obtained:

$$T\_{in} = 0.818207 T\_{out} + 0.013562 \text{(RH)}\_{out} - 0.12907 V\_w + 0.00038I + 8.18 \tag{15}$$

For winter the following model was obtained:

$$T\_{in} = 0.898116 T\_{out} + 0.03914 \text{(RH)}\_{out} - 0.2545 V\_w + 0.00575 I + 4.20 \tag{16}$$

Figures 16 and 17 show the comparison of the measured and predicted indoor temperatures for summer and winter, respectively.

Fig. 16. Summer: measured and predicted indoor temperature [Makaka, Meyer; 2011]..

From equations (15) and (16) it can be noted that the outdoor temperature, solar radiation and outdoor relative humidity have a positive contribution to the indoor temperature, i.e. when the outdoor temperature, solar radiation and relative humidity increase, the indoor temperature increases. But when the wind speed increases the tendency is to lower the indoor temperature. From the models it was seen that the outdoor weather parameters do have varying impact on the indoor temperature but with high sensitivity to the outdoor temperature. The determination of the sensitivity of the models revealed that the indoor temperature could be predicted to 85% accuracy in summer and to 87% accuracy in winter, considering the outdoor temperature only.

From figures 16 and 17, it can be seen that the predicted temperatures agree well with the measured data, however in summer there were some significant differences at some peak temperatures, with the measured temperature being higher than the predicted. These peak temperature differences were due to the indoor heat generating activities such as cooking and ironing. It must be noted that in the model thermal properties of the building were not taken into consideration.

Fig. 17. Winter: Measured and predicted indoor temperature [Makaka, Meyer; 2011].

#### **9. Conclusions**

Fly ash bricks with the ratio 1:1 clay to fly ash mixing ratio were found to have the low water absorption, low thermal conductivity, high compressive strength and attractive decorative colour that eliminates the need for external plastering.

The monitoring results suggest that the building behaviour cannot be restricted to construction issues only. The thermal behaviour was the result of construction and nonconstruction factors, and their interaction as well.

Passive solar gain, ventilation mechanisms, orientation, and socio-environmental factors such as the life style and behaviour of the occupants, associated with the use of complementary heating, operative heating (cooking, metabolic heat production), were found to have a significant impact on the indoor temperature distribution. The house performed differently in summer and winter. The indoor temperature followed the outdoor temperature implying that the solar radiation has a great impact on the indoor temperature variation.

The ventilation rate was found to depend on the ventilation component in use, windows were found to have a higher ventilation effect than doors. Correct opening and closing of windows can regulate the air infiltration thus controlling the indoor air quality.

When the day-to-day indoor temperature variations and the outdoor weather parameters are known, it is possible to reconstruct an approximate indoor temperature patterns during a given period. An indoor temperature prediction formula was modelled; it was shown that knowing the outdoor weather data parameters, the indoor temperature could be predicted. Outdoor temperature was found to have the largest impact on the indoor thermal environment. Significant differences were noted during cooking periods when a lot of heat was generated indoors, resulting in much higher measured indoor temperature than the model can predict. It is worth mentioning that the activities of the occupants play an important role in indoor temperature distribution.

#### **10. References**

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when the outdoor temperature, solar radiation and relative humidity increase, the indoor temperature increases. But when the wind speed increases the tendency is to lower the indoor temperature. From the models it was seen that the outdoor weather parameters do have varying impact on the indoor temperature but with high sensitivity to the outdoor temperature. The determination of the sensitivity of the models revealed that the indoor temperature could be predicted to 85% accuracy in summer and to 87% accuracy in winter,

From figures 16 and 17, it can be seen that the predicted temperatures agree well with the measured data, however in summer there were some significant differences at some peak temperatures, with the measured temperature being higher than the predicted. These peak temperature differences were due to the indoor heat generating activities such as cooking and ironing. It must be noted that in the model thermal properties of the building were not

Fig. 17. Winter: Measured and predicted indoor temperature [Makaka, Meyer; 2011].

decorative colour that eliminates the need for external plastering.

construction factors, and their interaction as well.

Fly ash bricks with the ratio 1:1 clay to fly ash mixing ratio were found to have the low water absorption, low thermal conductivity, high compressive strength and attractive

The monitoring results suggest that the building behaviour cannot be restricted to construction issues only. The thermal behaviour was the result of construction and non-

Passive solar gain, ventilation mechanisms, orientation, and socio-environmental factors such as the life style and behaviour of the occupants, associated with the use of

considering the outdoor temperature only.

taken into consideration.

**9. Conclusions**

[9] Wray, W., Balcomb, J.D., and Macfarland, R.D., (1979), A Semi Empirical method for estimating the performance of direct gain passive solar heated buildings. Proc. 3rd National Passive solar conference, San Jose California.

### **Nanogold Loaded, Nitrogen Doped TiO2 Photocatalysts for the Degradation of Aquatic Pollutants Under Sun Light**

Zahira Yaakob1, Anila Gopalakrishnan2, Silija Padikkaparambil1, Binitha N. Narayanan2,\* and Resmi M. Ramakrishnan2 *1Department of Chemical and Process Engineering,*

*Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor, 2Department of Chemistry, Sree Neelakanta Government Sanskrit College Pattambi, Palakkad, Kerala, 1Malaysia 2India*

#### **1. Introduction**

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[9] Wray, W., Balcomb, J.D., and Macfarland, R.D., (1979), A Semi Empirical method for

National Passive solar conference, San Jose California.

estimating the performance of direct gain passive solar heated buildings. Proc. 3rd

Along with the rapid development of industry, various issues related to energy and environment got generated which are now grown into a significant level. The hazardous waste materials with high concentrations are being discharged directly or indirectly into water bodies without adequate treatment to remove harmful compounds. The World Bank estimates that 17 to 20 percent of industrial water pollution comes from textile dyeing and treatment causing a major global problem. A facile and cheap method for removing inorganic and organic pollutants from wastewater has much relevance in modern world. Dyes are an important class of aquatic pollutants. Its complexity and variety makes it difficult to find a unique treatment procedure that entirely covers the effective elimination of all types of dyes. Particularly, biochemical oxidation suffers from significant limitations since most dyestuffs commercially available have been intentionally designed to resist aerobic microbial degradation and also they may get converted into toxic or carcinogenic compounds. The physical methods such as flocculation, reverse osmosis and adsorption on activated charcoal are nondestructive and merely transfer the pollutant to other media, thus causing secondary pollution (Binitha, 2009, as cited in Belver, 2006) Heterogeneous photocatalysis with various oxide semiconductor photocatalysts is an efficient and rapidly expanding purification technique for water and air. Semiconductor-oxides are a popular class of materials because of their functionalities and applications in the field of photocatalysis and generation of photoelectricity.

There has been greater attention on the photocatalytic activity of nanocrystalline TiO2 after the discovery of photodecomposition of water on Titania [Ali, 2009; Hao, 2008; Parida, 2007;

\* Corresponding Author

Xiang, 2008; Dambar, 2007; Wang, 2004; Colmenares, 2006; Kolenko, 2005; Shengli, 2006; Baiju, 2007]. Titania (titanium oxide) is considered as one of the most promising heterogeneous photocatalyst owing to the facts such as high photocatalytic activity, strong oxidizing power, low cost, chemical and thermal stability, resistance to photo corrosion and non-toxicity. In addition, TiO2 possess favorable optoelectronic properties which makes it a well accepted photocatalyst for the degradation of various environmental pollutants (Meenal, 2009; Pirkanniemi; 2002).

Fig. 1. Degraded methylene blue solution under 1 h exposure to sun light over N doped TiO2 and Au loaded N doped TiO2

However, the high intrinsic band gap energies of major crystalline forms of TiO2 (3.2 eV for anatase phase and 3.0 eV for rutile) makes them effective photocatalysts only when the wavelengths of light is shorter than 387 nm. Thus only a small part of solar light is harvested if we use bare TiO2 photocatalysts (Haijian, 2008, as cited in Fujishima & Honda, 1972; Li, 2000). It is known that the UV part of the solar spectrum accounts only for about 4% of the incoming solar energy while the major part of the rest is visible light (Binitha, 2010). It is therefore of great significance to adjust the band structure of TiO2 to improve the photoreaction rate for the efficient use of solar energy for photocatalysis. There are several studies in recent years attempting the incorporation of the visible range of solar spectrum also in the photocatalytic process, which include dye sensitization, metal ion doping, nonmetal doping, etc. (Meenal, 2009, as cited in Choi, 1994; Shockley & Read, 1952; Asahi, 2001). The incorporation of specific dopants in TiO2 should improve the efficiency of the photocatalytic behavior by creating new band structures or by suppressing the recombination of photogenerated electron–hole pairs resulting in improved quantum efficiency (Zhiqiao, 2009).

The most feasible method for improving the photocatalytic performance of titania are considered as doping with metals as well as non metals. Recent researches concerning TiO2 doped with nonmetal elements such as nitrogen (Yohei, 2004, as cited in Sato, 1986; Asahi, 2001; Morikawa, 2001), fluorine (Yohei, 2004, as cited in Hatori, 1998), sulfur (Yohei, 2004 as cited in Umebayashi, 2002) and carbon (Yohei, 2004, as cited in Khan, 2002) have been reported. Among the different anion dopants, nitrogen is observed to be the most effective one and is widely studied.

Xiang, 2008; Dambar, 2007; Wang, 2004; Colmenares, 2006; Kolenko, 2005; Shengli, 2006; Baiju, 2007]. Titania (titanium oxide) is considered as one of the most promising heterogeneous photocatalyst owing to the facts such as high photocatalytic activity, strong oxidizing power, low cost, chemical and thermal stability, resistance to photo corrosion and non-toxicity. In addition, TiO2 possess favorable optoelectronic properties which makes it a well accepted photocatalyst for the degradation of various environmental pollutants

Fig. 1. Degraded methylene blue solution under 1 h exposure to sun light over N doped

However, the high intrinsic band gap energies of major crystalline forms of TiO2 (3.2 eV for anatase phase and 3.0 eV for rutile) makes them effective photocatalysts only when the wavelengths of light is shorter than 387 nm. Thus only a small part of solar light is harvested if we use bare TiO2 photocatalysts (Haijian, 2008, as cited in Fujishima & Honda, 1972; Li, 2000). It is known that the UV part of the solar spectrum accounts only for about 4% of the incoming solar energy while the major part of the rest is visible light (Binitha, 2010). It is therefore of great significance to adjust the band structure of TiO2 to improve the photoreaction rate for the efficient use of solar energy for photocatalysis. There are several studies in recent years attempting the incorporation of the visible range of solar spectrum also in the photocatalytic process, which include dye sensitization, metal ion doping, nonmetal doping, etc. (Meenal, 2009, as cited in Choi, 1994; Shockley & Read, 1952; Asahi, 2001). The incorporation of specific dopants in TiO2 should improve the efficiency of the photocatalytic behavior by creating new band structures or by suppressing the recombination of photogenerated electron–hole pairs resulting in

The most feasible method for improving the photocatalytic performance of titania are considered as doping with metals as well as non metals. Recent researches concerning TiO2 doped with nonmetal elements such as nitrogen (Yohei, 2004, as cited in Sato, 1986; Asahi, 2001; Morikawa, 2001), fluorine (Yohei, 2004, as cited in Hatori, 1998), sulfur (Yohei, 2004 as cited in Umebayashi, 2002) and carbon (Yohei, 2004, as cited in Khan, 2002) have been reported. Among the different anion dopants, nitrogen is observed to be the most effective

(Meenal, 2009; Pirkanniemi; 2002).

TiO2 and Au loaded N doped TiO2

improved quantum efficiency (Zhiqiao, 2009).

one and is widely studied.

Fig. 2. Band structure of semiconductor titania: Making it suitable for pollutant degradation

Sato (Sato, 1986) was the first person who reported N-doped titania by annealing the mixtures of a commercial titanium hydroxide and NH4Clor NH4OH, which showed higher photocatalytic activity in the visible-light region. Asahi et al. (Asahi, 2001) reported that nitrogen-doped titania could induce the visible-light activity in which nitrogen atoms substitute a few oxygen atoms (0.75%), and the doped nitrogen was responsible for the visible-light sensitivity. These two studies had kicked off a new area of research to extend the absorbance of TiO2 into visible-light region by means of nitrogen-doping. There has been several methods reported thereafter for the preparation of N doped TiO2, such as high temperature treatment of TiO2 under NH3 flow (Hao, 2008, as cited in Asahi, 2001; Irie, 2003; Diwald, 2004), hydrolytic process (Hao, 2008 as cited in Ihara, 2003; Noda, 1986; Salthivel, 2003), mechanochemical (Hao, 2008 as cited in Yin, 2003; Wang, 2004), reactive DC magnetron sputtering (Hao, 2008, as cited in Lindgren, 2003; Chen, 2004), sol–gel (Hao, 2008, as cited in Burda, 2003), solvothermal process [(Hao, 2008, as cited in Aita, 2004), calcination of a complex of Ti ion with a nitrogen containing ligand (Hao, 2008, as cited in Sano, 2004), calcination in nitrogen atmosphere etc.

Among the different preparation procedures, sol gel route is the preferred one because of its endowed nature. The temperatures required for all stages of the process involved in the conversion of sol to gel apart from densification are low, avoiding material degradation and resulting in high purity and stoichiometry of the products. The fact that the precursor metal alkoxides are volatile in general and thus are easily purifiable, substantiates the formation of high purity products. Also, since the organometallic precursors involving different metals are normally miscible with each other, a homogeneous controlled doping can be achieved easily. In addition, during sol gel synthesis the chemical conditions are mild and thus even biological species including enzymes and whole cells may be entrapped retaining their functions after doping on sol gel prepared metal oxides. Besides, the formation of highly porous and nanocrystalline materials can be achieved by sol gel method, by means of appropriate chemical modification of the precursors, adequate control over the rates of hydrolysis and condensation, resulting in the formation of colloid particles of suitable size, porosity and the pore size, and thus achieving a fine control over the pore wall chemistry of the final material. However, there are only a few reports on the anion-doped photocatalysts prepared using wet-methods such as sol–gel and co-precipitation owing to the difficulties involved in the procedure (Hao,2008). Still there are some reports on the N doped sol gel titania which shows good visible light activity for the degradation of pollutants (Dewi, 2010; Hu, 2010; Jian, 2006; Liu, 2005; Min, 2008)).

In the previous studies, the shifting of the absorbance of TiO2 to visible light upon nitrogen doping is explained in two ways. One proposed mechanism is the narrowing of the band gap by mixing the N2p and O2p states. Another explanation is the existence of two absorption edges in the UV visible spectra, the one around 400 nm, the resultant of the band structure of original TiO2 and second one around 530 nm which is attributed to the newly formed N2p band located above the O2p valence band. The incorporation of nonmetal dopant atoms into the lattice structure of titania is believed to decrease the band gap, and shift its response to the visible part of the solar spectrum (Xin & Quingquan, 2008, as cited in Asahi, 2001; Khan, 2002; Ohno, 2004; Lin, 2007).

Fig. 3. Schematic picture of reduced band gap of titania as a result of mixing of N 2p and O 2p stages

As clear from the pictorial representation, the hole and electron pair separation is small when the electron is excited by the visible light after N doping and thus they can recombine easily, which will reduce the efficiency of photons. Thus suppression of the recombination of hole– electron pairs is a necessity for visible light active photocatalysts.

Fig. 4. The schematic diagram representing the shifting of TiO2 absorption to visible region as a result of the newly formed N2P band above the TiO2 valance band

In the previous studies, the shifting of the absorbance of TiO2 to visible light upon nitrogen doping is explained in two ways. One proposed mechanism is the narrowing of the band gap by mixing the N2p and O2p states. Another explanation is the existence of two absorption edges in the UV visible spectra, the one around 400 nm, the resultant of the band structure of original TiO2 and second one around 530 nm which is attributed to the newly formed N2p band located above the O2p valence band. The incorporation of nonmetal dopant atoms into the lattice structure of titania is believed to decrease the band gap, and shift its response to the visible part of the solar spectrum (Xin & Quingquan, 2008, as cited in

+

Fig. 3. Schematic picture of reduced band gap of titania as a result of mixing of N 2p and O

As clear from the pictorial representation, the hole and electron pair separation is small when the electron is excited by the visible light after N doping and thus they can recombine easily, which will reduce the efficiency of photons. Thus suppression of the recombination of hole–

> valance band

+ N 2P band

Fig. 4. The schematic diagram representing the shifting of TiO2 absorption to visible region

as a result of the newly formed N2P band above the TiO2 valance band

conduction band

valance band

conduction band

Asahi, 2001; Khan, 2002; Ohno, 2004; Lin, 2007).

h v

electron pairs is a necessity for visible light active photocatalysts.

h v

2p stages

Fig. 5. The schematic diagram showing the role of doped metal nanoparticles in the photocatalytic activity. Metal nanoparticles act as an electron sink, supporting interfacial charge transfer and reduce the chances for charge recombinations.

Doped metal nanoparticles are believed to suppress the recombination of photo-induced electron-hole pairs, when they migrate from the interior of the photocatalyst to the metal surface resulting in increased photo quantum efficiency (Xin & Quingquan, 2008, as cited in Choi, 1994; Lin & Yu, 1998). Thus, metal nanoparticles can be considered as an electron sink, which can promote interfacial charge transfer and consequently less charge recombinations occur.

Thus in the present work, we are looking for a synergestic effect from both dopings. That is the anion doping can provide the narrowing of the band gap and the noble metal nanoparticles are suppose to suppress recombinations. Our objective is the preparation of unsurpassed photocatalysts which are sun light active. Nano gold loaded nitrogen doped TiO2 photocatalyst was prepared through the sol–gel route using titanium isopropoxide as titanium source, ammonium nitrate as nitrogen precursor and chloroauric acid was used as the precursor for gold nanoparticles. The combined doping of nano gold and nitrogen leads to the high activity of the prepared system for the photodegradation of MB under visible light irradiation. Here, nanogold is observed to provide substantial progress in the photocatalytic activity compared to that of simple nitrogen doped titania for the MB degradation under sunlight.

#### **2. Experimental**

#### **2.1 Preparation of photocatalysts**

Au-loaded N doped TiO2 nano powders were prepared by sol–gel process. The sol was prepared following a reported procedure (Sunajadevi & Sugunan, 2004). Titanium isopropoxide (98%, Aldrich) was used as the precursor of TiO2. 50 ml of Titanium (IV) isopropoxide was hydrolyzed in 600 ml water containing 5 ml nitric acid. Precipitation occurred immediately and the mixture was stirred continuously at room temperature to form a highly dispersed sol. To this, 4.39g ammonium nitrate was added to get the N-doped titania and stirring was continued for another 4h. The sol was then aged for two days and dried at 70C. The powdered sample was then calcined at 400 C for 5h to get the yellow coloured nitrogen doped titania.

Nano gold loading: Different percentage of nanogold are loaded on loaded on N- doped TiO2 photocatalysts by deposition– precipitation method, which can provide nano-sized gold particles with strong contact of Au particles with the support. Chloro auric acid was used as the gold precursor. The aqueous Chloroauric acid solution(2.1x10-3M) is heated to 70 0C. Then the pH of the solution was adjusted to 8 by dropwise addition of 0.5M NaOH solution. The required amount of the support was added in to it with vigourous stirring. The stirring was continued at 70C for 2h. The pH was maintained as 8 throughout the preparation. It was then cooled to room temperature, filtered and washed with distilled water to make it free from chloride ions. Subsequently it was dried at 80C for overnight followed by calcination at 200 C for 5 h. The catalytic systems thus produced are designated as NTiO2, 1AuNTiO2 and 2AuNTiO2 for nitrogen doped TiO2 with no gold loading, 1% and 2% Au loading respectively.

#### **2.2 Catalyst characterization**

XRD patterns of the samples were recorded for 2θ between 10 and 80° on a Bruker AXS D8 Advance diffractometer employing a scanning rate of 0.02°/S with Cu Kα radiation (λ=1.5418). The FTIR spectra were recorded in Thermo Nicolet, Avatar 370 spectrometer in the region 400–4,000 cm-1. TEM photographs of the prepared systems were taken in JEOL JEM 2100 Electromicroscope. SEM pictures are collected using a JEOL Model JSM - 6390LV.

#### **2.3 Photocatalytic degradation studies**

Photocatalytic degradation of Methyleneblue (MB) was done by the use of solar energy. All outdoor experiments were carried out in closed Pyrex flasks at room temperature with stirring. The irradiation was performed on sunny days, from 11.00 to 14.00 h when solar intensity fluctuations were minimum. The samples were immediately centrifuged and the quantitative determination of dye was determined using colorimeter (CL 157 - ELICO) before and after the irradiation. Experiments were repeated to get better results. The MB concentraction of 5 mg/L mmol at 2h of exposure to sunlight are used for all measurements except for time study.

#### **3. Results and discussion**

With an objective to develop a visible light or solar energy responsive photocatalyst, nitrogen doping is done on titania where ammonium nitrate is used as the N precursor. N dopant is added to the stable sol and with gelation the development of a yellow colour is observed whereas introduction of gold changes the colour to violetish ash. With the increase in gold loading, the colour was found to deepen. The different catalytic systems prepared are characterized using various techniques.

#### **3.1 Photocatalyst characterization**

It is well established that the electron and hole recombination can be suppressed by increasing the crystallinity of the Semiconductor, minimizing the crystal defects which act as

form a highly dispersed sol. To this, 4.39g ammonium nitrate was added to get the N-doped titania and stirring was continued for another 4h. The sol was then aged for two days and dried at 70C. The powdered sample was then calcined at 400 C for 5h to get the yellow

Nano gold loading: Different percentage of nanogold are loaded on loaded on N- doped TiO2 photocatalysts by deposition– precipitation method, which can provide nano-sized gold particles with strong contact of Au particles with the support. Chloro auric acid was used as the gold precursor. The aqueous Chloroauric acid solution(2.1x10-3M) is heated to 70 0C. Then the pH of the solution was adjusted to 8 by dropwise addition of 0.5M NaOH solution. The required amount of the support was added in to it with vigourous stirring. The stirring was continued at 70C for 2h. The pH was maintained as 8 throughout the preparation. It was then cooled to room temperature, filtered and washed with distilled water to make it free from chloride ions. Subsequently it was dried at 80C for overnight followed by calcination at 200 C for 5 h. The catalytic systems thus produced are designated as NTiO2, 1AuNTiO2 and 2AuNTiO2 for nitrogen doped TiO2 with no gold loading, 1% and 2% Au loading respectively.

XRD patterns of the samples were recorded for 2θ between 10 and 80° on a Bruker AXS D8 Advance diffractometer employing a scanning rate of 0.02°/S with Cu Kα radiation (λ=1.5418). The FTIR spectra were recorded in Thermo Nicolet, Avatar 370 spectrometer in the region 400–4,000 cm-1. TEM photographs of the prepared systems were taken in JEOL JEM 2100 Electromicroscope. SEM pictures are collected using a JEOL Model JSM - 6390LV.

Photocatalytic degradation of Methyleneblue (MB) was done by the use of solar energy. All outdoor experiments were carried out in closed Pyrex flasks at room temperature with stirring. The irradiation was performed on sunny days, from 11.00 to 14.00 h when solar intensity fluctuations were minimum. The samples were immediately centrifuged and the quantitative determination of dye was determined using colorimeter (CL 157 - ELICO) before and after the irradiation. Experiments were repeated to get better results. The MB concentraction of 5 mg/L mmol at 2h of exposure to sunlight are used for all measurements

With an objective to develop a visible light or solar energy responsive photocatalyst, nitrogen doping is done on titania where ammonium nitrate is used as the N precursor. N dopant is added to the stable sol and with gelation the development of a yellow colour is observed whereas introduction of gold changes the colour to violetish ash. With the increase in gold loading, the colour was found to deepen. The different catalytic systems prepared

It is well established that the electron and hole recombination can be suppressed by increasing the crystallinity of the Semiconductor, minimizing the crystal defects which act as

coloured nitrogen doped titania.

**2.2 Catalyst characterization**

except for time study.

**3. Results and discussion**

are characterized using various techniques.

**3.1 Photocatalyst characterization**

**2.3 Photocatalytic degradation studies**

recombination centers (Tsugio, 2006, as cited in Sclafani, 1990). Our objective here is the preparation of fine particles of small band gap semiconductor with high degree of Crystallinity, which can make photocatalysts that are capable of showing visible light activity. The crystalline nature of the present samples were analyzed using XRD analysis. Both anatase as well rutile phase were visible in the samples where anatase phase was the predominant one. The foremost peak corresponding to (1 0 1) reflections of the anatase phase of TiO2 was well evident at the angle of 25.28◦, as well as the minor peaks were appeared around 37.8◦, 48.0◦, 53.8◦ and 55.1◦. The major peak of the (1 1 0) diffraction of rutile was observed at the angle of 27.50◦, whereas the minor peaks appeared at 36.15◦, 41.33◦, 54.44◦, 56.76◦, 62.89◦ and 69.17◦. Weight ratios of each phase were calculated using the following equation:

$$\mathbf{W\_r} = \frac{\mathbf{A\_R}}{0.884\mathbf{A\_A} + \mathbf{A\_R}}$$

Here AA represents the integrated intensity of the anatase (1 0 1) peak and AR the integrated intensity of the rutile (1 1 0) peak (jirapat, 2009, as cited in Gribb, 1997). Increase in gold loading increases the anatase to rutile ratio. This observation is apparently is surprising since we are adding gold precursor to the calcined gel. Thus it is expected that the 2AuTiO2 can show maximum photoactivity, since anatase is considered as the most photocatalytically active form of TiO2. TiO2 obtained by following the same procedure without any N doping is also showing the existence of both anatase and rutile as the crystalline phases (XRD is not shown) (Sunajadevi & Sugunan, 2004). The crystallite size of different systems were calculated using Scherrer equation and the results are provided in table 1.

Table 1. Anatase to rutile ratio and crystallite size of gold loaded catalysts

Fig. 6. XRD patterns of different systems

The surface morphology of different systems was analyzed using SEM. In Fig. 7, SEM micrographs of 1AuNTiO2 and 2AuNTiO2 are given. All samples appeared as agglomerations of smaller particles with a high tendency for crystallization.

Fig. 7. SEM images of the prepared systems

The TEM micrographs of the photocatalysts show that the gold particles are highly dispersed on the surface of N doped TiO2 and the mean diameter of gold particles estimated from the TEM images are less than 5nm. In the case of 1AuNTiO2, only few gold particles are visible and its size is also found to be smaller when compared to that in 2AuNTiO2. At higher gold loading of 2%, there is competent dispersion for the nanoparticles and the gold particles are visible as sharp dark spots over the gray coloured support. Furthermore, the

The surface morphology of different systems was analyzed using SEM. In Fig. 7, SEM micrographs of 1AuNTiO2 and 2AuNTiO2 are given. All samples appeared as

The TEM micrographs of the photocatalysts show that the gold particles are highly dispersed on the surface of N doped TiO2 and the mean diameter of gold particles estimated from the TEM images are less than 5nm. In the case of 1AuNTiO2, only few gold particles are visible and its size is also found to be smaller when compared to that in 2AuNTiO2. At higher gold loading of 2%, there is competent dispersion for the nanoparticles and the gold particles are visible as sharp dark spots over the gray coloured support. Furthermore, the

agglomerations of smaller particles with a high tendency for crystallization.

**1AuTi**

**2AuTi**

Fig. 7. SEM images of the prepared systems

lattice fringes of the crystallographic planes of anatase and rutlies are found to be visible, consistent with the XRD patterns. The visible TiO2 particles are more or less spherical in shape and are found to be of lower size of around 10 – 20 nm diameter

Fig. 8. TEM images of the prepared systems

In the FTIR spectra of the systems shown in Fig. 9, a broad peak seen around 3400 cm-1 can be assigned to stretching vibration mode of the OH groups within the TiO2 sol–gel. The corresponding bending vibration band was observed at 1637 cm-1. The TiO2–OH bonds arise from the hydrolysis reactions occurring during the gelling of the titanium alkoxide. In the low energy region of the spectrum, the bands around 500 cm-1 can be assigned to bending vibrations of Ti–O bonds.

Fig. 9. FTIR spectra of the prepared systems

#### **3.2 Photoactivity results**

Photodegradation of MB was done to get the information on the pollutant degrading capability of the present systems. The reaction variables were optimized using 2AuNTiO2 to achieve the conditions for maximum degradation.

#### **3.2.1 Effect of catalyst dosage**

A series of experiments were carried out to optimize the catalyst loading by varying the amount catalyst from 0.05g – 0.20g/50ml MB solution of concentration 5 mg/l. The degradation results for 2 h irradiation are shown in Fig. 10. The rate of degradation increased linearly with increase in catalyst weight from 0.05g to 0. 10g which then decreases with further increase in the amount of catalyst used. The initial enhancement in photoactivity with catalyst weight may be due to the increase in number of photons absorbed and the number of dye molecules adsorbed on the catalyst molecules. Also the density of the catalyst particles in the area of illumination increases with the catalyst dosage. When the amount of catalyst is exceeding certain limit, the dye molecules available are not sufficient for adsorption and hence the additional catalyst powder is not taking part in the photocatalytic activity and consequently the rate becomes independent of the amount of catalyst beyond certain limit. It is reported that the increase in opacity of the solution at high catalyst dosage decreases the penetration of light inside the solution with a consequent decrease in the photoreduction of the dye (Binitha, 2011, as cited in Maruthamuthu, 1989). Thus, the optimum amount of catalyst needed to get maximum degradation of pollutant in the present case is 0.1 g.

#### **3.2.2 Effect of volume of MB**

166 Solar Power

In the FTIR spectra of the systems shown in Fig. 9, a broad peak seen around 3400 cm-1 can be assigned to stretching vibration mode of the OH groups within the TiO2 sol–gel. The corresponding bending vibration band was observed at 1637 cm-1. The TiO2–OH bonds arise from the hydrolysis reactions occurring during the gelling of the titanium alkoxide. In the low energy region of the spectrum, the bands around 500 cm-1 can be assigned to bending

**4000 3500 3000 2500 2000 1500 1000 500**

**)**

Photodegradation of MB was done to get the information on the pollutant degrading capability of the present systems. The reaction variables were optimized using 2AuNTiO2 to

A series of experiments were carried out to optimize the catalyst loading by varying the amount catalyst from 0.05g – 0.20g/50ml MB solution of concentration 5 mg/l. The degradation results for 2 h irradiation are shown in Fig. 10. The rate of degradation increased linearly with increase in catalyst weight from 0.05g to 0. 10g which then decreases with further increase in the amount of catalyst used. The initial enhancement in photoactivity with catalyst weight may be due to the increase in number of photons absorbed and the number of dye molecules adsorbed on the catalyst molecules. Also the density of the catalyst particles in the area of illumination increases with the catalyst dosage. When the amount of catalyst is exceeding certain limit, the dye molecules available are not sufficient for adsorption and hence the additional catalyst powder is not taking part in the photocatalytic activity and consequently the rate becomes independent of the amount of catalyst beyond certain limit. It is reported that the increase in opacity of the solution at high catalyst dosage decreases the penetration of light inside the solution with a consequent decrease in the photoreduction of the

**Wavenumber (cm-1**

**N TiO2**

**1% Au NTiO2**

**2% AuNTiO2**

vibrations of Ti–O bonds.

**% Transmittance**

**3.2 Photoactivity results**

**3.2.1 Effect of catalyst dosage**

Fig. 9. FTIR spectra of the prepared systems

achieve the conditions for maximum degradation.

The influence of volume of MB on degradation rate was studied by varying the volume of the dye from 25 to 100 ml at a constant TiO2 loading of 2 g/L for 2 h exposure to sunlight. It was observed from Fig. 11, that the degradation rate increased from 25 ml to 50 ml and then decreased with further increase in the amount of dye. The absorption of light by the pollutant may be dominated at higher volumes which in turn decrease the absorbance of light by the catalyst causing a reduction in the photocatalytic activity.

Fig. 10. Effect of catalyst dosage on the degradation of 50 ml of 5 mg/L MB for 2h irradiation

#### **3.2.3 Effect of time**

The degradation of 50 ml of 5mg/L MB was investigated by changing the irradiation time from 1 h to 3 h and the activity over the three catalytic systems is plotted in Fig. 12. It is

Fig. 11. Effect of volume of MB solution on the degradation of 5 mg/L MB for 2h irradiation using 2g/L catalyst

observed that the degradation reaches 100 % within 3 h of exposure to sunlight in the case of 2AuNTiO2. This system is thus found to be capable for complete degradation of dye pollutants within this short duration of 3 h solar irradiation. Lower loading of gold (1%) also showed improvement in the photocatalytic activity when compared to N doped TiO2 without metal nanoparticles. All the three catalytic systems studied showed far better activity than undoped titania which showed very low photodegradation of MB (not included in the figure) , less that 10% even after keeping for 3 h under sunlight.

Fig. 12. Effect of Time on the degradation of 50 ml of 5 mg/L MB over a catalyst dosage of 2g/L

#### **4. Conclusion**

In conclusion, we are reporting the successful preparation of highly efficient photocatalytic systems, N-doped TiO2 and Au nanoparticle incorportated N doped TiO2. The photodegradation of aquatic pollutant methylene blue over N-doped TiO2 and Au nanoparticle incorportated N doped TiO2 catalysts were investigated and the catalytic performances were compared. Visible light activity was achieved for TiO2 upon anion doping. It was found that Au nanoparticle loading over TiO2 is capable of improving the photocatalytic activity of N doped TiO2 to a greater extent and the inserted metal nanoparticles are believed to act as electron sinks to prevent the recombination of electronhole pairs. In addition, X-ray diffraction patterns reveal the suppression of rutile phase with increase in the percentage of gold loading which also can be considered as a favorable factor to obtain superior photoactivity. The inserted gold particles were found to have spherical morphology of less than 5 nm dimension as evident from TEM micrographs. The 2AuNTiO2 catalytic system was found to be the best among the three showing complete removal of the pollutant within 3 h exposure to sunlight.

#### **5. Acknowledgment**

The authors acknowledge the UKM, the grant number UKM-OUP-NBT-27-118/2009 for the financial support. STIC, CUSAT, Kochi, India is acknowledged for XRD, FTIR and SEM analyses.

#### **6. References**

168 Solar Power

observed that the degradation reaches 100 % within 3 h of exposure to sunlight in the case of 2AuNTiO2. This system is thus found to be capable for complete degradation of dye pollutants within this short duration of 3 h solar irradiation. Lower loading of gold (1%) also showed improvement in the photocatalytic activity when compared to N doped TiO2 without metal nanoparticles. All the three catalytic systems studied showed far better activity than undoped titania which showed very low photodegradation of MB (not

Fig. 12. Effect of Time on the degradation of 50 ml of 5 mg/L MB over a catalyst dosage of 2g/L

In conclusion, we are reporting the successful preparation of highly efficient photocatalytic systems, N-doped TiO2 and Au nanoparticle incorportated N doped TiO2. The photodegradation of aquatic pollutant methylene blue over N-doped TiO2 and Au nanoparticle incorportated N doped TiO2 catalysts were investigated and the catalytic performances were compared. Visible light activity was achieved for TiO2 upon anion doping. It was found that Au nanoparticle loading over TiO2 is capable of improving the photocatalytic activity of N doped TiO2 to a greater extent and the inserted metal nanoparticles are believed to act as electron sinks to prevent the recombination of electronhole pairs. In addition, X-ray diffraction patterns reveal the suppression of rutile phase with increase in the percentage of gold loading which also can be considered as a favorable factor to obtain superior photoactivity. The inserted gold particles were found to have spherical morphology of less than 5 nm dimension as evident from TEM micrographs. The 2AuNTiO2 catalytic system was found to be the best among the three showing complete removal of the

The authors acknowledge the UKM, the grant number UKM-OUP-NBT-27-118/2009 for the financial support. STIC, CUSAT, Kochi, India is acknowledged for XRD, FTIR and SEM

**4. Conclusion**

pollutant within 3 h exposure to sunlight.

**5. Acknowledgment**

analyses.

included in the figure) , less that 10% even after keeping for 3 h under sunlight.

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#### **1. Introduction**

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visible light photocatalytic activity of nitrogen-doped titania via solvothermal process. *Journal of Solid State Chemistry,* Vol.177, No. 9, (July 2004), pp. 3235–3238 Zhiqiao, H., Lei, X., Shuang, S., Cheng, W., Jinjun, T., Fangyue, H., Qi, L., Jianmeng, C.,

Xinhua, X. (2010). The impact of silver modification on the catalytic activity of iodine-doped titania for p-chlorophenol degradation under visible-light irradiation. *Journal of Molecular Catalysis A: Chemical*, Vol. 319, (December 2009), pp. 78–84

"Solar radiation plays a fundamental role in the marine environment. It supplies the energy that drives thermodynamic and photochemical processes in the sea, including the heating of waters, their stratification and movement, and their evaporation, also the warming of the atmosphere, the photosynthesis of the organic matter essential for the maintenance of life and the functioning of marine ecosystems, and the photo-oxidation of marine pollutants" (Dera & Woźniak, 2010). The amount of solar energy reaching the sea surface can be determined by means of direct measurements or calculations with the use of various models of different precision and complexity. Due to obvious reasons, systematic, direct measurements in the area of oceans and seas with the spatial resolution satisfactory for monitoring and analysis of several processes like the ones mentioned above, are impossible in practice. On the other hand, credibility of modelling strongly depends on model's input data – their quality and time resolution. The most difficult task in calculating the solar energy stream at the sea surface using a model is to obtain its momentary value. It is caused by the lack of precise information on the components of the Earth's atmosphere influencing the solar radiation along its way from the upper boundary to the sea surface. The most important of these components is the cloudiness but other parameters like aerosol optical thickness, the content of water vapour or ozone should also be taken into account.

Fast development of satellite technique in the field of Earth observation and monitoring makes it possible to get more or less precise information about the atmosphere properties which play an important role in solar energy transfer to the Earth's surface in every place and further into the sea.

The idea of our consideration is to provide a broad overview of contemporary space-borne sources of data necessary to increase the accuracy of simple models of solar energy transfer through the Earth's atmosphere, especially over the oceans and seas.

#### **2. Solar radiation**

The Sun, a medium-sized star, produces energy as a result of thermo-nuclear processes in its interior. Recently it has been estimated to be about 3.9×1026 W. This power is radiated into space uniformly in all directions. Fundamental physical laws tell us that the intensity of this radiation decreases as the inverse square of the distance from the source. At the mean distance from the Sun to the Earth this energy, in total, is known as solar "constant". Its spectral distribution is close to the radiation of a blackbody at the temperature about 6000 K but, due to different processes inside and on the surface of the Sun, this dependence is not as smooth as it results from Planck's law. It is the reason that the first step to obtain the spectrum of solar energy at the Earth's surface is to determine, by means of measurements, its value in the upper frontier of the atmosphere (TOA1). Along the path to the Earth's surface, the incoming solar radiation undergoes the wave dependent processes of absorption and scattering. As a result, a part of it is reflected back to space, another part is absorbed by the elements of the air and the rest reaches the surface. It consists of two components: the direct radiation from the Sun and the diffuse radiation from the atmosphere. Under cloudless conditions the relationship between them depends on the position of the Sun in the sky - the higher the Sun elevation angle the higher the difference between them. In Fig. 1 one can see an exemplary spectral distribution of the solar irradiance at the sea level calculated for real cloudless atmosphere over the Baltic Sea near the solar noon on 2011-06-21 at 11:00 UTC (the Sun elevation angle was about 59º). The evident differences between the shapes of the extraterrestrial and other curves are due to the light absorption and scattering processes which depend on some components of the air. The most important of these processes are: molecular scattering, absorption by water vapour, ozone, and other atmospheric gases as well as attenuation (absorption and scattering) by atmospheric aerosols. The strength of these processes depends to a different degree on concentration and distribution in the atmosphere of the accounted components. The most spectacular consequences of strong deviations of some components' concentrations from their common state are known as the global warming (due to the increase in the greenhouse gases concentration - the water vapour is one of them) or the "ozone hole" - the effect of decreased ozone concentration in the so-called ozone layer. The importance of such phenomena for life on our planet causes the need of their continuous monitoring. It is done by the networks of ground-based measuring stations like for instance WMO-Global Atmospheric Watch observing system, Network for the Detection Atmospheric Composition Change, NOAA Earth System Research Laboratory/Global Monitoring Division etc. It is obvious, that such networks do not cover the whole Earth's surface and the shortage of information concerns in particular the atmosphere over the oceans and seas (Fig. 2). It is also true for the solar radiation monitoring . New opportunities in the area of global monitoring of the Earth's atmosphere and surface arose due to fast development of the space-based measurements. The satellite-based instruments provide regular views of any place of the Earth. Despite the fact that such measurements must still be supplemented by the ground-based measurements, it enables fast progress in broadening our knowledge in this area.

The traditional way of getting the information on the solar energy reaching the Earth's surface in the case of lack of direct measurements was to use more or less complicated models of light transmission in the atmosphere. To run the model it is necessary to gather the input data which characterize actual state of the atmospheric components important from this point of view, namely: the atmospheric pressure and humidity, aerosol optical

1 Top of the atmosphere

space uniformly in all directions. Fundamental physical laws tell us that the intensity of this radiation decreases as the inverse square of the distance from the source. At the mean distance from the Sun to the Earth this energy, in total, is known as solar "constant". Its spectral distribution is close to the radiation of a blackbody at the temperature about 6000 K but, due to different processes inside and on the surface of the Sun, this dependence is not as smooth as it results from Planck's law. It is the reason that the first step to obtain the spectrum of solar energy at the Earth's surface is to determine, by means of measurements, its value in the upper frontier of the atmosphere (TOA1). Along the path to the Earth's surface, the incoming solar radiation undergoes the wave dependent processes of absorption and scattering. As a result, a part of it is reflected back to space, another part is absorbed by the elements of the air and the rest reaches the surface. It consists of two components: the direct radiation from the Sun and the diffuse radiation from the atmosphere. Under cloudless conditions the relationship between them depends on the position of the Sun in the sky - the higher the Sun elevation angle the higher the difference between them. In Fig. 1 one can see an exemplary spectral distribution of the solar irradiance at the sea level calculated for real cloudless atmosphere over the Baltic Sea near the solar noon on 2011-06-21 at 11:00 UTC (the Sun elevation angle was about 59º). The evident differences between the shapes of the extraterrestrial and other curves are due to the light absorption and scattering processes which depend on some components of the air. The most important of these processes are: molecular scattering, absorption by water vapour, ozone, and other atmospheric gases as well as attenuation (absorption and scattering) by atmospheric aerosols. The strength of these processes depends to a different degree on concentration and distribution in the atmosphere of the accounted components. The most spectacular consequences of strong deviations of some components' concentrations from their common state are known as the global warming (due to the increase in the greenhouse gases concentration - the water vapour is one of them) or the "ozone hole" - the effect of decreased ozone concentration in the so-called ozone layer. The importance of such phenomena for life on our planet causes the need of their continuous monitoring. It is done by the networks of ground-based measuring stations like for instance WMO-Global Atmospheric Watch observing system, Network for the Detection Atmospheric Composition Change, NOAA Earth System Research Laboratory/Global Monitoring Division etc. It is obvious, that such networks do not cover the whole Earth's surface and the shortage of information concerns in particular the atmosphere over the oceans and seas (Fig. 2). It is also true for the solar radiation monitoring . New opportunities in the area of global monitoring of the Earth's atmosphere and surface arose due to fast development of the space-based measurements. The satellite-based instruments provide regular views of any place of the Earth. Despite the fact that such measurements must still be supplemented by the ground-based measurements, it enables fast progress in broadening our knowledge in

The traditional way of getting the information on the solar energy reaching the Earth's surface in the case of lack of direct measurements was to use more or less complicated models of light transmission in the atmosphere. To run the model it is necessary to gather the input data which characterize actual state of the atmospheric components important from this point of view, namely: the atmospheric pressure and humidity, aerosol optical

this area.

1 Top of the atmosphere

thickness (*AOT*), content of ozone, and cloudiness. One can do it using routine measurements or values obtained by interpolation between them, results of calculations with the use of other (meteorological) models, climatological values, models of the Earth's atmosphere etc. The space-based measurements of *AOT*, content of ozone, and water vapour in the column of the atmosphere, and cloudiness used as the input data can rise the precision of such models to a significant degree. To check this thesis we shall compare the results of calculations of solar energy input at the Baltic Sea surface performed with a simple model for irradiance on the horizontal plane at the Earth's surface presented in part 3.

Fig. 1. Direct, diffuse, and total solar irradiance for a real cloudless atmosphere over the Baltic Sea (long. 18.82°E, lat. 54.60°N) on 2011-06-21 near solar noon (11:00 UTC)

Fig. 2. The 132 WMO-GAW stations measuring total ozone with Dobson and/or Brewer spectrophotometers (GCOS-GAW ..., 2007)

#### **3. Model**

There are many ways of calculation of light transmission through the Earth's atmosphere from complicated models like developed in the 1970's LOWTRAN2 or MODTRAN3 to the simplest ones that take into account the dependence of the incoming radiation only on one variable (e.g. mean cloudiness). To compare the influence of different sources of the input data on the results of solar irradiance calculations at the sea level we have chosen a simple model proposed by Bird and Riordan (1986) with several later modifications (Krężel & Kozłowski, 2001; Krężel et al., 2008). The model enables one to calculate easily the contribution of the main constituents to the total attenuation of the solar radiation on its way from the upper frontier of the Earth's atmosphere to the sea surface.

#### **3.1 Calculation algorithms of direct solar radiation**

Considering the most important processes of interaction of the solar radiation passing to the sea surface with the atmosphere constituents, the direct irradiance of the horizontal area by the solar beam incidence (at the sea surface) could be expressed as:

$$E\_s(\lambda) = \cos \theta \frac{F\_s(\lambda)}{\beta^2} T\_R(\lambda) \cdot T\_a(\lambda) \cdot T\_{uv}(\lambda) \cdot T\_{O\_3}(\lambda) \cdot T\_G(\lambda) \tag{1}$$

where: the wavelength; - the solar zenith angle, *Fs*() the spectral density of the solar constant; =*Rs/R* - the factor defining the annual variability of the distance between the Earth and the Sun (*R* and *Rs* actual and mean distance between the Earth and the Sun, respectively); *TR*()...*TG*() the functions of the atmosphere transmission describing irradiance attenuation in the processes of molecular scattering, scattering and absorption by aerosols and absorption by water vapour, ozone and the most important constant gaseous components of the atmosphere, respectively.

In the algorithms the presented *Fs*() values and the coefficients of absorption of water vapour *awv*(), ozone *aO*3() and constant gaseous components of the atmosphere *aG*() for 122 wavelengths were accepted after Neckel and Labs (1981). The algorithms allowing to determine value and the solar zenith angle at a given place and any time were taken from Michalsky (1988).

To calculate the succeeding transmission functions one should know the so-called relative optical atmospheric mass *M*. At the sea level the expression of Kasten & Young (1989) considering curvature of the atmosphere was applied:

$$M = \left[\sin\chi\_s + 0.50572 \cdot \left(\frac{180\chi\_s}{\pi} \cdot 6.07995\right)^{-1.6364}\right]^1\tag{2}$$

where: the solar altitude 2 *s* is expressed in radians

2 Low-resolution propagation model and computer code for predicting atmospheric transmittance and background radiance from 350 to 40,000 cm-1 at a resolution of 20 cm-1

3 MODerate resolution atmospheric TRANsmission – computer program designed to model atmospheric propagation of electromagnetic radiation from 100 to 50,000 cm-1 spectral range at a resolution of 0.2 cm-1.

In the calculations of the atmospheric transmission functions (1) the following algorithms were used:

a. molecular scattering (Kneizys et al., 1980):

$$T\_r(\lambda) = \exp\left[\frac{-M'}{\lambda^4 \cdot (115.6406 \cdot \frac{1.335}{\lambda^2})}\right] \tag{3}$$

where: *M'*=*M*(*P/P*0), *P* - the atmospheric pressure [hPa], *P*0=1013 hPa; the wavelength is expressed in [µm].

b. aerosol attenuation:

174 Solar Power

There are many ways of calculation of light transmission through the Earth's atmosphere from complicated models like developed in the 1970's LOWTRAN2 or MODTRAN3 to the simplest ones that take into account the dependence of the incoming radiation only on one variable (e.g. mean cloudiness). To compare the influence of different sources of the input data on the results of solar irradiance calculations at the sea level we have chosen a simple model proposed by Bird and Riordan (1986) with several later modifications (Krężel & Kozłowski, 2001; Krężel et al., 2008). The model enables one to calculate easily the contribution of the main constituents to the total attenuation of the solar radiation on its way

Considering the most important processes of interaction of the solar radiation passing to the sea surface with the atmosphere constituents, the direct irradiance of the horizontal area by

> ( ) ( ) cos ( ) ( ) ( ) ( ) ( ) *3 s s 2 R a wv O G*

where: the wavelength; - the solar zenith angle, *Fs*() the spectral density of the solar constant; =*Rs/R* - the factor defining the annual variability of the distance between the Earth and the Sun (*R* and *Rs* actual and mean distance between the Earth and the Sun, respectively); *TR*()...*TG*() the functions of the atmosphere transmission describing irradiance attenuation in the processes of molecular scattering, scattering and absorption by aerosols and absorption by water vapour, ozone and the most important constant gaseous

In the algorithms the presented *Fs*() values and the coefficients of absorption of water vapour *awv*(), ozone *aO*3() and constant gaseous components of the atmosphere *aG*() for 122 wavelengths were accepted after Neckel and Labs (1981). The algorithms allowing to determine value and the solar zenith angle at a given place and any time were taken from

To calculate the succeeding transmission functions one should know the so-called relative optical atmospheric mass *M*. At the sea level the expression of Kasten & Young (1989)

180 -1.6364 -1 [sin 0.50572 ( 6.07995) ] *s M= +s -*

2 Low-resolution propagation model and computer code for predicting atmospheric transmittance and

3 MODerate resolution atmospheric TRANsmission – computer program designed to model atmospheric propagation of electromagnetic radiation from 100 to 50,000 cm-1 spectral range at a resolution of 0.2 cm-1.

is expressed in radians

(2)

*F E T TT T T =* (1)

from the upper frontier of the Earth's atmosphere to the sea surface.

the solar beam incidence (at the sea surface) could be expressed as:

**3.1 Calculation algorithms of direct solar radiation**

components of the atmosphere, respectively.

considering curvature of the atmosphere was applied:

2 *s*

background radiance from 350 to 40,000 cm-1 at a resolution of 20 cm-1

Michalsky (1988).

where: the solar altitude

**3. Model**

The influence of aerosols on the light transmission is taken into consideration by determination of so-called aerosol optical thickness of the atmosphere (*AOT*):

$$AOT(\lambda) \equiv \mathsf{r}\_a(\lambda) = -M \ln T\_a(\lambda) \tag{4}$$

where *Ta* – the light transmission through the cloudless atmosphere resulting from the presence of aerosols. For determination of *AOT* the classical formula of Angström (1963) was used:

$$AOT(\lambda) = \mathbb{B}\_n \mathbb{X}^{-b\_n} \tag{5}$$

For the Baltic Sea area the values of *bn* and *n* coefficients, typical for the regions of relatively low atmosphere dustiness, were applied, i.e. *n*=0.12 and *bn*=1.0274 for <0.5 µm and 1.2060 for >0.5 µm.

c. absorption by ozone is calculated from the formula:

$$T\_0(\mathcal{Z}) = \exp\left[-a\_0(\mathcal{Z}) \cdot O\_3 \cdot M\_0\right] \tag{6}$$

where the relative optical mass of the atmospheric ozone *M*0 was defined after Iqbal (1983):

$$M\_0 = \frac{(1 + \frac{h\_{l0}}{6370})}{(\cos^2 9 + \frac{2 \cdot h\_{l0}}{6370})^{0.5}}$$

and *h*0 = 22 km was accepted as the mean height of the maximum ozone concentration. The mean nine-year (1997-2005) *O*3 values, defining the ozone concentration in an atmospheric air column of a unit base area, for latitudes 50°- 60°N in individual months are shown in Table 1.

Table 1. Mean ozone concentration in the atmosphere in latitudes 50°- 60°N (TOMS, 2007)

d. absorption by water vapour; the expression presented by Leckner (1978) was used:

$$T\_w(\text{\AA}) = \exp\left[\frac{-0.2385 \cdot a\_w(\text{\AA}) \cdot \mathcal{W} \cdot M}{[1 + 20.07 \cdot a\_w(\text{\AA}) \cdot \mathcal{W} \cdot M]^{0.45}}\right] \tag{8}$$

where *W* is the water vapour mass in an atmospheric air column of unit base area and is calculated from the data on the water vapour pressure *e*0 at the sea level, according to the relation (Reitan, 1960):

$$\mathcal{W} = (0.123 + 0.152 \cdot \varepsilon\_0) \frac{P}{1000} \tag{9}$$

where: *e*0 and *P* are expressed in [hPa].

e. absorption by the other significant components of the atmosphere; the expression from the paper by Leckner (1978) was applied:

$$T\_{\mu}(\lambda) = \exp\left[\frac{-1.41 \cdot a\_{\text{u}}(\lambda) \cdot M'}{\left[1 + 118.3 \cdot a\_{\text{u}}(\lambda) \cdot M'\right]^{0.45}}\right] \tag{10}$$

In the model for determination of illumination at the sea level by direct solar radiation, the algorithms (1) to (10) were used. The source of the highest errors is undoubtedly the way of determination of solar radiation attenuation by aerosols. This error is the greatest when the instantaneous illumination values are calculated and it decreases with the extension of averaging period.

#### **3.2 Calculation algorithms of scattered radiation**

Algorithms presented in the paper of Bird and Riordan (1986) were used in the model construction.

It should be assumed that scattered radiation incident on the horizontal area is the total of three components:

$$E^{q}(\mathbf{y}) = E^{q\mathbf{R}}(\mathbf{y}) + E^{q\mathbf{q}}(\mathbf{y}) + E^{q\mathbf{q}}(\mathbf{y}) \tag{11}$$

These components have the following forms:

$$E\_{\rm dR}(\lambda) = F\_s(\lambda) \cdot \otimes \,^2 \cdot \cos \theta \cdot T\_{O\_2}(\lambda) \cdot T\_G(\lambda) \cdot T\_{uv}(\lambda) \cdot T\_{uv}(\lambda) \left[1 \cdot T\_R(\lambda)^{0.95}\right] \cdot 0.5 \tag{12}$$

$$\mathbf{E\_{dn}}(\boldsymbol{\lambda}) = \mathbf{F\_{s}}(\boldsymbol{\lambda}) \cdot \mathbf{B}^{\top\_{2}} \cdot \cos \boldsymbol{\mathfrak{s}} \cdot \mathbf{T\_{O\_{3}}}(\boldsymbol{\lambda}) \cdot \mathbf{T\_{G}}(\boldsymbol{\lambda}) \cdot \mathbf{T\_{uv}}(\boldsymbol{\lambda}) \cdot \mathbf{T\_{au}}(\boldsymbol{\lambda}) \cdot \mathbf{T\_{R}}(\boldsymbol{\lambda})^{1.5} \cdot [\mathbf{1} \cdot \mathbf{T\_{as}}(\boldsymbol{\lambda})] \cdot \mathbf{F} \tag{13}$$

$$E\_{d\lg}(\lambda) = \frac{[F\_s(\lambda)\cos\Theta + E\_{d\mathbb{R}} + E\_{d\mathbb{R}}(\lambda)]r\_s(\lambda)r\_{\lg}(\lambda)}{1 - r\_s(\lambda)r\_{\lg}(\lambda)}\tag{14}$$

0.45

0.45

() () () () *EE E E d dR da dg = ++* (11)

(14)

(9)

(8)

(10)

d. absorption by water vapour; the expression presented by Leckner (1978) was used:

*T = a W M*

*w w*

relation (Reitan, 1960):

averaging period.

construction.

three components:

*Edg*().

where: *e*0 and *P* are expressed in [hPa].

the paper by Leckner (1978) was applied:

**3.2 Calculation algorithms of scattered radiation**

These components have the following forms:

*dg*

0.2385 ( ) ( ) exp [1+20.07 ( ) ]

where *W* is the water vapour mass in an atmospheric air column of unit base area and is calculated from the data on the water vapour pressure *e*0 at the sea level, according to the

(0.123+0.152 1000 *0 P W = e )*

e. absorption by the other significant components of the atmosphere; the expression from

1.41 ( ) ( ) exp [1+118.3 ( ) ] *u u*

In the model for determination of illumination at the sea level by direct solar radiation, the algorithms (1) to (10) were used. The source of the highest errors is undoubtedly the way of determination of solar radiation attenuation by aerosols. This error is the greatest when the instantaneous illumination values are calculated and it decreases with the extension of

Algorithms presented in the paper of Bird and Riordan (1986) were used in the model

It should be assumed that scattered radiation incident on the horizontal area is the total of

0.95 ( ) ( ) cos ( ) ( ) ( ) ( )[1 ( ) ] 0.5 *3*

1.5 ( ) ( ) cos ( ) ( ) ( ) ( ) ( ) [1 ( )] *3*

[ ( )cos ( )] ( ) ( ) ( ) () () *s dR da s g*

*E = 1 -r r*

*-2 E F T TT T T dR s = - O G wv aa R* (12)

*-2 E F T TT T da s = O G wv aa T - F R Tas* (13)

*s g F EE + + r r*

*- M a T = a M*

*u*

*w - a W M*

*Taa*() and *Tas*() are transmission functions determined by aerosol absorption and scattering, respectively:

$$\tau\_{as}(\lambda) = \exp\left[\text{-}\_{000}(\lambda) \cdot \tau\_{\pi}(\lambda) \cdot M\right] \tag{15}$$

$$T\_{\rm av}(\lambda) = \exp\left\{-\left[1 - \alpha\_0(\lambda)\right] \cdot \tau\_a(\lambda) \cdot M\right\} \tag{16}$$

where: 0() is the so-called single scattering albedo (from definition *0 b = a+b* ; *b* and *a* are the volume coefficients of scattering and absorption, respectively), *a*() - the optical thickness of the atmosphere resulting from aerosol occurrence. For its determination formulas (4) and (5) were used.

It is assumed here that molecular scattering and scattering by aerosols are independent and also, that half of the irradiance is directed towards the lower hemisphere in the process of Rayleigh`s scattering, regardless of actual direction of incident solar radiation.

The dependence of single scattering albedo on the wavelength is defined by the relation:

$$\rho\_{000}(\lambda) = \epsilon\_{000}(0.4 \,\mu\text{m}) \cdot \exp\left[-\text{co}'(\ln \frac{\lambda}{0.4})^2\right] \tag{17}$$

where: 0(0.4 µm) - the single scattering albedo for the wavelength of 0.4 µm, *'* - the wavelength variation factor.

In the regions of relatively low atmospheric turbidity 0(0.4)=0.945 and '=0.095, *rg*() represents surface albedo and is one of the input data necessary for calculations whereas atmosphere albedo could be expressed as:

$$T\_{\mathcal{I}\_s}(\lambda) = T\_{\mathcal{O}\_s^\times}'(\lambda) T\_{\text{av}}'(\lambda) T\_{\text{av}}'(\lambda) \left\{ 0.5 \left[ 1 - T\_r'(\lambda) \right] + \left( 1 - F \right) T\_r'(\lambda) \left[ 1 - T\_{\text{av}}'(\lambda) \right] \right\} \tag{18}$$

In the relation (13) *F* denotes the contribution of radiation scattered towards the lower hemisphere, which in the case of molecular scattering could be accepted as 0.5 and for aerosol attenuation depends on the solar zenith angle:

$$F = 1 \text{-} 0.5 \cdot \exp\left[ (AES + BFS \cdot \cos \, \9) \cdot \cos \, \9 \right] \tag{19}$$

$$AFS = ALG \cdot \left[1.459 + ALG \cdot \left(0.1595 + ALG \cdot 0.4129\right)\right] \tag{20}$$

$$BFS = ALG \cdot \left[ 0.0783 + ALG \cdot \left( -0.3824 - ALG \cdot 0.5874 \right) \right] \tag{21}$$

$$ALG = \ln\left(1 \cdot \le \cos \theta \ge \right) \tag{22}$$

Asymmetry factor <cos> was accepted for so called rural atmosphere model (0.65). In relation (18) the parameters with apostrophe were calculated applying *M*=1.8, and in the formula defining *F* - 1 cos 1.8 *=* . Finally, the obtained expression for *Ed*() is multiplied by *C* coefficient:

$$C = \begin{cases} \left(\lambda + 0.55\right)^{0.8} & \text{for } \lambda \le 0.45 \text{ }\mu\text{m} \\\ 1.0 & \text{for } \lambda \ge 0.45 \text{ }\mu\text{m} \end{cases} \tag{23}$$

Reassuming, the determination of the illumination or radiation dose at the sea level by means of the presented algorithms requires the knowledge only on two parameters of atmospheric conditions, the atmospheric pressure and the water vapour pressure, routinely registered at the sea level at meteorological stations. Albedo of the sea area was accepted as constant and equal to 0.06.

#### **3.3 Cloudiness**

Traditionally, clouds have been observed visually by trained technicians at weather stations and onboard ships around the world (often in units of eighths, or oktas), following the general rules outlined by the World Meteorological Organization (WMO, 1975). Before "the satellite epoch" the influence of cloudiness on the solar irradiation at the Earth's surface has been taken into account by using these visual observations of the cloud cover as input data in the simple models. The longer the averaging time was the better coherence of measured and calculated results was obtained. Apart from the subjectivity of such observations, their number as well as time and spatial resolution over the seas and oceans, make these results unreliable. Accounting for high temporal variability of cloudiness, meteorological models as a source of the input data are also a source of serious errors (Krężel & Kozłowski, 2001). Nevertheless, if the cloudiness over the area of interest is known the average energy flux at the horizontal plane can be expressed as:

$$E = E\_0 T\_{Cl} \tag{24}$$

where: *E*0 - irradiance reaching the sea surface in the case of cloudless atmosphere; *TCl* - a function describing the influence of an average cloudiness on the light transmission, given by Krężel (1985):

$$T\_{Cl} = 1 - a\_{Cl}c - b\_{Cl}c^2 \tag{25}$$

where: *c* - cloudiness in tenth, *aCl* i *bCl* – best-fit coefficients.

Assuming the independence of *TCl* on the wavelength the formulas (24) and (25) complement the model with this very important component and make it applicable for the atmosphere.

#### **4. Satellite sources of input data**

#### **4.1 Aerosol optical thickness**

In the next approximation, it is assumed to utilise the Advanced Very High Resolution Radiometer (AVHRR) data. The upward radiance over the dark sea surface within red and infrared bands depends primarily on the type and concentration of aerosols in the atmosphere. The amount of solar radiation backscattered by aerosols is proportional to the aerosol optical thickness τ*a* and the phase function *PA*(γ). Light attenuation by marine aerosols arises mainly from the scattering processes (i.e. the single scattering albedo ω' = 1). Therefore, the

Reassuming, the determination of the illumination or radiation dose at the sea level by means of the presented algorithms requires the knowledge only on two parameters of atmospheric conditions, the atmospheric pressure and the water vapour pressure, routinely registered at the sea level at meteorological stations. Albedo of the sea area was accepted as

Traditionally, clouds have been observed visually by trained technicians at weather stations and onboard ships around the world (often in units of eighths, or oktas), following the general rules outlined by the World Meteorological Organization (WMO, 1975). Before "the satellite epoch" the influence of cloudiness on the solar irradiation at the Earth's surface has been taken into account by using these visual observations of the cloud cover as input data in the simple models. The longer the averaging time was the better coherence of measured and calculated results was obtained. Apart from the subjectivity of such observations, their number as well as time and spatial resolution over the seas and oceans, make these results unreliable. Accounting for high temporal variability of cloudiness, meteorological models as a source of the input data are also a source of serious errors (Krężel & Kozłowski, 2001). Nevertheless, if the cloudiness over the area of interest is known the average energy flux at

where: *E*0 - irradiance reaching the sea surface in the case of cloudless atmosphere; *TCl* - a function describing the influence of an average cloudiness on the light transmission, given

Assuming the independence of *TCl* on the wavelength the formulas (24) and (25) complement the model with this very important component and make it applicable for the

In the next approximation, it is assumed to utilise the Advanced Very High Resolution Radiometer (AVHRR) data. The upward radiance over the dark sea surface within red and infrared bands depends primarily on the type and concentration of aerosols in the atmosphere. The amount of solar radiation backscattered by aerosols is proportional to the aerosol optical thickness τ*a* and the phase function *PA*(γ). Light attenuation by marine aerosols arises mainly from the scattering processes (i.e. the single scattering albedo ω' = 1). Therefore, the

*+ C =*

constant and equal to 0.06.

the horizontal plane can be expressed as:

**4. Satellite sources of input data**

**4.1 Aerosol optical thickness**

where: *c* - cloudiness in tenth, *aCl* i *bCl* – best-fit coefficients.

**3.3 Cloudiness**

by Krężel (1985):

atmosphere.

0.8 ( 0.55) for 0.45 m 1.0 for >0.45 m

*E ET* 0 *Cl* (24)

2 1 *T ac bc Cl Cl Cl* (25)

(23)

  dependence between the radiance measured by the space-borne radiometer and τ*a* should also allow for the light source-observation direction geometric configuration. This means that *PA*(γ) has to be known in order to determine the value of τ*a* (Stowe et al., 1997).

Results of hitherto investigations indicated that the variability of *PA*(γ) in the case of backscattering is considerably lower than the τ*a* variability, and even, to a certain extent, does not depend on the type of aerosols (Stowe et al., 1997). Koepke & Quenzel (1979) showed that the variability of *PA*(γ) is contained within the range of about ±25% (and can be lowered down to ±4% in the case of optimal geometric configuration), whereas τ*a* can range even over an order of magnitude. The feasibility of τ*a* determination based on a single spectral channel data on reflectance was proved by Kaufman (1993) who stated the universality in the dependence between τ*a* and the path radiance.

Standard algorithms used to determine the aerosol optical thickness on the basis of data from a single spectral channel (1 or 2) of the AVHRR, which were developed for NOAA/NESDIS4 (first-generation algorithm), allow for the geometric configuration of the system. Moreover, the correction referring to the actual distance between the Earth and the Sun is considered, and the values are rescaled according to the wavelength of 0.5 μm (rescale coefficient is 1.348). Additional parameters used as the input data are i) the ocean albedo (Lambert's reflection coefficient), ii) volumetric absorption and scattering coefficients and iii) the phase function *PA*(γ) which is determined on the basis of the Mie theory and redefined for the model of aerosol particles.

The values of *AOT*(500 nm) calculated by means of the above-mentioned algorithm are rendered accessible in a quasi-operational mode by NOAA/NESDIS. Utilisation of AVHRR data in *AOT* determination enables gaining information on the values of this parameter within a spectral interval of 100 nm with the central value at 630 nm. The algorithm enabling one to determine the *AOT* variability within the whole visible spectrum was developed basing on the analysis of measurement data recorded mainly on Gotland, within the framework of AERONET5 program. Functional dependencies enabling determination of *AOT* for optional wavelength within PAR band were obtained (Krężel et al., 2008):

$$AOT(\lambda) = AOT(500)a \exp\, b(\lambda) \tag{26}$$

where: *AOT*(500) – the aerosol optical thickness at 500 nm determined on the basis of AVHRR data by means of the first-generation algorithm, *a* – empirical coefficient equal to 1.63, – the wavelength [nm], *b*() – coefficient related to the wavelength , which can be described by the following formula:

$$h(\lambda) = 4.588 \exp(-2.981\lambda / 1000) \tag{27}$$

Dependencies (26) - (27) make it possible to obtain digital images of τ*a*(), which are presented in Fig. 3.

4 National Oceanic and Atmospheric Administration /National Environmental Satellite, Data, and Information Service

5 Aerosol Robotic Network - http://aeronet.gsfc.nasa.gov/

Fig. 3. Exemplary maps of aerosol optical thickness over the Baltic Sea calculated on the basis of interpolated AVHRR channel 1 data

#### **4.2 Water vapour**

The water vapour is present in the troposphere – the lower part of the atmosphere. Its content varies significantly with space and time, being dependent on the air temperature. At the sea level, concentration of water vapour can change from 10-2 gm-3 at high latitudes up to 30 gm-3 in the equatorial regions. It is one of the most important atmosphere constituents that attenuates radiation. The strongest absorption occurs far out in the infrared part of the solar spectrum, making atmosphere almost opaque for the wavelengths in the range of about 30-900 μm. Weaker water vapour absorption bands affect transmittance windows that are useful for remote sensing in the near infrared (NIR), thermal-infrared (IR) and microwave (MW). It provides a way to retrieve water vapour content on the basis of satellite measurements of reflected solar NIR radiation or emitted by the Earth's surface IR and MW radiation.

The humidity of the atmosphere can be expressed in different ways, e.g. absolute humidity, specific humidity, dew point temperature or integrated precipitable water vapour (IPWV). In the satellite techniques the latter one, also called the integrated column water vapour or total atmospheric water vapour (denoted *W*), is used. It expresses the height of the layer of liquid water, obtained by condensing all the water vapour in the zenith direction at the surface of a unit area. A layer of 1 cm thickness corresponds to 1 g of precipitable water per 1 cm2 of the area.

In the NIR part of the spectrum, the absorption peaks of the water vapour are located mainly around 720, 820 and 940 nm. The differential absorption technique utilizing this region of spectrum assumes, in general, that the integrated water vapour content is related to the transmission in a spectral channel affected by the water vapour absorption and that the transmission can be estimated using the radiance ratio of two bands - absorption and

Fig. 3. Exemplary maps of aerosol optical thickness over the Baltic Sea calculated on the

The water vapour is present in the troposphere – the lower part of the atmosphere. Its content varies significantly with space and time, being dependent on the air temperature. At the sea level, concentration of water vapour can change from 10-2 gm-3 at high latitudes up to 30 gm-3 in the equatorial regions. It is one of the most important atmosphere constituents that attenuates radiation. The strongest absorption occurs far out in the infrared part of the solar spectrum, making atmosphere almost opaque for the wavelengths in the range of about 30-900 μm. Weaker water vapour absorption bands affect transmittance windows that are useful for remote sensing in the near infrared (NIR), thermal-infrared (IR) and microwave (MW). It provides a way to retrieve water vapour content on the basis of satellite measurements of reflected solar NIR radiation or emitted by the Earth's surface IR

The humidity of the atmosphere can be expressed in different ways, e.g. absolute humidity, specific humidity, dew point temperature or integrated precipitable water vapour (IPWV). In the satellite techniques the latter one, also called the integrated column water vapour or total atmospheric water vapour (denoted *W*), is used. It expresses the height of the layer of liquid water, obtained by condensing all the water vapour in the zenith direction at the surface of a unit area. A layer of 1 cm thickness corresponds to 1 g of precipitable water per

In the NIR part of the spectrum, the absorption peaks of the water vapour are located mainly around 720, 820 and 940 nm. The differential absorption technique utilizing this region of spectrum assumes, in general, that the integrated water vapour content is related to the transmission in a spectral channel affected by the water vapour absorption and that the transmission can be estimated using the radiance ratio of two bands - absorption and

basis of interpolated AVHRR channel 1 data

**4.2 Water vapour**

and MW radiation.

1 cm2 of the area.

non-absorption ones. It comes from the simplification of the Lambert's law (Gao & Goetz, 1990; Bouffiès et al., 1997; Tahl & von Schoenermark, 1998). As the assumptions undergoing the theory cannot be true in reality, nonlinear relationships are used in practice. The disadvantage of this method is limitation to daytime measurements and its high sensitivity to aerosols and thin clouds – their occurrence can lead both to overestimation (photon path enlarged due to multiple scattering) or underestimation (photon path reduced due to backscattering) of the atmospheric humidity content.

In the recent years, studies in retrieving total atmospheric water vapour content based on the absorption technique has been carried out using sensors such as the Medium Resolution Imaging Spectrometer (MERIS) working onboard the European Envisat satellite (Fischer & Bennartz, 1997) and Moderate Resolution Imaging Spectrometer (MODIS) onboard two U.S. American platforms, Terra and Aqua (Gao & Kaufman, 2003; Albert et al., 2005; Moradizadeh et al., 2008). The general form of the water vapour retrieval algorithm for MERIS level 2 products is (ESA, 2006):

$$\mathcal{W} = k\_0 + k\_1 \log \left( \frac{L\_{\rm Ch15}}{L\_{\rm Ch14}} \right) + k\_2 \log^2 \left( \frac{L\_{\rm Ch15}}{L\_{\rm Ch14}} \right)^2 \tag{28}$$

where *LCh*14 and *LCh*15 are the radiances measured in MERIS channels 14 and 15 respectively, and *k*0, *k*1 and *k*2 are the regression coefficients, dependent on few factors, with the geometry of observation among them. The absorption band 15 is centred at 900 nm and the reference non-absorption band 14 at 885 nm. Above land surfaces, top of the atmosphere (TOA) radiances are corrected for the spectral slope of the surface albedo prior to applying the algorithm. Above water surfaces, the algorithm additionally takes into account the aerosol optical thickness, except for regions where the Sun glint is significant. Correction is made using *AOT* at 900 nm estimation based on the radiances measured at MERIS bands 12 (775 nm) and 13 (865 nm). The advantage of the second instrument, MODIS, is that it has two more channels in the infrared spectral region, where the water vapour absorption occurs. Thus, the water vapour content is calculated as the weighted mean of three independent estimations (NASA MOD05 algorithm) performed according to (Gao & Kaufman, 1998, 2003):

$$\mathcal{W}\_i = a\_0 + a\_1 \left(\frac{L\_{\rm Chi}}{L\_{\rm Ch2}}\right) + a\_2 \left(\frac{L\_{\rm Chi}}{L\_{\rm Ch2}}\right)^2 \tag{29}$$

where *LChi* are the radiances measured in MODIS water vapour absorption channels 17, 18 or 19, centred around 905, 936 and 940 nm respectively. Non-absorption reference band 2 has its centre at 865 nm. It have to be mentioned that these three bands have different water vapour sensitivity (Sobrino & El Kharraz, 2003), so weighting factors for averaging are calculated based on the range of possible water vapour content and the transmissivities in band *i*.

Global data of both sensors are available with the nadir resolution of 1 km. MERIS full resolution of 300 m can be used locally for more detailed water vapour estimation (Fig. 4). Near polar orbits and wide swath of these systems enable to observe each point at the Earth's surface at least once every two days. Higher temporal resolution can be provided using sensors measuring emitted infrared radiation, such as AVHRR or the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The AVHRR, which is carried by the American TIROS-N/NOAA series of satellites since 1979, provides long time series of data with about 1 km spatial resolution. NOAA satellites, working in pairs, can give information on the same point of the surface at least 4 times per day. SEVIRI, operating on board of meteorological geostationary satellites provides data with coarser spatial resolution depending on distance from the sub satellite point (about 3.3 km at the central Baltic Sea) but with very high temporal resolution – up to 15 min from the recently working Meteosat Second Generation satellite (MSG).

Fig. 4. Exemplary maps of total atmospheric water vapour over the Baltic Sea region calculated on the basis of a weather prediction model (left) and a standard MERIS Level 2 product under clear sky conditions (right) for the same day

Both instruments have two spectral bands in the range of 8.5-12.5 μm atmospheric window, which is affected by the water vapour absorption. These are AVHRR's band 4 (10.3-11.3 μm) and 5 (11.5-12.5 μm) or SEVIRI's band 9 (9.8-11.8 μm) and 10 (11.0-13.0 μm). Using measurements of radiation at two wavelengths in retrieving the surface brightness temperature makes it possible to limit the influence of the atmosphere brightness temperature on the calculated values of the water vapour content. This is a so-called splitwindow technique, that can be used directly to obtain the surface brightness temperature and, if an independent estimate of the mean brightness temperature of the atmosphere is available, also the column-integrated water vapour.

In practice the split-window technique is rarely used directly to obtain water vapour from sensors measuring emitted infrared radiation. Several simplified methods utilising regression have been proposed instead of it. To retrieve humidity content from the AVHRR and SEVIRI thermal channels a method based on the linear atmosphere–surface temperature relationship (LASTR) can be used (Sobrino et al., 2002; Sobrino & Romaguera, 2008). It considers a simple linear relationship between *W* and the atmospheric transmittance (τ) in channel 4 of AVHRR or 9 of SEVIRI. Best fit regression coefficients for each instrument was derived based on simulations of atmospheric models. In the case of SEVIRI they are a function of the viewing geometry by means of the cosine of satellite zenith angle. The transmittance through the atmosphere from the surface to the satellite in channel *i*, for a cloud-free situation, and considering the emissivity as being equal to one for a blackbody, can be obtained from the radiative transfer equation as:

$$\pi\_i = \frac{T\_i - T\_{ai}}{SST - T\_{ai}} \tag{30}$$

where *Ti* is the brightness temperature retrieved from the radiance measured in channel *i*, *Tai* is the effective atmospheric temperature (resulting from the whole column of the atmosphere) and *SST* is the sea surface temperature. *SST* can be derived from the same data using the split-window algorithm. Its nonlinear form NLSST is recommended (Walton et al., 1998). The effective atmospheric temperature is strongly correlated with *SST* values thus can be obtained with sufficient accuracy using linear regression.

The new SEVIRI instrument has two more water vapour channels at 6.2 and 7.3 μm, that improves possibilities of water vapour retrieval. However, IR radiometry has the same limitation as mentioned before – it allows to estimate the humidity content only under clear sky conditions. In the presence of clouds, only the microwave techniques can be used. Similar methods, accounting for measurements at two frequencies (22.235 and 31 GHz) has been developed. They can be used to retrieve precipitable water over the ocean (but not over the land) based on observations provided for example by the Special Sensor Microwave/Imager (SSM/I) and its successor (SSMIS) onboard a series of platforms in the US Defence Meteorological Satellite Programme (DMSP) or the Advanced Microwave Scanning Radiometer (AMSR-E) onboard NASA's Aqua satellite. The advantage of 'seeing through clouds', however, is reduced by coarse ground resolution, that cannot be finer than 25 km.

#### **4.3 Ozone**

182 Solar Power

using sensors measuring emitted infrared radiation, such as AVHRR or the Spinning Enhanced Visible and Infrared Imager (SEVIRI). The AVHRR, which is carried by the American TIROS-N/NOAA series of satellites since 1979, provides long time series of data with about 1 km spatial resolution. NOAA satellites, working in pairs, can give information on the same point of the surface at least 4 times per day. SEVIRI, operating on board of meteorological geostationary satellites provides data with coarser spatial resolution depending on distance from the sub satellite point (about 3.3 km at the central Baltic Sea) but with very high temporal resolution – up to 15 min from the recently working Meteosat

Fig. 4. Exemplary maps of total atmospheric water vapour over the Baltic Sea region calculated on the basis of a weather prediction model (left) and a standard MERIS Level 2

Both instruments have two spectral bands in the range of 8.5-12.5 μm atmospheric window, which is affected by the water vapour absorption. These are AVHRR's band 4 (10.3-11.3 μm) and 5 (11.5-12.5 μm) or SEVIRI's band 9 (9.8-11.8 μm) and 10 (11.0-13.0 μm). Using measurements of radiation at two wavelengths in retrieving the surface brightness temperature makes it possible to limit the influence of the atmosphere brightness temperature on the calculated values of the water vapour content. This is a so-called splitwindow technique, that can be used directly to obtain the surface brightness temperature and, if an independent estimate of the mean brightness temperature of the atmosphere is

In practice the split-window technique is rarely used directly to obtain water vapour from sensors measuring emitted infrared radiation. Several simplified methods utilising regression have been proposed instead of it. To retrieve humidity content from the AVHRR and SEVIRI thermal channels a method based on the linear atmosphere–surface temperature relationship (LASTR) can be used (Sobrino et al., 2002; Sobrino & Romaguera, 2008). It

product under clear sky conditions (right) for the same day

available, also the column-integrated water vapour.

Second Generation satellite (MSG).

The amount of the total stratospheric ozone has been regularly measured since the mid 1950s. It is usually expressed in Dobson units (DU). 100 DU correspond to the amount of ozone that would produce a layer of 1 mm of thickness at pressure of 1 atm. The highest concentrations of ozone in the stratosphere occur at about 25 km at the equator and 20 km in the northern polar regions. The average values are about 300 DU but they vary significantly in space and over the year.

Ozone has absorption lines in all major portions of the electromagnetic spectrum so it can be measured with a variety of techniques. The most important for the satellite measurements of integrated ozone are the ultraviolet Huggins bands (310-350 nm) and Chappuis bands in the visible part of the spectrum (380-750 nm), where absorption occurs due to dissociation of ozone. There is also an infrared ozone absorption band at 9.6 μm connected with changes in the vibrational state of the molecules.

Satellite remote sensing of the total ozone in both the stratosphere and troposphere is based mainly on measurements of the ultraviolet albedo of the Earth. Instruments which currently measure these albedos are the Solar Backscatter Ultraviolet Spectrometer (SBUV/2) and the Global Ozone Monitoring Experiment (GOME-2) onboard NOAA and METOP 2 satellites. Previous GOME instrument operated from the ESA platforms as well. Both instruments are nadir-looking sensors, thus ozone maps are generated using interpolation and smoothing procedures.

Algorithms to retrieve the total ozone utilize the split-window technique using measurements of radiation in two closely spaced channels in the Hartley-Huggins bands. Channels 2 and 3 of GOME fulfil these requirements. They are characterised by limited interference from other trace gas absorbers and a smaller interference of Ring effect (partially filled Fraunhofer lines in the backscattered spectrum). SBUV/2 measures backscattered radiance at 12 discrete wavelengths in the range of spectrum between 252 and 340 nm, giving possibility to retrieve vertical profiles of the ozone concentration (Bhartia et al., 1996).

The infrared ozone absorption band is utilized by TIROS-N Operational Vertical Sounder (TOVS) processing the data recorded by High Resolution InfraRed Sounder (HIRS/2). The ozone retrieval regression algorithm uses channels 9 (9.71 μm), 8 (11.11 μm), and 3 (14.49 μm) (Engelen & Stephens, 1997). Channels 3 and 8 are incorporated in order to correct the ozone-sensitive channel 9 for the temperature dependence. Climatological data are used to incorporate the unobserved upper layer of the atmosphere (Neuendorffer, 1996). The 9.7 μm IR channel is also present in the SEVIRI. This channel measures ozone concentration at high temporal and spatial resolution, in near-real-time mode. The algorithm uses an optimal estimation approach to fit the calculated 9.7 channel radiances to the observed values by adjusting the ozone quantity in the 1000-30 hPa layer with constraining boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the 10.8 μm channel observations (EUMETSAT, 2010).

Reanalised SBUV/2 and TOVS ozone products are provided in form of global ozone grids with spatial resolution of 1° and temporal resolution of 1 day by TOAST System6. GOME-2 products can be obtained from NESDIS with the same temporal resolution but at 1°×1.25° spatial grids. Our solar irradiance model does not utilize the satellite ozone data yet.

#### **4.4 Cloudiness**

It is out of discussion that pretty-nearly any space born information on cloudiness, especially over the seas and oceans, is better than that calculated even by advanced meteorological models (Fig. 5). Nevertheless, because of the complicated structure of clouds in the terrestrial atmosphere, variability in their consistency, diversification of water drops size and ice particles' shape etc. there is no easy way to presume the optical thickness of clouds basing on the analysis of the solar radiance reflected from their surface and recorded by a satellite-borne radiometer. For instance the problem of the cloudiness influence on the solar energy flux reaching the sea surface can be divided into two stages:

 it has to be determined whether the Sun was obstructed by clouds at a given point and time or not – wrong answer to this question leads to over- or underestimation of considered parameter value almost by an order of magnitude,

 6 Total Ozone Analysis using SBUV/2 and TOVS

measure these albedos are the Solar Backscatter Ultraviolet Spectrometer (SBUV/2) and the Global Ozone Monitoring Experiment (GOME-2) onboard NOAA and METOP 2 satellites. Previous GOME instrument operated from the ESA platforms as well. Both instruments are nadir-looking sensors, thus ozone maps are generated using interpolation and smoothing

Algorithms to retrieve the total ozone utilize the split-window technique using measurements of radiation in two closely spaced channels in the Hartley-Huggins bands. Channels 2 and 3 of GOME fulfil these requirements. They are characterised by limited interference from other trace gas absorbers and a smaller interference of Ring effect (partially filled Fraunhofer lines in the backscattered spectrum). SBUV/2 measures backscattered radiance at 12 discrete wavelengths in the range of spectrum between 252 and 340 nm, giving possibility to retrieve

The infrared ozone absorption band is utilized by TIROS-N Operational Vertical Sounder (TOVS) processing the data recorded by High Resolution InfraRed Sounder (HIRS/2). The ozone retrieval regression algorithm uses channels 9 (9.71 μm), 8 (11.11 μm), and 3 (14.49 μm) (Engelen & Stephens, 1997). Channels 3 and 8 are incorporated in order to correct the ozone-sensitive channel 9 for the temperature dependence. Climatological data are used to incorporate the unobserved upper layer of the atmosphere (Neuendorffer, 1996). The 9.7 μm IR channel is also present in the SEVIRI. This channel measures ozone concentration at high temporal and spatial resolution, in near-real-time mode. The algorithm uses an optimal estimation approach to fit the calculated 9.7 channel radiances to the observed values by adjusting the ozone quantity in the 1000-30 hPa layer with constraining boundary conditions from the European Centre for Medium-Range Weather Forecasts (ECMWF) and

Reanalised SBUV/2 and TOVS ozone products are provided in form of global ozone grids with spatial resolution of 1° and temporal resolution of 1 day by TOAST System6. GOME-2 products can be obtained from NESDIS with the same temporal resolution but at 1°×1.25°